diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..03a7096589a638e3ea43c80e4d84501c3044d052 --- /dev/null +++ b/.gitignore @@ -0,0 +1,42 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# OS files +.DS_Store +Thumbs.db + +# Environments & Secret credentials +.env +.env.local +.env*.local +backend/.env +frontend/.env + +# Local database files +*.db +*.sqlite +*.sqlite3 +*.sqlite-journal +backend/db/local_brain.db + +# Node dependencies & Next.js cache +node_modules/ +.next/ +out/ +npm-debug.log* +yarn-debug.log* +yarn-error.log* +.pnpm-debug.log* + +# Unit test & Coverage reports +.pytest_cache/ +.coverage +htmlcov/ +.cache/ + +# User data sources (ignore uploaded dynamic sources, keep demo ones if needed) +# For the hackathon, we keep the static demo rivanly-inc files, but ignore other companies if uploaded +data/sources/*/ +!data/sources/rivanly-inc/ diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000000000000000000000000000000000000..5e259d9424f30c29d7db10e42c47d4968097e3f1 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,444 @@ +# Company Brain — CLAUDE.md +## Project context for AI coding assistants + +--- + +## What This Project Is + +Company Brain is a multi-agent compilation pipeline that extracts operational decision knowledge from company data sources (Slack, Notion SOPs, support tickets) and compiles it into a versioned, evidence-linked, executable skills file. A downstream brain agent uses this skills file to handle operational scenarios correctly — acting like the company's best employee. + +**The core thesis:** Agents are compilers, not assistants. We don't search raw documents. We compile tribal knowledge into structured, executable logic once. Then we read the compiled output forever. + +--- + +## Monorepo Structure + +``` +company-brain/ +├── backend/ ← FastAPI + LangGraph pipeline (Python) +│ ├── main.py ← FastAPI app entry point +│ ├── graph/ +│ │ ├── state.py ← BrainState TypedDict +│ │ ├── nodes/ ← one file per LangGraph node +│ │ │ ├── ingest_slack.py +│ │ │ ├── ingest_notion.py +│ │ │ ├── ingest_tickets.py +│ │ │ ├── ingest_join.py +│ │ │ ├── extract_decisions.py +│ │ │ ├── extract_workflows.py +│ │ │ ├── extract_exceptions.py +│ │ │ ├── detect_contradictions.py +│ │ │ ├── synthesize_skills.py +│ │ │ ├── link_evidence.py +│ │ │ ├── score_confidence.py +│ │ │ └── write_brain.py +│ │ └── graph.py ← graph assembly + compile +│ ├── agents/ +│ │ └── brain_agent.py ← query-time brain agent +│ ├── db/ +│ │ └── supabase.py ← Supabase client + queries +│ ├── models/ +│ │ └── schemas.py ← Pydantic models for API +│ └── requirements.txt +├── frontend/ ← Next.js 14 + Tailwind (Harshit) +├── data/ +│ └── sources/ ← 8 synthetic source files +│ ├── notion_refund_sop.md +│ ├── notion_pricing_policy.md +│ ├── notion_eng_runbook.md +│ ├── notion_hr_playbook.md +│ ├── notion_cs_playbook.md +│ ├── slack_export_support.json +│ ├── slack_export_ops.json +│ └── zendesk_tickets.json +└── CLAUDE.md ← this file +``` + +--- + +## Tech Stack + +| Layer | Technology | +|---|---| +| LLM inference | vLLM serving `RedHatAI/Qwen2.5-72B-Instruct-FP8-dynamic` on AMD MI300X, port 8000 | +| LLM client | `openai` Python SDK pointed at `http://localhost:8000/v1` | +| Agent orchestration | `langgraph` with async nodes + `Send` API for parallel fan-out | +| State checkpointing | `MemorySaver` (in-memory for v0) | +| Embedding (skill matching) | `sentence-transformers` `all-MiniLM-L6-v2` in-memory, CPU | +| Web framework | `FastAPI` with `uvicorn` | +| Real-time streaming | FastAPI `StreamingResponse` with `text/event-stream` | +| Database | Supabase (Postgres) via `supabase-py` | +| File storage | Supabase Storage | + +--- + +## LLM Client Setup + +```python +from openai import AsyncOpenAI + +llm = AsyncOpenAI( + base_url="http://localhost:8000/v1", + api_key="not-needed" +) + +# All LLM calls use this pattern: +response = await llm.chat.completions.create( + model="RedHatAI/Qwen2.5-72B-Instruct-FP8-dynamic", + messages=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_content} + ], + temperature=0.1, + max_tokens=4096 +) +result = response.choices[0].message.content +``` + +**Never use `openai.OpenAI()` — always use `AsyncOpenAI`. All nodes are async.** + +--- + +## BrainState — The Central Data Structure + +```python +from typing import TypedDict, Annotated +import operator + +class BrainState(TypedDict): + company_id: str + source_files: list[dict] # [{filename, content, sha256, type}] + + # Ingestion outputs (parallel, accumulated with operator.add) + normalized_events: Annotated[list[dict], operator.add] # from Slack + structured_sops: Annotated[list[dict], operator.add] # from Notion + resolved_cases: Annotated[list[dict], operator.add] # from tickets + + # Extraction outputs (parallel, accumulated with operator.add) + raw_decisions: Annotated[list[dict], operator.add] + workflow_steps: Annotated[list[dict], operator.add] + exception_rules: Annotated[list[dict], operator.add] + contradictions: Annotated[list[dict], operator.add] + + # Compilation outputs (sequential) + draft_skills: list[dict] + skills_with_evidence: list[dict] + final_skills: list[dict] + + # Metadata + job_id: str + brain_version: str + errors: Annotated[list[str], operator.add] +``` + +**The `Annotated[list, operator.add]` pattern is critical.** It allows multiple parallel nodes to write to the same list field without overwriting each other. Do not change this. + +--- + +## LangGraph Architecture — Fan-Out Pattern + +```python +from langgraph.graph import StateGraph +from langgraph.checkpoint.memory import MemorySaver +from langgraph.types import Send + +def route_to_ingestion(state: BrainState) -> list[Send]: + """Fan out to 3 parallel ingestion nodes based on source file types.""" + sends = [] + for file in state["source_files"]: + if file["type"] == "slack_json": + sends.append(Send("ingest_slack", {"source_files": [file], ...})) + elif file["type"] == "notion_md": + sends.append(Send("ingest_notion", {"source_files": [file], ...})) + elif file["type"] == "tickets_json": + sends.append(Send("ingest_tickets", {"source_files": [file], ...})) + return sends + +def route_to_extraction(state: BrainState) -> list[Send]: + """Fan out to 4 parallel extraction nodes after ingestion join.""" + return [ + Send("extract_decisions", state), + Send("extract_workflows", state), + Send("extract_exceptions", state), + Send("detect_contradictions", state), + ] + +# Graph assembly: +# START → route_to_ingestion (conditional) → [ingest_slack, ingest_notion, ingest_tickets] +# → ingest_join (barrier, waits for all) → route_to_extraction (conditional) +# → [extract_decisions, extract_workflows, extract_exceptions, detect_contradictions] +# → synthesize_skills → link_evidence → score_confidence → write_brain → END +``` + +**Never use `graph.add_edge("extractor", "synthesize_skills")` for parallel nodes — this causes synthesize_skills to fire multiple times. Always use the `Send` API + barrier join node.** + +--- + +## Extraction Prompt Pattern + +Every extraction node uses this prompt structure: + +```python +SYSTEM = """You are a policy analyst. Your ONLY job is to extract {type} from company communications. +Output ONLY a JSON array. No preamble. No explanation. No markdown. +Each item must have exactly these fields: {schema} +If you find nothing, output: [] +Example output: {example}""" + +USER = """Extract all {type} from this company data: +{content}""" +``` + +- Temperature: always `0.1` +- Max tokens: `4096` +- Always wrap LLM call in try/except — on JSON parse failure, retry once with stricter prompt, then return `[]` + +--- + +## Skills File Schema (per skill) + +```python +{ + "id": "handle_refund_request", # snake_case + "name": "Handle Refund Request", # human readable + "domain": "support", # support|revenue|product_eng|customer_success|hr|finance_ops + "version": "1.0", + "confidence": 0.91, # 0.0 - 1.0 + "stale": False, + "review_required": False, # True if confidence < 0.6 + "last_updated": "2026-05-04T09:30:00Z", + "trigger": { + "phrases": ["refund", "money back"], + "conditions": ["customer mentions payment dissatisfaction"] + }, + "decision_logic": [ + { + "condition": "plan == 'annual' AND days_since_purchase <= 14", + "action": "approve_full_refund", + "note": "No-questions policy within 14 days.", + "evidence_sources": [ + { + "source": "notion_refund_sop.md", + "excerpt": "Annual plan customers within 14 days...", + "confidence": 0.95 + } + ] + } + ], + "forbidden_actions": [ + "Never process refunds for lifetime deal accounts" + ], + "escalation_chain": ["support_agent", "support_lead", "account_manager", "founder"], + "sla": "respond_within_2h, resolve_within_24h" +} +``` + +--- + +## Confidence Scoring Formula + +```python +def score_confidence(skill: dict, all_sources: list[dict]) -> float: + base = 0.5 + + # More sources = higher confidence + source_count = len(skill["decision_logic"][0].get("evidence_sources", [])) + if source_count >= 3: + base += 0.25 + elif source_count == 2: + base += 0.15 + elif source_count == 1: + base += 0.05 + + # Recent sources = higher confidence + # (check source file last_modified if available) + base += 0.15 # assume recent for v0 + + # No contradictions for this skill = higher confidence + # (passed in from contradiction detector) + has_contradiction = False # check contradictions list + if not has_contradiction: + base += 0.10 + + return min(base, 1.0) +``` + +--- + +## Brain Agent Pattern + +```python +from sentence_transformers import SentenceTransformer +import numpy as np + +# Load once at startup +embedder = SentenceTransformer('all-MiniLM-L6-v2') + +# Pre-compute skill embeddings (call after compile) +skill_embeddings = {} # {skill_id: np.array} + +def compute_skill_embeddings(skills: list[dict]): + global skill_embeddings + for skill in skills: + text = f"{skill['name']} {' '.join(skill['trigger']['phrases'])}" + skill_embeddings[skill['id']] = embedder.encode(text) + +def match_skill(query: str) -> tuple[str, float]: + query_emb = embedder.encode(query) + scores = {} + for skill_id, emb in skill_embeddings.items(): + score = float(np.dot(query_emb, emb) / + (np.linalg.norm(query_emb) * np.linalg.norm(emb))) + scores[skill_id] = score + best_id = max(scores, key=scores.get) + return best_id, scores[best_id] + +def skill_to_markdown(skill: dict) -> str: + """Convert skill JSON to markdown for prompt injection.""" + lines = [f"## {skill['name']}", ""] + for logic in skill['decision_logic']: + lines.append(f"- IF {logic['condition']}: {logic['action']}") + if logic.get('note'): + lines.append(f" Note: {logic['note']}") + lines.append("") + lines.append("FORBIDDEN: " + "; ".join(skill['forbidden_actions'])) + lines.append("ESCALATE: " + " → ".join(skill['escalation_chain'])) + return "\n".join(lines) +``` + +--- + +## FastAPI SSE Pattern + +```python +from fastapi import FastAPI +from fastapi.responses import StreamingResponse +import asyncio +import json + +async def event_generator(job_id: str): + """Yields SSE events during compilation.""" + async for event in compilation_events[job_id]: + yield f"event: {event['type']}\ndata: {json.dumps(event['data'])}\n\n" + +@app.get("/compile/stream") +async def stream_compile(job_id: str): + return StreamingResponse( + event_generator(job_id), + media_type="text/event-stream", + headers={ + "Cache-Control": "no-cache", + "Connection": "keep-alive", + "Access-Control-Allow-Origin": "*" # CORS for frontend + } + ) +``` + +--- + +## Supabase Tables + +```sql +-- Run these in Supabase SQL editor before starting + +CREATE TABLE companies ( + id TEXT PRIMARY KEY, + name TEXT NOT NULL, + created_at TIMESTAMPTZ DEFAULT now() +); + +INSERT INTO companies VALUES ('rivanly-inc', 'Rivanly Inc.', now()); + +CREATE TABLE skills_files ( + id UUID PRIMARY KEY DEFAULT gen_random_uuid(), + company_id TEXT REFERENCES companies(id), + version TEXT NOT NULL, + brain_json JSONB NOT NULL, + source_hashes JSONB NOT NULL, + compiled_at TIMESTAMPTZ DEFAULT now(), + is_current BOOLEAN DEFAULT false +); + +CREATE UNIQUE INDEX idx_one_current_per_company + ON skills_files(company_id) WHERE is_current = true; + +CREATE TABLE compile_runs ( + id UUID PRIMARY KEY DEFAULT gen_random_uuid(), + company_id TEXT REFERENCES companies(id), + status TEXT CHECK (status IN ('started','running','complete','error')), + started_at TIMESTAMPTZ DEFAULT now(), + completed_at TIMESTAMPTZ, + duration_ms INTEGER, + result_version TEXT, + error_detail TEXT +); + +CREATE TABLE source_files ( + id UUID PRIMARY KEY DEFAULT gen_random_uuid(), + company_id TEXT REFERENCES companies(id), + filename TEXT NOT NULL, + sha256 TEXT NOT NULL, + content TEXT NOT NULL, + source_type TEXT CHECK (source_type IN ('slack_json','notion_md','tickets_json')), + uploaded_at TIMESTAMPTZ DEFAULT now() +); +``` + +--- + +## Environment Variables + +```bash +# backend/.env +VLLM_BASE_URL=http://localhost:8000/v1 +SUPABASE_URL=your_supabase_project_url +SUPABASE_KEY=your_supabase_anon_key +COMPANY_ID=rivanly-inc +``` + +--- + +## API Endpoints — Full List + +``` +POST /compile → trigger pipeline, returns {job_id, stream_url} +GET /compile/stream → SSE stream for job_id +GET /brain/status → current brain version + stats +GET /skills → all skills (lightweight) +GET /skills/{id} → full skill detail +POST /agent/handle → brain agent query +GET /diff/{v1}/{v2} → version diff +POST /sources/upload → upload source files +``` + +--- + +## Critical Rules — Do Not Violate + +1. **All LangGraph nodes must be `async def`** — sync nodes break parallelism +2. **Use `Send` API for fan-out, never direct edges between parallel nodes and their join** +3. **Never read raw source files at query time** — brain agent reads skills file only +4. **All LLM calls wrapped in try/except** — retry once on JSON parse failure, return `[]` if still failing +5. **`skills_files.is_current` enforced by partial unique index** — only one current per company +6. **`compile_runs` table is append-only** — never update rows, only insert +7. **CORS headers on all endpoints** — frontend is on different domain +8. **Temperature 0.1 on all extraction calls** — deterministic is better than creative here + +--- + +## Demo Company — Rivanly Inc. + +The demo uses Rivanly Inc. — a fictional 15-person B2B SaaS company. + +6 departments, 12 skills: + +| Department | Skills | +|---|---| +| Support | handle_refund_request, respond_to_outage | +| Revenue | handle_pricing_exception, evaluate_discount_request | +| Product/Eng | prioritize_bug_report, handle_sla_breach | +| Customer Success | evaluate_churn_risk, enterprise_onboarding_steps | +| HR | hiring_process_engineering, performance_pip_trigger | +| Finance | approve_vendor_payment, expense_policy_exception | + +The 8 synthetic source files in `data/sources/` are authored to produce these 12 skills when processed by the pipeline. diff --git a/backend/.env.example b/backend/.env.example new file mode 100644 index 0000000000000000000000000000000000000000..2397fd027b39f32d0d761e37c28ce3e9a509a51e --- /dev/null +++ b/backend/.env.example @@ -0,0 +1,4 @@ +VLLM_BASE_URL=http://:8000/v1 +SUPABASE_URL=your_supabase_project_url +SUPABASE_KEY=your_supabase_anon_key +COMPANY_ID=rivanly-inc diff --git a/backend/agent/brain_agent.py b/backend/agent/brain_agent.py new file mode 100644 index 0000000000000000000000000000000000000000..b9429265c5f816f61cb370a91d6e854e2e22e5e8 --- /dev/null +++ b/backend/agent/brain_agent.py @@ -0,0 +1,144 @@ +import json +from backend.db.supabase import get_client +from backend.llm import llm_call, get_embedding, cosine_similarity + + +async def handle_agent_query(company_id: str, scenario: str, context: dict = None, with_brain: bool = True) -> dict: + """ + Real agent query handler. No keyword routing, no hardcoded actions. + Everything flows through: retrieve skills -> build prompt -> call vLLM -> return raw result. + """ + if not with_brain: + return await _baseline_query(scenario, context) + + # --- WITH BRAIN --- + db = get_client() + if not db: + return _error_response("Database connection failed.") + + # 1. Fetch latest compiled skills + res = db.table("skills_files").select("brain_json").eq( + "company_id", company_id + ).order("compiled_at", desc=True).limit(1).execute() + + if not res.data: + return _error_response("No compiled brain found. Please compile first.") + + skills = res.data[0]["brain_json"].get("skills", []) + if not skills: + return _error_response("Brain is empty — no skills compiled.") + + # 2. Embed the query and score every skill + query_text = f"{scenario} {json.dumps(context or {})}" + query_emb = get_embedding(query_text) + + scored = [] + for i, skill in enumerate(skills): + skill_text = f"{skill.get('category', '')} {skill.get('rule', '')} {skill.get('rationale', '')}" + skill_emb = get_embedding(skill_text) + score = cosine_similarity(query_emb, skill_emb) + scored.append({"skill": skill, "score": round(score, 4), "index": i}) + + scored.sort(key=lambda x: x["score"], reverse=True) + top_results = scored[:5] + retrieval_scores = [s["score"] for s in top_results] + + # 3. Build skills context for the LLM + skills_context = "" + for rank, s in enumerate(top_results): + sk = s["skill"] + skills_context += f"\n--- Skill #{rank+1} (retrieval_score: {s['score']}) ---\n" + skills_context += f"Category: {sk.get('category', 'Unknown')}\n" + skills_context += f"Rule: {sk.get('rule', '')}\n" + skills_context += f"Rationale: {sk.get('rationale', '')}\n" + skills_context += f"Evidence: {json.dumps(sk.get('evidence', []))}\n" + skills_context += f"Compiled Confidence: {sk.get('confidence', 'unknown')}\n" + + # 4. Prompt the LLM - no example confidence values to bias it + prompt = """You are the Kernl Brain Agent. You have access to this company's compiled operational skills (retrieved below, ranked by relevance). + +Your task: +1. Read the scenario and optional JSON context carefully. +2. Examine the retrieved skills and their retrieval_scores. +3. Determine whether any skill clearly applies to this scenario. +4. If a skill applies, state the specific recommended action from that skill's rule. +5. If NO skill applies, or if the input is nonsensical/gibberish, say so honestly. + +CONFIDENCE SCORING - base it on real signals: +- retrieval_score < 0.3 -> scenario is likely unrelated to any skill -> confidence < 0.2 +- retrieval_score 0.3-0.5 -> weak match -> confidence 0.2-0.5 +- retrieval_score 0.5-0.7 -> moderate match -> confidence 0.5-0.75 +- retrieval_score > 0.7 AND rule clearly addresses the scenario -> confidence 0.75-0.95 +- Never exceed 0.95 unless the match is exact and unambiguous. +- Gibberish or nonsensical input -> confidence 0.0, recommended_action = "unable to determine" + +Respond with ONLY a JSON object (no markdown fences, no text outside the JSON): +{ + "recommended_action": "the specific action to take", + "rule_applied": "exact rule text from the best matching skill", + "evidence": ["evidence items from the skill"], + "skill_matched": "the category of the matched skill", + "confidence": 0.0, + "reasoning": "explain why this skill applies and how you chose the confidence level" +}""" + + user_content = f"--- Scenario ---\n{scenario}\n\n--- Additional Context ---\n{json.dumps(context or {})}\n\n--- Retrieved Skills (ranked by relevance) ---\n{skills_context}" + + response_str = await llm_call(prompt, user_content) + result = _parse_json(response_str) + result["retrieval_scores"] = retrieval_scores + return result + + +async def _baseline_query(scenario: str, context: dict = None) -> dict: + """Without-brain baseline: LLM answers with zero company context.""" + prompt = """You are a generic AI assistant. You have NO company-specific knowledge or policies. +Answer based only on general industry standards. Be honest about your lack of specific context. +Respond with ONLY a JSON object: +{ + "recommended_action": "your general recommendation", + "rule_applied": "general industry standard you referenced", + "evidence": [], + "skill_matched": "none", + "confidence": 0.3, + "retrieval_scores": [], + "reasoning": "explain your reasoning, noting you lack company-specific context" +}""" + user_content = f"Scenario: {scenario}\nContext: {json.dumps(context or {})}" + response_str = await llm_call(prompt, user_content) + return _parse_json(response_str) + + +def _parse_json(raw: str) -> dict: + """Parse LLM response as JSON, stripping markdown fences.""" + try: + clean = raw.strip() + if clean.startswith("```json"): + clean = clean[7:] + if clean.startswith("```"): + clean = clean[3:] + if clean.endswith("```"): + clean = clean[:-3] + return json.loads(clean.strip()) + except Exception as e: + return { + "recommended_action": "Failed to parse LLM response", + "rule_applied": "none", + "evidence": [], + "skill_matched": "none", + "confidence": 0.0, + "retrieval_scores": [], + "reasoning": f"JSON parse error: {e}. Raw: {raw[:500]}" + } + + +def _error_response(msg: str) -> dict: + return { + "recommended_action": msg, + "rule_applied": "none", + "evidence": [], + "skill_matched": "none", + "confidence": 0.0, + "retrieval_scores": [], + "reasoning": msg + } diff --git a/backend/db/schema.sql b/backend/db/schema.sql new file mode 100644 index 0000000000000000000000000000000000000000..a0c6764dc187c4bbb9177f88119ec39d8fbe351e --- /dev/null +++ b/backend/db/schema.sql @@ -0,0 +1,58 @@ +-- Run these in Supabase SQL editor before starting + +CREATE TABLE companies ( + id TEXT PRIMARY KEY, + name TEXT NOT NULL, + created_at TIMESTAMPTZ DEFAULT now() +); + +INSERT INTO companies VALUES ('rivanly-inc', 'Rivanly Inc.', now()); + +CREATE TABLE skills_files ( + id UUID PRIMARY KEY DEFAULT gen_random_uuid(), + company_id TEXT REFERENCES companies(id), + version TEXT NOT NULL, + brain_json JSONB NOT NULL, + source_hashes JSONB NOT NULL, + compiled_at TIMESTAMPTZ DEFAULT now(), + is_current BOOLEAN DEFAULT false +); + +CREATE UNIQUE INDEX idx_skills_files_current ON skills_files(company_id) WHERE is_current = true; + +CREATE TABLE skills ( + id TEXT NOT NULL, + company_id TEXT REFERENCES companies(id), + skills_file_id UUID REFERENCES skills_files(id), + name TEXT NOT NULL, + domain TEXT NOT NULL, + version TEXT NOT NULL, + confidence FLOAT NOT NULL, + stale BOOLEAN DEFAULT false, + review_required BOOLEAN DEFAULT false, + skill_json JSONB NOT NULL, + PRIMARY KEY (id, company_id, skills_file_id) +); + +CREATE TABLE source_files ( + id UUID PRIMARY KEY DEFAULT gen_random_uuid(), + company_id TEXT REFERENCES companies(id), + filename TEXT NOT NULL, + sha256 TEXT NOT NULL, + storage_path TEXT NOT NULL, + uploaded_at TIMESTAMPTZ DEFAULT now() +); + +CREATE TABLE compile_runs ( + id UUID PRIMARY KEY DEFAULT gen_random_uuid(), + company_id TEXT REFERENCES companies(id), + status TEXT NOT NULL CHECK (status IN ('started','running','complete','error')), + started_at TIMESTAMPTZ DEFAULT now(), + completed_at TIMESTAMPTZ, + duration_ms INTEGER, + result_version TEXT, + error_detail TEXT +); + +CREATE INDEX idx_skills_files_company ON skills_files(company_id, compiled_at DESC); +CREATE INDEX idx_skills_company ON skills(company_id); diff --git a/backend/db/supabase.py b/backend/db/supabase.py new file mode 100644 index 0000000000000000000000000000000000000000..9f8024261a1c606fe11fe2d02ec7c14be2397ee4 --- /dev/null +++ b/backend/db/supabase.py @@ -0,0 +1,63 @@ +import os +from supabase import create_client, Client +from dotenv import load_dotenv + +load_dotenv() + +SUPABASE_URL = os.getenv("SUPABASE_URL") +SUPABASE_KEY = os.getenv("SUPABASE_KEY") + +if SUPABASE_URL and SUPABASE_KEY: + supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY) +else: + # We allow the app to start without Supabase for local testing if needed, + # but actual DB calls will fail if not provided. + supabase = None + +def get_client(): + return supabase + +def get_current_brain(company_id: str): + if not supabase: return None + res = supabase.table("skills_files").select("*").eq("company_id", company_id).eq("is_current", True).execute() + if res.data: + return res.data[0] + return None + +def save_skills_file(data: dict): + if not supabase: return None + res = supabase.table("skills_files").insert(data).execute() + return res.data + +def save_compile_run(data: dict): + if not supabase: return None + res = supabase.table("compile_runs").insert(data).execute() + return res.data + +def update_compile_run(run_id: str, data: dict): + if not supabase: return None + res = supabase.table("compile_runs").update(data).eq("id", run_id).execute() + return res.data + +def get_source_hashes(company_id: str): + if not supabase: return {} + # Get the latest current brain + brain = get_current_brain(company_id) + if brain: + return brain.get("source_hashes", {}) + return {} + +def save_source_file(data: dict): + if not supabase: return None + res = supabase.table("source_files").insert(data).execute() + return res.data + +def get_skills_by_brain_id(brain_id: str): + if not supabase: return [] + res = supabase.table("skills").select("*").eq("skills_file_id", brain_id).execute() + return res.data + +def insert_skills(data: list): + if not supabase: return None + res = supabase.table("skills").insert(data).execute() + return res.data diff --git a/backend/graph/graph.py b/backend/graph/graph.py new file mode 100644 index 0000000000000000000000000000000000000000..92d55f33ffb891ccb2ba3bd22910e399b367d4d2 --- /dev/null +++ b/backend/graph/graph.py @@ -0,0 +1,30 @@ +from langgraph.graph import StateGraph, END +from backend.graph.state import BrainState +from backend.graph.nodes.load_and_chunk import load_and_chunk +from backend.graph.nodes.cluster_evidence import cluster_evidence +from backend.graph.nodes.synthesize_skills import synthesize_skills +from backend.graph.nodes.quality_normalize import quality_normalize +from backend.graph.nodes.write_brain import write_brain + + +def build_compilation_graph() -> StateGraph: + """ + Linear 5-node pipeline: + load_and_chunk → cluster_evidence → synthesize_skills → quality_normalize → write_brain + """ + workflow = StateGraph(BrainState) + + workflow.add_node("load_and_chunk", load_and_chunk) + workflow.add_node("cluster_evidence", cluster_evidence) + workflow.add_node("synthesize_skills", synthesize_skills) + workflow.add_node("quality_normalize", quality_normalize) + workflow.add_node("write_brain", write_brain) + + workflow.set_entry_point("load_and_chunk") + workflow.add_edge("load_and_chunk", "cluster_evidence") + workflow.add_edge("cluster_evidence", "synthesize_skills") + workflow.add_edge("synthesize_skills", "quality_normalize") + workflow.add_edge("quality_normalize", "write_brain") + workflow.add_edge("write_brain", END) + + return workflow.compile() diff --git a/backend/graph/nodes/cluster_evidence.py b/backend/graph/nodes/cluster_evidence.py new file mode 100644 index 0000000000000000000000000000000000000000..831d6593b368d527daa1b65f0e25311ee2743455 --- /dev/null +++ b/backend/graph/nodes/cluster_evidence.py @@ -0,0 +1,64 @@ +""" +Node 2: Embed all chunks and cluster them by domain using the LLM. +Emits SSE stage: EMBEDDING +""" +import json +from backend.graph.state import BrainState +from backend.llm import llm_call, get_embeddings +from backend.sse import emit + + +async def cluster_evidence(state: BrainState) -> dict: + job_id = state["job_id"] + chunks = state.get("chunks", []) + + print(f"[{job_id}] Node cluster_evidence started with {len(chunks)} chunks") + + if not chunks: + await emit(job_id, "stage", {"name": "EMBEDDING", "detail": "No chunks to embed"}) + return {"clusters": {"domains": {}}} + + await emit(job_id, "stage", {"name": "EMBEDDING", "detail": f"Embedding {len(chunks)} chunks"}) + + # Build a numbered summary of each chunk for the LLM + summaries = [] + for i, c in enumerate(chunks): + # Truncate long chunks for the categorization prompt + preview = c["text"][:300].replace("\n", " ") + summaries.append(f"[{i}] ({c['source_file']}) {preview}") + + chunk_list_text = "\n".join(summaries) + + prompt = """You are an operations analyst. Below is a numbered list of text chunks extracted from a company's internal documents (SOPs, Slack messages, support tickets). + +Categorize each chunk into an operational domain. Use clear domain names like: +"Customer Support", "Engineering", "Sales", "Human Resources", "Finance", "Operations", etc. + +Return ONLY a valid JSON object mapping domain names to arrays of chunk indices. +Example: {"Customer Support": [0, 3, 5], "Engineering": [1, 2], "Sales": [4]} + +Every chunk index must appear exactly once. Do not skip any.""" + + response_str = await llm_call(prompt, chunk_list_text) + + try: + clean = response_str.strip() + if clean.startswith("```json"): + clean = clean[7:] + if clean.startswith("```"): + clean = clean[3:] + if clean.endswith("```"): + clean = clean[:-3] + domains = json.loads(clean.strip()) + except Exception as e: + print(f"[cluster_evidence] Failed to parse LLM clustering: {e}") + # Fallback: put all chunks in one cluster + domains = {"General": list(range(len(chunks)))} + + await emit(job_id, "stage", { + "name": "EMBEDDING_DONE", + "detail": f"Clustered into {len(domains)} domains: {list(domains.keys())}", + }) + + print(f"[{job_id}] Node cluster_evidence finished with {len(domains)} domains") + return {"clusters": {"domains": domains}} diff --git a/backend/graph/nodes/load_and_chunk.py b/backend/graph/nodes/load_and_chunk.py new file mode 100644 index 0000000000000000000000000000000000000000..826afa6f3ef8240c9f194cc10f9f09245ecb3622 --- /dev/null +++ b/backend/graph/nodes/load_and_chunk.py @@ -0,0 +1,174 @@ +""" +Node 1: Load source files from disk and chunk them. +Emits SSE stages: LOADING_DOCS, CHUNKING +""" +import os +import json +import hashlib +import time +from backend.graph.state import BrainState +from backend.sse import emit + + +async def load_and_chunk(state: BrainState) -> dict: + company_id = state["company_id"] + job_id = state["job_id"] + + print(f"[{job_id}] Node load_and_chunk started") + await emit(job_id, "stage", {"name": "LOADING_DOCS", "detail": f"Reading sources for {company_id}"}) + + # Read files from the company-specific directory + # __file__ is backend/graph/nodes/load_and_chunk.py + base = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) + sources_dir = os.path.join(base, "data", "sources", company_id) + + if not os.path.isdir(sources_dir): + await emit(job_id, "pipeline_error", {"error": f"No source directory found: data/sources/{company_id}/"}) + print(f"[{job_id}] Node load_and_chunk failed (Missing dir: {sources_dir})") + return {"errors": [f"Missing directory: {sources_dir}"], "source_files": [], "chunks": []} + + source_files = [] + for filename in sorted(os.listdir(sources_dir)): + filepath = os.path.join(sources_dir, filename) + if not os.path.isfile(filepath): + continue + with open(filepath, "r", encoding="utf-8") as f: + content = f.read() + doc_type = _detect_type(filename) + source_files.append({ + "filename": filename, + "content": content, + "sha256": hashlib.sha256(content.encode("utf-8")).hexdigest(), + "doc_type": doc_type, + }) + + await emit(job_id, "stage", { + "name": "CHUNKING", + "detail": f"Splitting {len(source_files)} files into chunks", + }) + + chunks = [] + for sf in source_files: + if sf["doc_type"] == "notion_md": + chunks.extend(_chunk_markdown(sf)) + elif sf["doc_type"] == "slack_json": + chunks.extend(_chunk_slack(sf)) + elif sf["doc_type"] == "tickets_json": + chunks.extend(_chunk_tickets(sf)) + else: + # Treat unknown as plain text + chunks.append({ + "text": sf["content"], + "source_file": sf["filename"], + "chunk_index": 0, + "doc_type": sf["doc_type"], + }) + + await emit(job_id, "stage", { + "name": "CHUNKING_DONE", + "detail": f"Produced {len(chunks)} chunks from {len(source_files)} files", + }) + + print(f"[{job_id}] Node load_and_chunk finished (chunks: {len(chunks)})") + return {"source_files": source_files, "chunks": chunks} + + +# --- Helpers --- + +def _detect_type(filename: str) -> str: + fn = filename.lower() + if fn.endswith(".json"): + if "slack" in fn: + return "slack_json" + if "ticket" in fn or "zendesk" in fn: + return "tickets_json" + return "json" + if fn.endswith(".md"): + return "notion_md" + return "unknown" + + +def _chunk_markdown(sf: dict) -> list: + """Split a markdown file by ## headers. Each section is a chunk.""" + content = sf["content"] + sections = [] + current_header = "Introduction" + current_body = [] + + for line in content.split("\n"): + if line.startswith("## "): + if current_body: + sections.append((current_header, "\n".join(current_body).strip())) + current_header = line.lstrip("# ").strip() + current_body = [] + else: + current_body.append(line) + + if current_body: + sections.append((current_header, "\n".join(current_body).strip())) + + chunks = [] + for i, (header, body) in enumerate(sections): + if not body: + continue + chunks.append({ + "text": f"[{header}] {body}", + "source_file": sf["filename"], + "chunk_index": i, + "doc_type": "notion_md", + "section_header": header, + }) + return chunks + + +def _chunk_slack(sf: dict) -> list: + """Each Slack message is one chunk.""" + try: + messages = json.loads(sf["content"]) + except json.JSONDecodeError: + return [] + chunks = [] + for i, msg in enumerate(messages): + text = msg.get("text", "") + if not text: + continue + user = msg.get("user", "unknown") + channel = msg.get("channel", "unknown") + chunks.append({ + "text": f"[Slack #{channel} @{user}] {text}", + "source_file": sf["filename"], + "chunk_index": i, + "doc_type": "slack_json", + }) + return chunks + + +def _chunk_tickets(sf: dict) -> list: + """Each ticket is one chunk.""" + try: + tickets = json.loads(sf["content"]) + except json.JSONDecodeError: + return [] + chunks = [] + for i, tkt in enumerate(tickets): + parts = [] + if tkt.get("subject"): + parts.append(f"Subject: {tkt['subject']}") + if tkt.get("description"): + parts.append(f"Description: {tkt['description']}") + if tkt.get("resolution"): + parts.append(f"Resolution: {tkt['resolution']}") + if tkt.get("priority"): + parts.append(f"Priority: {tkt['priority']}") + if tkt.get("customer_plan"): + parts.append(f"Plan: {tkt['customer_plan']}") + text = " | ".join(parts) + if not text: + continue + chunks.append({ + "text": f"[Zendesk Ticket] {text}", + "source_file": sf["filename"], + "chunk_index": i, + "doc_type": "tickets_json", + }) + return chunks diff --git a/backend/graph/nodes/quality_normalize.py b/backend/graph/nodes/quality_normalize.py new file mode 100644 index 0000000000000000000000000000000000000000..0be2a0fa5491814fe1009b13dee46d6708d9696c --- /dev/null +++ b/backend/graph/nodes/quality_normalize.py @@ -0,0 +1,83 @@ +""" +Node 4: De-duplicate skills, resolve conflicts, score confidence, enforce schema. +Emits SSE stage: QUALITY_CHECK +""" +import json +from backend.graph.state import BrainState +from backend.llm import llm_call +from backend.sse import emit + + +async def quality_normalize(state: BrainState) -> dict: + job_id = state["job_id"] + raw_skills = state.get("raw_skills", []) + + print(f"[{job_id}] Node quality_normalize started with {len(raw_skills)} raw skills") + + if not raw_skills: + await emit(job_id, "stage", {"name": "QUALITY_CHECK", "detail": "No skills to normalize"}) + print(f"[{job_id}] Node quality_normalize finished (0 skills)") + return {"skills_file": {"skills": []}} + + await emit(job_id, "stage", { + "name": "QUALITY_CHECK", + "detail": f"Normalizing {len(raw_skills)} raw skills", + }) + + prompt = """You are a quality assurance agent for an operational skills file. + +Below is a raw list of skills extracted from company documents. Your job: + +1. DEDUPLICATE: merge skills that describe the same rule (keep the most complete version). +2. RESOLVE CONFLICTS: if two skills contradict, keep both but note the conflict in the rationale. Prefer observed behavior (from Slack/tickets) over stated policy (from SOPs) when they conflict. +3. SCORE CONFIDENCE (0.0 to 1.0) for each skill based on: + - 0.9–1.0: multiple confirming sources, clear unambiguous rule + - 0.7–0.89: single strong source or multiple weak sources + - 0.5–0.69: only one source, or some ambiguity + - 0.3–0.49: weak evidence or significant ambiguity + - < 0.3: speculative or poorly supported +4. ENFORCE SCHEMA: every skill must have: id, category, rule, rationale, evidence (array), confidence (float). + +Return ONLY a JSON object: +{ + "skills": [ + { + "id": "skill_slug", + "category": "Domain Name", + "rule": "The specific rule text", + "rationale": "Why this rule exists", + "evidence": ["source reference 1", "source reference 2"], + "confidence": 0.85 + } + ] +}""" + + skills_text = json.dumps(raw_skills, indent=2) + print(f"[{job_id}] Requesting quality normalization...") + response_str = await llm_call(prompt, skills_text, max_tokens=8192) + print(f"[{job_id}] Received quality normalization response") + + try: + clean = response_str.strip() + if clean.startswith("```json"): + clean = clean[7:] + if clean.startswith("```"): + clean = clean[3:] + if clean.endswith("```"): + clean = clean[:-3] + data = json.loads(clean.strip()) + final_skills = data.get("skills", raw_skills) + except Exception as e: + print(f"[{job_id}] [quality_normalize] Parse error: {e}") + # Fallback: use raw skills with default confidence + final_skills = raw_skills + for sk in final_skills: + sk.setdefault("confidence", 0.5) + + await emit(job_id, "stage", { + "name": "QUALITY_CHECK_DONE", + "detail": f"Final skills count: {len(final_skills)} (from {len(raw_skills)} raw)", + }) + + print(f"[{job_id}] Node quality_normalize finished (final skills: {len(final_skills)})") + return {"skills_file": {"skills": final_skills}} diff --git a/backend/graph/nodes/synthesize_skills.py b/backend/graph/nodes/synthesize_skills.py new file mode 100644 index 0000000000000000000000000000000000000000..9b0275fb88f1c12d6f003327cda0c5d350a4940b --- /dev/null +++ b/backend/graph/nodes/synthesize_skills.py @@ -0,0 +1,111 @@ +""" +Node 3: For each domain cluster, call vLLM to synthesize structured skills. +Emits SSE stage: SYNTHESIZING_SKILLS +""" +import json +import uuid +from backend.graph.state import BrainState +from backend.llm import llm_call +from backend.sse import emit + + +async def synthesize_skills(state: BrainState) -> dict: + job_id = state["job_id"] + chunks = state.get("chunks", []) + clusters = state.get("clusters", {}) + domains = clusters.get("domains", {}) + + print(f"[{job_id}] Node synthesize_skills started with {len(domains)} domains") + + if not domains: + await emit(job_id, "stage", {"name": "SYNTHESIZING_SKILLS", "detail": "No clusters to synthesize"}) + print(f"[{job_id}] Node synthesize_skills finished (0 domains)") + return {"raw_skills": []} + + await emit(job_id, "stage", { + "name": "SYNTHESIZING_SKILLS", + "detail": f"Synthesizing skills for {len(domains)} domains", + }) + + all_skills = [] + + for domain_name, chunk_indices in domains.items(): + # Gather the actual chunk texts for this domain + domain_chunks = [] + for idx in chunk_indices: + if 0 <= idx < len(chunks): + domain_chunks.append(chunks[idx]) + + if not domain_chunks: + continue + + chunk_text = "\n\n".join([c["text"] for c in domain_chunks]) + source_files = list(set(c["source_file"] for c in domain_chunks)) + + prompt = f"""You are a Principal Operations Architect analyzing the "{domain_name}" domain. + +Below are real excerpts from a company's internal documents (SOPs, Slack messages, support tickets) related to {domain_name}. + +Your job: extract every distinct operational rule, policy, process, or decision pattern you can find. + +For EACH skill, provide: +- id: a unique identifier (use a short slug like "refund_loyal_customer") +- category: "{domain_name}" +- rule: the specific, actionable rule or process (be precise — include thresholds, timeframes, approvals) +- rationale: why this rule exists (based on the evidence) +- evidence: array of specific quotes or references from the source chunks that support this rule +- source_files: which files this came from + +Rules for quality: +- Extract what the documents ACTUALLY say, not what you assume. +- If there are contradictions (e.g., SOP says X but Slack shows Y), note BOTH and state which takes precedence in practice. +- Do NOT invent rules that aren't supported by the text below. +- Each rule should be specific enough that a human could follow it without additional context. + +Respond with ONLY a JSON object: +{{ + "skills": [ + {{ + "id": "refund_loyal_customer", + "category": "{domain_name}", + "rule": "Approve refunds up to 45 days for customers with >2 years tenure", + "rationale": "Exception applied over standard 30-day limit for loyal customers", + "evidence": ["slack_export_support.json: Mike approved 45-day refund for Acme Corp"], + "source_files": ["slack_export_support.json", "notion_refund_sop.md"] + }} + ] +}}""" + + print(f"[{job_id}] Requesting skills for domain '{domain_name}'...") + response_str = await llm_call(prompt, chunk_text) + print(f"[{job_id}] Received skills response for domain '{domain_name}'") + + try: + clean = response_str.strip() + if clean.startswith("```json"): + clean = clean[7:] + if clean.startswith("```"): + clean = clean[3:] + if clean.endswith("```"): + clean = clean[:-3] + data = json.loads(clean.strip()) + domain_skills = data.get("skills", []) + except Exception as e: + print(f"[{job_id}] [synthesize_skills] Parse error for {domain_name}: {e}") + domain_skills = [] + + # Ensure every skill has an id + for sk in domain_skills: + if not sk.get("id"): + sk["id"] = str(uuid.uuid4())[:8] + sk["category"] = domain_name # ensure consistency + + all_skills.extend(domain_skills) + + await emit(job_id, "stage", { + "name": "SYNTHESIZING_SKILLS", + "detail": f"{domain_name}: extracted {len(domain_skills)} skills", + }) + + print(f"[{job_id}] Node synthesize_skills finished (extracted {len(all_skills)} skills overall)") + return {"raw_skills": all_skills} diff --git a/backend/graph/nodes/write_brain.py b/backend/graph/nodes/write_brain.py new file mode 100644 index 0000000000000000000000000000000000000000..9b9c5d243ee7cf336e7a2462301a3f473a065938 --- /dev/null +++ b/backend/graph/nodes/write_brain.py @@ -0,0 +1,96 @@ +""" +Node 5: Write the final skills file to the database. +Emits SSE stage: WRITING_DB, then pipeline_complete. +""" +import time +import json +import uuid +import datetime +from backend.graph.state import BrainState +from backend.db.supabase import get_client +from backend.sse import emit + + +async def write_brain(state: BrainState) -> dict: + job_id = state.get("job_id") + company_id = state.get("company_id") + skills_file = state.get("skills_file", {}) + skills = skills_file.get("skills", []) + start_time = state.get("start_time", time.time()) + duration_ms = int((time.time() - start_time) * 1000) + + print(f"[{job_id}] Node write_brain started for {company_id}") + + await emit(job_id, "stage", {"name": "WRITING_DB", "detail": f"Persisting {len(skills)} skills"}) + + db = get_client() + if not db: + await emit(job_id, "pipeline_error", {"error": "Database connection failed"}) + print(f"[{job_id}] Node write_brain failed (no DB client)") + return {"errors": ["DB connection failed in write_brain"]} + + try: + now_iso = datetime.datetime.now(datetime.timezone.utc).isoformat() + version_str = f"v_{int(time.time())}" + + source_hashes = {} + for f in state.get("source_files", []): + if "filename" in f and "sha256" in f: + source_hashes[f["filename"]] = f["sha256"] + + # Mark previous brain as not current + db.table("skills_files").update( + {"is_current": False} + ).eq("company_id", company_id).eq("is_current", True).execute() + + # Insert new brain + sf_res = db.table("skills_files").insert({ + "company_id": company_id, + "version": version_str, + "brain_json": skills_file, + "source_hashes": source_hashes, + "is_current": True, + }).execute() + + sf_id = sf_res.data[0]["id"] + + # Insert individual skills + for skill in skills: + db.table("skills").insert({ + "id": skill.get("id", str(uuid.uuid4())[:8]), + "company_id": company_id, + "skills_file_id": sf_id, + "name": skill.get("rule", "Unknown")[:200], + "domain": skill.get("category", "general"), + "version": version_str, + "confidence": float(skill.get("confidence", 0.5)), + "skill_json": skill, + }).execute() + + # Update compile run + db.table("compile_runs").update({ + "status": "complete", + "completed_at": now_iso, + "duration_ms": duration_ms, + "result_version": version_str, + }).eq("id", job_id).execute() + + except Exception as e: + print(f"[{job_id}] [write_brain] DB Error: {e}") + await emit(job_id, "pipeline_error", {"error": str(e)}) + return {"errors": [f"write_brain DB error: {e}"]} + + await emit(job_id, "stage", { + "name": "DONE", + "detail": f"Brain {version_str} written: {len(skills)} skills, {len(source_hashes)} sources, {duration_ms}ms", + }) + await emit(job_id, "pipeline_complete", { + "status": "success", + "version": version_str, + "skills_count": len(skills), + "source_count": len(source_hashes), + "duration_ms": duration_ms, + }) + + print(f"[{job_id}] Node write_brain finished successfully (version: {version_str})") + return {} diff --git a/backend/graph/state.py b/backend/graph/state.py new file mode 100644 index 0000000000000000000000000000000000000000..8d4ef0a1f2359fadaf2a7070592e59d3c88db212 --- /dev/null +++ b/backend/graph/state.py @@ -0,0 +1,14 @@ +from typing import TypedDict, Annotated, List, Dict, Any +import operator + +class BrainState(TypedDict): + company_id: str + job_id: str + source_files: List[Dict[str, Any]] # [{filename, content, sha256, doc_type}] + chunks: List[Dict[str, Any]] # [{text, source_file, chunk_index, doc_type}] + clusters: Dict[str, Any] # {domains: {domain_name: [chunk_indices]}} + raw_skills: List[Dict[str, Any]] # skills before quality pass + skills_file: Dict[str, Any] # final {skills: [...]} + brain_version: str + start_time: float + errors: Annotated[List[str], operator.add] diff --git a/backend/llm.py b/backend/llm.py new file mode 100644 index 0000000000000000000000000000000000000000..0815e88603329253597bb6be33a6222c0e9a453a --- /dev/null +++ b/backend/llm.py @@ -0,0 +1,65 @@ +import os +import json +import numpy as np +from openai import AsyncOpenAI +from dotenv import load_dotenv + +load_dotenv() + +VLLM_BASE_URL = os.getenv("VLLM_BASE_URL", "http://localhost:8000/v1") +MODEL_NAME = "RedHatAI/Qwen2.5-72B-Instruct-FP8-dynamic" + +llm = AsyncOpenAI(base_url=VLLM_BASE_URL, api_key="not-needed", timeout=120.0) + +# --- Embedding model (local, fast, centralized here) --- +_embedding_model = None + +def _get_embedding_model(): + global _embedding_model + if _embedding_model is None: + from sentence_transformers import SentenceTransformer + _embedding_model = SentenceTransformer("all-MiniLM-L6-v2") + return _embedding_model + +def get_embedding(text: str) -> list: + """Return a single embedding vector as a Python list.""" + model = _get_embedding_model() + return model.encode(text).tolist() + +def get_embeddings(texts: list) -> list: + """Return a list of embedding vectors.""" + model = _get_embedding_model() + return [v.tolist() for v in model.encode(texts)] + +def cosine_similarity(v1, v2) -> float: + """Cosine similarity between two vectors.""" + a, b = np.array(v1), np.array(v2) + denom = np.linalg.norm(a) * np.linalg.norm(b) + if denom == 0: + return 0.0 + return float(np.dot(a, b) / denom) + +async def check_vllm_health() -> dict: + """Ping the vLLM /v1/models endpoint. Returns status dict.""" + try: + response = await llm.models.list() + models = [m.id for m in response.data] + return {"healthy": True, "models": models, "url": VLLM_BASE_URL} + except Exception as e: + return {"healthy": False, "error": str(e), "url": VLLM_BASE_URL} + +async def llm_call(system_prompt: str, user_content: str, temperature: float = 0.1, max_tokens: int = 4096) -> str: + """Single centralized LLM call through vLLM. Raises on failure.""" + try: + response = await llm.chat.completions.create( + model=MODEL_NAME, + messages=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_content} + ], + temperature=temperature, + max_tokens=max_tokens + ) + return response.choices[0].message.content + except Exception as e: + raise RuntimeError(f"vLLM call failed ({VLLM_BASE_URL}): {e}") diff --git a/backend/main.py b/backend/main.py new file mode 100644 index 0000000000000000000000000000000000000000..5881e46810e8e3c7a6977f9ac6e0cf29ec6b89b9 --- /dev/null +++ b/backend/main.py @@ -0,0 +1,310 @@ +from fastapi import FastAPI, BackgroundTasks, HTTPException, UploadFile, File, Form +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import StreamingResponse +import os +import uuid +import time +import json +import hashlib +import shutil + +from backend.graph.graph import build_compilation_graph +from backend.sse import event_bus, emit +from backend.agent.brain_agent import handle_agent_query +from backend.db.supabase import get_client +from backend.llm import check_vllm_health +from backend.models.schemas import CompileRequest, AgentHandleRequest, AgentQueryRequest + +app = FastAPI(title="Kernl API", version="2.0.0") + +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +SOURCES_ROOT = os.path.join(BASE_DIR, "data", "sources") + + +# ───────────────────────────────────────────── +# Health +# ───────────────────────────────────────────── +@app.get("/health") +async def health_check(): + vllm = await check_vllm_health() + db = get_client() + return { + "status": "ok", + "vllm": vllm, + "database": "connected" if db else "not configured", + } + + +# ───────────────────────────────────────────── +# Source file management +# ───────────────────────────────────────────── +def _company_sources_dir(company_id: str) -> str: + return os.path.join(SOURCES_ROOT, company_id) + + +@app.post("/sources/upload") +async def upload_source(company_id: str = Form(...), file: UploadFile = File(...)): + """Upload a source file for a company.""" + dest_dir = _company_sources_dir(company_id) + os.makedirs(dest_dir, exist_ok=True) + + content = await file.read() + filepath = os.path.join(dest_dir, file.filename) + with open(filepath, "wb") as f: + f.write(content) + + file_hash = hashlib.sha256(content).hexdigest() + + # Record in DB + db = get_client() + if db: + try: + db.table("source_files").insert({ + "company_id": company_id, + "filename": file.filename, + "sha256": file_hash, + "storage_path": f"data/sources/{company_id}/{file.filename}", + }).execute() + except Exception as e: + print(f"[upload] DB record error: {e}") + + return {"filename": file.filename, "sha256": file_hash, "status": "uploaded"} + + +@app.get("/sources/{company_id}") +async def list_sources(company_id: str): + """List all source files for a company.""" + src_dir = _company_sources_dir(company_id) + if not os.path.isdir(src_dir): + return {"files": []} + files = [] + for fn in sorted(os.listdir(src_dir)): + fp = os.path.join(src_dir, fn) + if os.path.isfile(fp): + with open(fp, "rb") as f: + content = f.read() + files.append({ + "filename": fn, + "size_bytes": len(content), + "sha256": hashlib.sha256(content).hexdigest(), + }) + return {"files": files, "company_id": company_id} + + +@app.delete("/sources/{company_id}/{filename}") +async def delete_source(company_id: str, filename: str): + """Delete a source file.""" + filepath = os.path.join(_company_sources_dir(company_id), filename) + if not os.path.isfile(filepath): + raise HTTPException(status_code=404, detail=f"File not found: {filename}") + os.remove(filepath) + + db = get_client() + if db: + try: + db.table("source_files").delete().eq( + "company_id", company_id + ).eq("filename", filename).execute() + except Exception as e: + print(f"[delete] DB cleanup error: {e}") + + return {"status": "deleted", "filename": filename} + + +# ───────────────────────────────────────────── +# Compilation pipeline +# ───────────────────────────────────────────── +import asyncio +import traceback +import datetime + +async def run_compilation_graph(job_id: str, company_id: str): + initial_state = { + "job_id": job_id, + "company_id": company_id, + "source_files": [], + "chunks": [], + "clusters": {}, + "raw_skills": [], + "skills_file": {}, + "brain_version": "", + "start_time": time.time(), + "errors": [], + } + + graph = build_compilation_graph() + + await emit(job_id, "pipeline_start", {"company_id": company_id}) + try: + # Prevent indefinite hanging + await asyncio.wait_for(graph.ainvoke(initial_state), timeout=600.0) + except Exception as e: + err_msg = str(e) + if isinstance(e, asyncio.TimeoutError): + err_msg = "Pipeline execution timed out after 600 seconds." + + trace = traceback.format_exc() + print(f"Graph execution failed for {job_id}:\n{trace}") + + await emit(job_id, "pipeline_error", {"error": err_msg, "traceback": trace}) + # Update compile run status + db = get_client() + if db: + try: + db.table("compile_runs").update({ + "status": "error", + "completed_at": datetime.datetime.now(datetime.timezone.utc).isoformat(), + "error_detail": err_msg, + }).eq("id", job_id).execute() + except Exception as db_e: + print(f"Failed to update compile_runs with error status: {db_e}") + + +@app.post("/compile") +@app.post("/compile/run") +async def compile_brain(req: CompileRequest, background_tasks: BackgroundTasks): + # Verify source directory exists + src_dir = _company_sources_dir(req.company_id) + if not os.path.isdir(src_dir) or not os.listdir(src_dir): + raise HTTPException( + status_code=400, + detail=f"No source files found at data/sources/{req.company_id}/. Upload files first.", + ) + + job_id = str(uuid.uuid4()) + db = get_client() + + if db: + try: + db.table("compile_runs").insert({ + "id": job_id, + "company_id": req.company_id, + "status": "running", + }).execute() + except Exception as e: + print(f"Error creating run: {e}") + + background_tasks.add_task(run_compilation_graph, job_id, req.company_id) + return {"job_id": job_id, "status": "started"} + + +@app.get("/compile/{job_id}/stream") +async def compile_stream(job_id: str): + return StreamingResponse( + event_bus.event_generator(job_id), + media_type="text/event-stream", + ) + + +@app.get("/compile/{job_id}/status") +async def compile_status(job_id: str): + db = get_client() + if not db: + return {"status": "unknown", "error_detail": "No DB"} + res = db.table("compile_runs").select("*").eq("id", job_id).execute() + if not res.data: + return {"status": "not_found"} + return res.data[0] + + +# ───────────────────────────────────────────── +# Agent query +# ───────────────────────────────────────────── +@app.post("/agent/handle") +async def agent_handle_endpoint(req: AgentHandleRequest): + """Legacy endpoint — kept for frontend compat.""" + result = await handle_agent_query(req.company_id, req.scenario, req.context, req.with_brain) + return result + + +@app.post("/agent/query") +async def agent_query_endpoint(req: AgentQueryRequest): + """New canonical endpoint.""" + result = await handle_agent_query( + req.company_id, + req.scenario_text, + req.json_context, + req.with_brain, + ) + return result + + +# ───────────────────────────────────────────── +# Skills & brain versions +# ───────────────────────────────────────────── +@app.get("/skills") +async def get_skills_legacy(company_id: str): + """Legacy endpoint: returns raw brain_json.""" + db = get_client() + if not db: + raise HTTPException(status_code=500, detail="Database not connected") + res = db.table("skills_files").select("brain_json").eq( + "company_id", company_id + ).order("compiled_at", desc=True).limit(1).execute() + if not res.data: + return {"skills": []} + return res.data[0]["brain_json"] + + +@app.get("/skills/{company_id}") +async def get_skills(company_id: str): + """Returns detailed skills with metadata.""" + db = get_client() + if not db: + raise HTTPException(status_code=500, detail="Database not connected") + + res = db.table("skills_files").select("*").eq( + "company_id", company_id + ).eq("is_current", True).execute() + + if not res.data: + return {"skills": [], "version": None, "compiled_at": None} + + brain = res.data[0] + skills = brain["brain_json"].get("skills", []) + return { + "skills": skills, + "version": brain["version"], + "compiled_at": brain["compiled_at"], + "source_hashes": brain.get("source_hashes", {}), + "brain_id": brain["id"], + } + + +@app.get("/brain/versions/{company_id}") +async def list_brain_versions(company_id: str): + """Lists all brain versions for a company.""" + db = get_client() + if not db: + raise HTTPException(status_code=500, detail="Database not connected") + + res = db.table("skills_files").select( + "id, version, compiled_at, is_current, source_hashes" + ).eq("company_id", company_id).order("compiled_at", desc=True).execute() + + versions = [] + for row in res.data: + brain_json = None + # Get skill count from the full row + full = db.table("skills_files").select("brain_json").eq("id", row["id"]).execute() + skill_count = 0 + if full.data: + skill_count = len(full.data[0]["brain_json"].get("skills", [])) + versions.append({ + "id": row["id"], + "version": row["version"], + "compiled_at": row["compiled_at"], + "is_current": row["is_current"], + "source_count": len(row.get("source_hashes", {})), + "skill_count": skill_count, + }) + + return {"versions": versions, "company_id": company_id} diff --git a/backend/models/schemas.py b/backend/models/schemas.py new file mode 100644 index 0000000000000000000000000000000000000000..e271af5e43e846cf6f62b138c9e34a5d6faa54f0 --- /dev/null +++ b/backend/models/schemas.py @@ -0,0 +1,20 @@ +from pydantic import BaseModel +from typing import List, Optional, Dict, Any + +class CompileRequest(BaseModel): + company_id: str + force_recompile: bool = False + +class AgentHandleRequest(BaseModel): + """Legacy schema — kept for frontend compatibility.""" + company_id: str + scenario: str + context: Optional[Dict[str, Any]] = None + with_brain: bool = True + +class AgentQueryRequest(BaseModel): + """New canonical schema for agent queries.""" + company_id: str + scenario_text: str + json_context: Optional[Dict[str, Any]] = None + with_brain: bool = True diff --git a/backend/requirements.txt b/backend/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..9646415396f665f86bd02e1f2997dcdac3bddcca --- /dev/null +++ b/backend/requirements.txt @@ -0,0 +1,10 @@ +fastapi>=0.115 +uvicorn[standard] +openai +langgraph>=0.4 +sentence-transformers +numpy +supabase +python-dotenv +python-multipart +pydantic diff --git a/backend/sse.py b/backend/sse.py new file mode 100644 index 0000000000000000000000000000000000000000..a5e919857cb8707f94b9135a83f4a5b573b1e9f9 --- /dev/null +++ b/backend/sse.py @@ -0,0 +1,40 @@ +import asyncio +import json +from typing import Dict, AsyncGenerator + +class CompilationEventBus: + def __init__(self): + self.queues: Dict[str, asyncio.Queue] = {} + + def get_queue(self, job_id: str) -> asyncio.Queue: + if job_id not in self.queues: + self.queues[job_id] = asyncio.Queue() + return self.queues[job_id] + + async def emit_event(self, job_id: str, event_type: str, data: dict): + queue = self.get_queue(job_id) + await queue.put({"type": event_type, "data": data}) + + async def event_generator(self, job_id: str) -> AsyncGenerator[str, None]: + """Yields SSE-formatted strings. Uses unnamed events so the + frontend's EventSource.onmessage handler fires correctly. + Payload: data: {"event": "", "data": {}}\n\n + """ + queue = self.get_queue(job_id) + try: + while True: + event = await asyncio.wait_for(queue.get(), timeout=300) + payload = json.dumps({"event": event["type"], "data": event["data"]}) + yield f"data: {payload}\n\n" + if event["type"] in ["pipeline_complete", "pipeline_error"]: + break + except asyncio.TimeoutError: + yield f'data: {json.dumps({"event": "timeout", "data": {}})}\n\n' + finally: + if job_id in self.queues: + del self.queues[job_id] + +event_bus = CompilationEventBus() + +async def emit(job_id: str, event_type: str, data: dict): + await event_bus.emit_event(job_id, event_type, data) diff --git a/backend/test_compile.py b/backend/test_compile.py new file mode 100644 index 0000000000000000000000000000000000000000..d7263995a894267d8466db29dafbb815cbba90f2 --- /dev/null +++ b/backend/test_compile.py @@ -0,0 +1,89 @@ +import asyncio +import os +import json +import uuid +import sys +from dotenv import load_dotenv + +# Set backend in path +sys.path.append(os.path.dirname(os.path.dirname(__file__))) + +from backend.graph.graph import build_compilation_graph + +async def run_compilation_test(): + load_dotenv() + + # Check vLLM + vllm_url = os.getenv("VLLM_BASE_URL") + if not vllm_url: + print("VLLM_BASE_URL not set in .env. LLM calls will fail.") + else: + print(f"Using VLLM_BASE_URL: {vllm_url}") + + company_id = "rivanly-inc" + job_id = str(uuid.uuid4()) + + # Read files + source_files = [] + sources_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), "data", "sources") + if os.path.exists(sources_dir): + import hashlib + for filename in os.listdir(sources_dir): + filepath = os.path.join(sources_dir, filename) + with open(filepath, "r", encoding="utf-8") as f: + content = f.read() + + ftype = "unknown" + if filename.endswith(".json"): + if "slack" in filename: ftype = "slack_json" + elif "tickets" in filename: ftype = "tickets_json" + elif filename.endswith(".md"): + ftype = "notion_md" + + source_files.append({ + "filename": filename, + "content": content, + "type": ftype, + "sha256": hashlib.sha256(content.encode('utf-8')).hexdigest() + }) + else: + print(f"No sources dir found at {sources_dir}") + return + + print(f"Found {len(source_files)} source files. Starting graph...") + + initial_state = { + "job_id": job_id, + "company_id": company_id, + "source_files": source_files, + "structured_sops": [], + "normalized_events": [], + "resolved_cases": [], + "extracted_decisions": [], + "extracted_workflows": [], + "extracted_exceptions": [], + "detected_contradictions": [], + "skills_file": {} + } + + graph = build_compilation_graph() + + try: + final_state = await graph.ainvoke(initial_state) + print("\n=== COMPILATION COMPLETE ===") + print(f"Extracted Decisions: {len(final_state.get('extracted_decisions', []))}") + print(f"Detected Contradictions: {len(final_state.get('detected_contradictions', []))}") + for c in final_state.get('detected_contradictions', []): + print(f" - Contradiction: {c}") + + skills_file = final_state.get('skills_file', {}) + skills = skills_file.get('skills', []) + print(f"Generated Skills: {len(skills)}") + for s in skills: + print(f" - {s.get('id')} ({s.get('confidence')} conf)") + + except Exception as e: + print(f"Graph execution failed: {e}") + +if __name__ == "__main__": + asyncio.run(run_compilation_test()) diff --git a/brand_alchemy_company_brain.html b/brand_alchemy_company_brain.html new file mode 100644 index 0000000000000000000000000000000000000000..3e7cf368f0516921470daab8884e5f1386addd49 --- /dev/null +++ b/brand_alchemy_company_brain.html @@ -0,0 +1,254 @@ + + + +
+

Brand Alchemy Report — Company Brain

+ +
Product DNA — what was learned
+
+

A compiler layer that extracts the operational judgment scattered across Slack, SOPs, tickets and people's heads — and produces a versioned, evidence-linked, executable skills file any AI agent can consume.

+

Not RAG. Not a chatbot. Not search. A compiler of how a company actually decides things — living, stale-aware, and infrastructure-grade.

+
+ +
+
+
Category
+
AI Infrastructure
+
+
+
Audience
+
B2B SaaS Ops + AI Builders
+
+
+
Brand vibe
+
Authoritative + Precise
+
+
+
Core metaphor
+
Compiler, not assistant
+
+
+
Alternative today
+
Long system prompts, Notion docs
+
+
+
The moat
+
Stale detection + evidence trail
+
+
+ +
+ +
Category point of view (POV)
+
+

The old category — "knowledge management" — is about humans finding information. The new category is operational memory infrastructure: the persistent, executable layer that lets AI agents inherit a company's judgment. The race isn't between AI models. It's between companies that give their agents operational memory and those that don't.

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+ +
+ +
Phonosemantics key — sound symbolism used
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+ P — plosive → authority, precision + L — liquid → intelligence, flow + F — fricative → speed, edge + N — nasal → warmth, connection + K — hard plosive → technical force +
+ +
5 name candidates — linguistic breakdown + domain verification
+ + +
+
+
Kernl
+ ★ top pick +
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+
+
+
"The operational kernel your AI agents run on."
+
+ K plosive — technical force + R liquid — intelligence + N nasal — familiar + dropped 'e' — modern tech register +
+
A kernel is the essential core of an operating system — the layer that mediates between hardware and everything above it. Kernl is that layer for AI agents: between raw company data and reliable automation. The dropped final 'e' (à la Tumblr, Flickr) signals a distinctly technical register. Infrastructure-grade naming — belongs alongside Redis, Kafka, Postgres.
+
+ kernl.com + kernl.ai + kernl.io +
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+ + +
+
+
Meld
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+
+
+
+
"Melds scattered knowledge into a single executable mind."
+
+ M nasal — warmth, familiarity + L liquid — intelligence + D plosive — decisive, final + 1 syllable — maximum compression +
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To meld is to blend disparate elements into a unified whole. In card games, it means to lay down your hidden hand — revealing what was implicit. Both meanings map perfectly: Meld takes scattered, implicit operational knowledge and merges it into explicit, executable form. One syllable, pure infrastructure energy, zero ambiguity.
+
+ meld.com + meld.ai + meld.io +
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+ + +
+
+
Opslore
+
+
+
+
+
"The living operational lore of your company, made executable."
+
+ P plosive — operational precision + S fricative — speed + L+R liquids — intelligence, depth +
+
Lore is the body of accumulated, traditional knowledge belonging to a group — the unwritten rules, the cultural wisdom. "Opslore" is the operational lore of a company: how refunds get handled, why escalation chains exist, what the pricing exception rule actually is. The word has warmth and depth while remaining precise. Best domain position of any candidate — .com, .ai, and .io all clear.
+
+ opslore.com + opslore.ai + opslore.io +
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+ + +
+
+
Opscodex
+
+
+
+
+
"The compiled operational codex your agents execute from."
+
+ K+P plosives — double authority + X terminal — distinct, rare + Codex etymology — compiled law +
+
A codex was the ancient form of the book — sheets compiled and bound, replacing scrolls. Historically, codices preserved legal codes, canonical texts, laws. Opscodex is the compiled operational code of a company: the canonical, authoritative record of how things are decided. The terminal X adds sonic distinctiveness and technical sharpness. Carries scholarly gravitas — infrastructure, not a feature.
+
+ opscodex.com + opscodex.ai +
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+ + +
+
+
Lorekern
+
+
+
+
+
"The kernel of operational lore, distilled and executable."
+
+ L+R liquids — intelligence, flow + K plosive — technical core + N nasal — grounding +
+
Morpheme blend of Lore (accumulated operational wisdom) and Kern (kernel/core). Reads as two concepts merged — like the product itself: taking the living lore of an organization and distilling it into an executable core. More descriptive and compound than the other candidates; works well if a human-warmth brand angle is preferred over pure infrastructure framing.
+
+ lorekern.com + lorekern.ai +
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+ +
+ +
Diamond test — top pick: Kernl
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+
+
Distinctiveness
+
+
96
+
+
+
Processing fluency
+
+
95
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+
+
Relevance
+
+
97
+
+
+
Energy
+
+
88
+
+
+ +
+
Final recommendation: Kernl
+
Register kernl.com immediately. The kernel metaphor is structurally perfect — it is the deepest, most precise analogy in the OS/infra vocabulary for what this product does. The hard K plosive delivers maximum technical authority. The dropped 'e' places it firmly in infrastructure naming tradition. It scales: "your company's Kernl", "deploy Kernl", "the Kernl API". It competes with Redis and Kafka on naming gravity, which is exactly the positioning the PRD demands.
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+ +
+ +
Visual system direction
+ + + + + + +
Mark styleWordmark only. No icon. Infrastructure products don't need icons — they are the icon. Monospace or geometric sans. Think Stripe, Linear, Vercel.
ColorSingle accent on near-black ground. Deep teal (#0F6E56) or electric indigo — evokes precision and living systems without the generic "AI blue" trap.
VoiceDeclarative. Short sentences. Never explain — demonstrate. "12 skills. 58 seconds. Evidence-linked." — not "our platform leverages AI to..."
Tagline direction"Operational memory for AI agents." — or — "Your AI knows how your company works."
Pitch one-liner"Kernl compiles how your company decides into an executable skills file. Any agent. Any task. Correct every time."
+
diff --git a/company_brain_PRD_v4.md b/company_brain_PRD_v4.md new file mode 100644 index 0000000000000000000000000000000000000000..10569ff6fdb168673529f1612281bf7ee35534ab --- /dev/null +++ b/company_brain_PRD_v4.md @@ -0,0 +1,1061 @@ +# Company Brain — Product Requirements Document +**Version:** 4.0 — Final (Pre-Build, All Issues Resolved) +**Date:** May 4, 2026 +**Authors:** Abhijith Pingali, Harshit Anand +**Status:** Final — Build starts post-kickoff + +> **v4 changes over v3:** +> 1. Ground truth table completed — all 12 Rivanly scenarios with expected action + skill +> 2. `with_brain: false` behaviour fully documented in Section 9 +> 3. Section 10 user flow added — screen-to-screen navigation with decision points +> 4. Competitive landscape updated with 8 real companies identified in LinkedIn thread +> 5. Risk table updated: "knowledge never captured" risk added from Paul Breuler's comment +> 6. Section 2.5 added: "The Stale Knowledge Problem" — validates drift detection as core feature +> 7. Section 15 updated: execution boundary insight from Horizon Labs added to v2 roadmap + +--- + +## 1. Executive Summary + +**Problem:** AI agents deployed by B2B companies behave like a new hire on day one — they lack the operational judgment embedded in how the company actually decides things. This knowledge lives in Slack threads, SOPs, support tickets, and people's heads, invisible to any model. + +**Solution:** Company Brain is a compilation layer that extracts this operational judgment and produces a versioned, evidence-linked, executable skills file any AI agent can consume to act like the company's best employee. + +**Success Criteria (Hackathon v0):** + +| KPI | Target | +|---|---| +| Full compilation pipeline: sources → 12 skills | Completes without error, every run | +| Skills with confidence ≥ 0.7 | ≥ 10 of 12 | +| Brain agent: correct action on all Rivanly scenarios | 12 / 12 correct | +| Compilation time on AMD MI300X | < 90s (target 60s) | +| Brain agent response latency | < 8s per query | + +--- + +## 2. Problem Statement & Solution + +### 2.1 The Problem + +Every company trying to deploy AI automation hits the same wall. The models are good enough. The infrastructure is available. But the AI behaves like a new hire on day one — it doesn't know how the company actually operates. + +Refund policies live in Priya's head. Pricing exceptions get decided in Slack threads nobody archived. Escalation chains exist because three incidents taught the team the hard way. This operational knowledge — how the company actually decides things — is invisible to AI agents. + +Existing solutions miss this entirely. RAG retrieves document chunks. Chatbots answer questions. Neither gives an AI agent the operational judgment to do real work correctly and consistently. + +### 2.2 The Solution + +Company Brain is the missing compilation layer. It extracts the operational judgment embedded in how a company behaves — not what it documents, but how it actually decides — and compiles it into an executable, versioned, living skills file that any AI agent can use. + +**Agents are compilers, not assistants.** Company Brain's extraction agents do not summarize or search. They convert messy human behavior into structured, executable logic. The downstream brain agent that does real work is a consumer of that compiled output — it never reasons from scratch. + +### 2.3 One-Line Pitch + +> "We turn how your company actually operates into an executable Company Brain. Any agent can use it to do real work without guessing." + +### 2.4 Product Positioning + +Company Brain is **infrastructure, not a feature.** + +| What it is NOT | What it IS | +|---|---| +| RAG over documents | Compiler of operational judgment | +| Chatbot over your data | Executable skills file for AI agents | +| A search engine | A living map of how your company works | +| One-time snapshot | Versioned, updatable, drift-aware | + +### 2.5 The Stale Knowledge Problem (Why "Living" Matters) + +*Validated by multiple practitioners in the YC RFS LinkedIn thread.* + +The hardest part of any knowledge system is not building it — it is keeping it alive. Most companies will document their workflows once, ship the AI agent, and within six weeks the map diverges from reality. A new pricing exception gets approved in a Slack DM. An escalation chain changes when someone leaves. The AI keeps following the old rules. + +Company Brain's stale detection — SHA-256 hashing of source files, `stale: true` badges on affected skills, and recompile triggers — directly solves this. The skills file is not a document. It is a living artifact that stays current with how the company actually evolves. + +This is not a minor feature. It is the moat. + +### 2.6 What Company Brain Does NOT Solve (v0) + +*Acknowledged risk from Paul Breuler (BaseState founder), LinkedIn thread:* + +> "The decisions that matter happen in context, on the ground, and were never captured in a ticket or Slack thread." + +Company Brain compiles knowledge that was captured somewhere — Slack, SOPs, tickets, call transcripts. Knowledge that exists only in someone's head and was never written down or discussed in any recorded channel cannot be extracted. This is a known limitation. The pitch should never claim to capture all company knowledge — only the knowledge that was communicated in any digital form. + +For v1, voice call transcription addresses a portion of this gap. For v2, an active knowledge capture interface (where employees record decisions as they happen) closes it further. + +--- + +## 3. Target Customer & User Personas + +### 3.1 Primary Wedge — v1 + +**B2B SaaS companies, 10–50 employees, actively deploying AI automation for the first time.** + +These companies have: +- Enough operational complexity that AI agents fail without context +- Enough technical sophistication to understand why RAG isn't solving their problem +- Enough urgency to pay for a solution (they are actively trying and failing to deploy AI) +- Not enough resources to build this infrastructure themselves + +### 3.2 User Personas + +| Persona | Role | Primary Goal | Pain Point | +|---|---|---|---| +| **Ops Owner** | Head of Operations / Founder | Get AI agents to handle work consistently | Agents hallucinate edge cases; policies go stale | +| **AI Builder** | Developer / Automation Lead | Consume company knowledge in agent prompts | No structured source of truth to inject into prompts | +| **Agent Consumer** | Support agent, AM, anyone using the AI | Get correct, policy-backed responses instantly | Generic AI responses that don't match company policy | +| **Demo Viewer / Judge** | Hackathon judge, investor, prospect | Understand what the product does in 5 minutes | Can't distinguish this from "just another RAG tool" | + +### 3.3 Fictional Reference Customer — Rivanly Inc. + +Rivanly is a 15-person B2B SaaS company used throughout this document and the demo. 6 departments, 12 operational skills. Enough complexity to make the product real, small enough to demo in 5 minutes. + +### 3.4 Expanded Customer Universe — v2+ + +- E-commerce operators (refund, shipping, returns automation) +- Agencies (client approval, scope change, billing exception workflows) +- Healthcare admin (referral routing, prior authorization, scheduling exceptions) +- Legal operations (intake, escalation, matter routing) + +--- + +## 4. Jobs To Be Done + +| Job | Current Solution | Problem | +|---|---|---| +| "I want an AI agent to handle customer refunds correctly" | Write a long system prompt with refund rules | Rules go stale, edge cases missed, no evidence trail | +| "I need to onboard a new AI tool to how we operate" | Document everything manually in Notion | Takes weeks, immediately outdated, agent still hallucinates | +| "I want to know if my AI agent is following company policy" | Read agent logs manually | No structured audit trail linking actions to rules | +| "We updated our pricing policy — the AI needs to know" | Edit the system prompt manually | No systematic way to detect or propagate policy changes | +| "Why did the agent make that decision?" | Cannot answer | No evidence chain from agent action back to source | + +--- + +## 5. User Stories + +### Source Ingestion & File Handling + +1. As an Ops Owner, I want to upload `.md` Notion SOPs so that my written policies are ingested without manual reformatting. +2. As an Ops Owner, I want to upload Slack JSON exports so that informal decision patterns from real conversations are captured. +3. As an Ops Owner, I want to upload Zendesk ticket JSON exports so that resolved case reasoning is extracted as evidence. +4. As an Ops Owner, I want the system to detect unchanged files by SHA-256 hash so that re-uploading a file doesn't trigger unnecessary re-extraction. +5. As an Ops Owner, I want a clear parse error message when a file is malformed so that I know exactly which file to fix. +6. As an Ops Owner, I want unsupported file types to be rejected with a helpful error so that I don't wait for a compilation that will fail. +7. As an Ops Owner, I want source files stored in Supabase so that I don't need to re-upload them on every compile. + +### Compilation Pipeline + +8. As an Ops Owner, I want 4 extraction agents to run in parallel on AMD MI300X so that compilation completes under 90 seconds instead of 8+ minutes. +9. As an Ops Owner, I want IF-THEN-EXCEPT decision rules extracted from Slack threads so that informal decisions become structured, executable policies. +10. As an Ops Owner, I want sequential process steps extracted from SOPs and runbooks so that workflow sequences are captured correctly. +11. As an Ops Owner, I want edge cases, overrides, and "unless..." patterns extracted specifically so that exception logic isn't lost in summarization. +12. As an Ops Owner, I want contradictions between SOPs and actual Slack/ticket behavior flagged so that I can identify and resolve policy drift. +13. As an Ops Owner, I want skills with confidence below 0.6 to be flagged for human review rather than auto-published so that only verified rules go live. +14. As an Ops Owner, I want each decision rule backlinked to its source file and excerpt so that every policy is auditable, not asserted. +15. As an Ops Owner, I want the system to retry once if the LLM returns malformed JSON so that a single bad LLM response doesn't abort the whole compile. +16. As an Ops Owner, I want a clear compile error message if vLLM becomes unreachable so that I know the issue is infrastructure, not my data. +17. As a Developer, I want LangGraph checkpointing via MemorySaver so that a crashed compile can be inspected and does not silently lose data. + +### Skills File & Schema + +18. As an Ops Owner, I want each skill stored as a versioned JSON object with id, name, domain, confidence, decision_logic, forbidden_actions, escalation_chain, and evidence_sources so that the schema is complete and consistent. +19. As an Ops Owner, I want skills converted to markdown at query time so that they are injected into LLM prompts efficiently. +20. As an Ops Owner, I want the meta block of the skills file to store source hashes so that stale skills can be detected when source files change. +21. As an Ops Owner, I want the skills file versioned with semver so that every compile produces a traceable snapshot. + +### Version Management & Drift Detection + +22. As an Ops Owner, I want to compare any two historical brain versions in a diff view so that I can see exactly what changed after a policy update. +23. As an Ops Owner, I want changed rules highlighted in the diff (added green, removed red, modified yellow) so that I don't have to read everything to spot changes. +24. As an Ops Owner, I want stale skills badged in the Skills Viewer so that I know which skills need recompilation after a source file changed. +25. As an Ops Owner, I want at least 2 pre-seeded historical versions in the demo so that the diff view is usable on day one. + +### Brain Dashboard (Frontend) + +26. As an Ops Owner, I want a "Build Company Brain" button on the dashboard so that I can trigger recompilation from the UI without touching a terminal. +27. As an Ops Owner, I want the button to be disabled with a spinner during an active compile so that I can't trigger duplicate jobs. +28. As an Ops Owner, I want a real-time SSE feed showing each pipeline node completing with timestamps so that I trust the system is working. +29. As an Ops Owner, I want the compilation time displayed to the second so that I can use this as a live AMD MI300X proof point. +30. As an Ops Owner, I want the current brain version and last-compiled timestamp visible at a glance so that I know which brain is active. + +### Skills Viewer (Frontend) + +31. As an Ops Owner, I want skills grouped by department so that I can navigate to the right area quickly. +32. As an Ops Owner, I want a visual confidence bar per skill so that I can immediately see which skills are strong vs. uncertain without reading numbers. +33. As an Ops Owner, I want to expand any skill and see all its decision conditions and forbidden actions so that I can verify the rules are correct. +34. As an Ops Owner, I want the evidence panel per skill to show source file names and excerpts so that I can trace every policy back to where it came from. + +### Brain Agent (Demo) + +35. As an AI Builder, I want to submit a natural language scenario to the brain agent so that I get a structured action recommendation with evidence, not a guess. +36. As an AI Builder, I want the response to include the exact rule condition that matched so that I can verify the logic is correct. +37. As an AI Builder, I want the agent to gracefully handle scenarios with no matching skill so that low-confidence situations escalate to a human rather than produce a wrong answer. +38. As a Demo Viewer / Judge, I want to see the agent without the brain respond generically to the same scenario so that the value of the compilation layer is immediately obvious. +39. As a Demo Viewer / Judge, I want to see the "Change a SOP rule → Rebuild → same scenario → different outcome" flow so that I understand this is a living map, not a static snapshot. + +--- + +## 6. Product Scope + +### 6.1 Three-Ring Model + +| Ring | Name | Timeline | What Ships | +|---|---|---|---| +| **Ring 1** | Hackathon v0 | May 4–10, 2026 | Offline compiler, Rivanly demo, file upload inputs, brain agent demo | +| **Ring 2** | Product v1 | 4–6 weeks post-hackathon | Live connectors, multi-tenant, real company data, auth | +| **Ring 3** | Scale | 2–6 months | Agent SDK, skills marketplace, audit trails, RBAC | + +### 6.2 Hackathon v0 — In Scope + +- Multi-agent compilation pipeline (LangGraph, 4 parallel async extraction agents) +- 6-department, 12-skill coverage of Rivanly Inc. +- Synthetic dataset (8 source files authored before kickoff) +- Skills file: JSON storage, markdown runtime, evidence-linked, confidence-scored, versioned +- Brain agent: scenario input → in-memory skill match → structured response with rule trace +- Frontend: Brain Dashboard + Skills Viewer + Demo Agent panel +- Real-time SSE compilation progress feed +- Brain version diffing (v1.2 → v1.3 what changed) +- AMD MI300X deployment via vLLM (`RedHatAI/Qwen2.5-72B-Instruct-FP8-dynamic`) +- Side-by-side "with brain vs. without brain" comparison panel — **P0, the money shot** +- Build in Public: 2 posts on X/LinkedIn during build + +### 6.3 Out of Scope for v0 + +- Real Slack, Notion, Zendesk OAuth connectors — file upload only +- Multi-tenant isolation — single company demo +- Auth / login — none required for demo +- Redis job queue — direct `graph.ainvoke()` only +- pgvector — in-memory sentence-transformers for v0 skill matching +- Webhook-triggered recompilation +- Human skill review queue UI + +### 6.4 Team Ownership + +| Owner | Scope | +|---|---| +| **Abhijith** | F-01, F-02, F-03, F-04, F-05, F-06, F-07, F-12 (pipeline + API) | +| **Harshit** | F-08, F-09, F-10, F-11 (all frontend) | +| **Both** | Synthetic dataset — 4 files each, done before May 4 kickoff | + +--- + +## 7. Feature Requirements + +Priority: **[P0]** = demo breaks without it · **[P1]** = must ship · **[P2]** = ship if time allows + +--- + +### F-01: Source Ingestion [P0] + +**Functional Requirements:** +- Accept `.md`, `.json`, `.txt` file uploads +- Parse Notion SOP markdown → `structured_sops[]` +- Parse Slack export JSON → `normalized_events[]` +- Parse ticket JSON → `resolved_cases[]` +- Compute SHA-256 hash per file; compare to previous run; skip unchanged files +- No LLM calls at this stage — pure Python parsing only + +**Acceptance Criteria:** + +*AC-01-1:* +- **Given** a valid Notion SOP `.md` file is uploaded +- **When** the ingest node runs +- **Then** `structured_sops` contains at least one entry with `source`, `content`, and `type` fields + +*AC-01-2:* +- **Given** a file was uploaded in a previous compile with hash `H` +- **When** the same file is uploaded again unchanged +- **Then** the ingestion node skips extraction for that file and logs "hash match, skipping" + +*AC-01-3:* +- **Given** a malformed JSON file is uploaded +- **When** the ingest node attempts to parse it +- **Then** the SSE stream emits `node_error` with `file`, `error: "parse_error"`, and `detail` — and the compile continues with remaining files + +*AC-01-4:* +- **Given** an unsupported file type (e.g. `.xlsx`) is uploaded +- **When** `POST /sources/upload` is called +- **Then** the API returns `400` with `{"error": "unsupported_file_type", "accepted": [".md", ".json", ".txt"]}` + +--- + +### F-02: Parallel Extraction [P0] + +**Functional Requirements:** +- Four async LangGraph nodes run simultaneously via `Send` API + `await llm.ainvoke()` +- Decision Extractor: IF-THEN-EXCEPT judgment patterns from Slack + tickets +- Workflow Extractor: sequential process steps from SOPs and runbooks +- Exception Extractor: edge cases, overrides, "unless..." patterns +- Contradiction Detector: divergence between SOPs and actual behavior +- All four target `RedHatAI/Qwen2.5-72B-Instruct-FP8-dynamic` on AMD MI300X via vLLM + +**Acceptance Criteria:** + +*AC-02-1:* +- **Given** all three ingest nodes have completed +- **When** `route_to_extractors` is called +- **Then** all four extraction nodes start within 2 seconds of each other + +*AC-02-2:* +- **Given** the Rivanly synthetic dataset +- **When** extraction completes +- **Then** each extractor returns a non-empty list; `contradictions[]` contains ≥ 1 entry + +*AC-02-3:* +- **Given** the LLM returns malformed JSON for one extractor +- **When** the node catches the error +- **Then** it retries once with a stricter JSON-only prompt; if still malformed, returns empty list and emits `node_error` without aborting other extractors + +*AC-02-4:* +- **Given** all four async extractors complete +- **When** wall clock is checked +- **Then** total extraction time is under 45 seconds on MI300X + +--- + +### F-03: Skill Compilation [P0] + +**Functional Requirements:** +- Synthesize extractor outputs into 12 canonical skill objects +- Evidence linker: backfill `evidence_sources[]` for every `decision_logic` entry +- Confidence scorer: `f(source_count, source_recency, internal_consistency)` +- Skills below 0.6 confidence: present with `"review_required": true`, not auto-published +- Write `skills_file.json` to Supabase `skills_files` table with incremented semver + +**Acceptance Criteria:** + +*AC-03-1:* +- **Given** all extraction nodes have produced output +- **When** `synthesize_skills` runs +- **Then** output contains exactly 12 skill objects, each with all required schema fields + +*AC-03-2:* +- **Given** a skill has been synthesized +- **When** `link_evidence` runs +- **Then** every `decision_logic` entry has at least one `evidence_sources` entry with non-empty `source` and `excerpt` + +*AC-03-3:* +- **Given** a skill has only one supporting source +- **When** `score_confidence` runs +- **Then** that skill's `confidence` is below 0.7 and `review_required` is `true` if below 0.6 + +*AC-03-4:* +- **Given** compilation succeeds +- **When** `write_brain` runs +- **Then** `skills_files` table gains a new row with semver one minor bump higher, `is_current: true`, and all previous rows `is_current: false` + +--- + +### F-04: Skills File Format [P0] + +**Schema (per skill):** +```json +{ + "id": "handle_refund_request", + "name": "Handle Refund Request", + "domain": "support", + "version": "1.2", + "confidence": 0.91, + "stale": false, + "review_required": false, + "last_updated": "2026-05-04T09:30:00Z", + "trigger": { + "phrases": ["refund", "money back"], + "conditions": ["customer mentions payment dissatisfaction"] + }, + "decision_logic": [ + { + "condition": "plan == 'annual' AND days_since_purchase <= 14", + "action": "approve_full_refund", + "note": "No-questions policy within 14 days.", + "evidence_sources": [ + { "source": "notion_refund_sop.md", "excerpt": "...", "confidence": 0.95 } + ] + } + ], + "forbidden_actions": ["Never process refunds for lifetime deal accounts"], + "escalation_chain": ["support_agent", "support_lead", "account_manager", "founder"], + "sla": "respond_within_2h, resolve_within_24h" +} +``` + +**Acceptance Criteria:** + +*AC-04-1:* +- **Given** the compiled skills file +- **When** validated against JSON schema +- **Then** zero validation errors + +*AC-04-2:* +- **Given** a skill selected for prompt injection +- **When** converted to markdown +- **Then** output is plain English, contains all conditions and forbidden actions, under 800 tokens + +--- + +### F-05: Brain Version Management [P1] + +**Acceptance Criteria:** + +*AC-05-1:* +- **Given** one source file changed and recompilation triggered +- **When** compile finishes +- **Then** new brain version is a minor bump (`1.2.0 → 1.3.0`) and diff endpoint returns that file's dependent skills as `modified_skills` + +*AC-05-2:* +- **Given** two brain versions exist +- **When** `GET /diff/1.2.0/1.3.0` is called +- **Then** response contains `added_skills`, `removed_skills`, and `modified_skills` with per-skill field-level changes + +*AC-05-3:* +- **Given** a source file changes +- **When** the new compile runs +- **Then** skills whose `evidence_sources` reference that file have `stale: true` + +--- + +### F-06: Scenario Handling — Brain Agent [P0] + +**Functional Requirements:** +- Accept natural language scenario input + optional structured context +- Embed query via `all-MiniLM-L6-v2` (in-memory, CPU); pre-compute skill embeddings once at startup +- Cosine similarity match → select top skill +- Convert skill JSON → markdown snippet +- Single LLM call: company context + skill rules + scenario +- Return structured response (F-07) + +**Acceptance Criteria:** + +*AC-06-1:* +- **Given** an enterprise refund scenario +- **When** `POST /agent/handle` is called +- **Then** matched skill is `handle_refund_request` (cosine similarity > 0.6) + +*AC-06-2:* +- **Given** all 12 Rivanly demo scenarios submitted in sequence +- **When** each response reviewed +- **Then** all 12 return correct action (verified against ground truth table in Section 12) + +*AC-06-3:* +- **Given** a scenario matching no skill above cosine 0.4 +- **When** match function runs +- **Then** response is `{"action": "escalate_to_human", "reason": "no_skill_match", "confidence": }` — not an error, not a hallucination + +*AC-06-4:* +- **Given** a valid scenario submitted +- **When** response returned +- **Then** wall-clock latency under 8 seconds + +--- + +### F-07: Response Structure [P0] + +Every `POST /agent/handle` response: + +```json +{ + "action": "escalate_to_am_within_1hr", + "message_to_customer": "...", + "rule_applied": "plan == 'enterprise' AND any_amount", + "evidence": { + "source": "slack_thread_2024-03-12", + "excerpt": "enterprise = always AM" + }, + "skill_matched": "handle_refund_request", + "confidence": 0.91 +} +``` + +**Acceptance Criteria:** + +*AC-07-1:* +- **Given** any valid scenario input +- **When** brain agent responds +- **Then** all six top-level fields present and non-null + +*AC-07-2:* +- **Given** the response `rule_applied` field +- **When** compared to matched skill's `decision_logic` +- **Then** string matches an exact `condition` field — never paraphrased + +--- + +### F-08: Brain Dashboard [P0] + +**Acceptance Criteria:** + +*AC-08-1:* +- **Given** the dashboard is loaded +- **When** user clicks "Build Company Brain" +- **Then** button becomes disabled with spinner and SSE feed begins showing node events within 2 seconds + +*AC-08-2:* +- **Given** SSE stream is active +- **When** each pipeline node completes +- **Then** feed shows node name, completion checkmark, and elapsed time in real time without page refresh + +*AC-08-3:* +- **Given** compilation completes +- **When** dashboard updates +- **Then** brain version, last-compiled timestamp, and total compilation time displayed with correct values + +--- + +### F-09: Skills Viewer [P0] + +**Acceptance Criteria:** + +*AC-09-1:* +- **Given** a compiled brain with 12 skills +- **When** Skills Viewer loaded +- **Then** all 12 skills visible, grouped under 6 correct department headings + +*AC-09-2:* +- **Given** a skill with `stale: true` +- **When** it appears in the viewer +- **Then** it has a visible "Stale" badge and confidence bar is visually de-emphasized + +*AC-09-3:* +- **Given** user clicks any skill +- **When** detail panel expands +- **Then** all `decision_logic` conditions, all `forbidden_actions`, `escalation_chain`, and at least one `evidence_sources` entry visible + +--- + +### F-10: Demo Agent Panel [P0] — Side-by-Side Required + +**Functional Requirements:** +- Free-text scenario input + optional structured context fields +- Two response panels rendered simultaneously: + - **Without Brain:** Same LLM, same scenario, system prompt contains only the raw scenario — no company name, no skills context, no Rivanly-specific information. Goal: produce a demonstrably generic response. + - **With Brain:** Full skill context + rule trace + evidence +- Visual trace showing matched skill and cosine similarity score + +**Acceptance Criteria:** + +*AC-10-1:* +- **Given** a scenario submitted +- **When** both panels render +- **Then** both responses appear within 10 seconds + +*AC-10-2:* +- **Given** the enterprise refund demo scenario +- **When** both panels render +- **Then** "Without Brain" response is generic (no company-specific rule, no evidence); "With Brain" response includes `rule_applied` and `evidence` visually highlighted + +*AC-10-3:* +- **Given** a judge views the demo panel for the first time +- **When** they read both responses +- **Then** the value of the brain is legible without any verbal explanation + +--- + +### F-11: Version Diff View [P1] + +**Acceptance Criteria:** + +*AC-11-1:* +- **Given** two pre-seeded brain versions (v1.1.0 and v1.2.0) +- **When** diff view opened and both selected +- **Then** modified skills highlighted yellow with field-level changes inline + +*AC-11-2:* +- **Given** the "change a rule → rebuild → diff" demo flow +- **When** performed end-to-end +- **Then** diff correctly shows changed rule in under 30 seconds of demo time + +--- + +### F-12: API Layer [P0] + +**`POST /compile`** +``` +Request: { "company_id": "rivanly-inc", "force_recompile": false } +Response: { "job_id": "uuid", "status": "started", "stream_url": "/compile/stream?job_id=uuid" } +``` + +**`GET /brain/status`** +``` +Response: { + "company_id": "rivanly-inc", + "brain_version": "1.3.0", + "last_compiled_at": "2026-05-04T09:30:00Z", + "total_skills": 12, + "stale_skills": 2, + "coverage_areas": ["support", "revenue", "product_eng", "customer_success", "hr", "finance_ops"] +} +``` + +**`GET /skills`** +``` +Response: { + "skills": [ + { "id": "handle_refund_request", "name": "Handle Refund Request", + "domain": "support", "confidence": 0.91, "stale": false, "version": "1.2" } + ] +} +``` + +**`GET /skills/:id`** → full skill object (schema per F-04) + +**`POST /agent/handle`** +``` +Request: { + "scenario": "Enterprise customer, 18 months tenure, wants $1,200 refund", + "context": { "plan": "enterprise", "tenure_months": 18, "refund_amount": 1200 }, + "with_brain": true +} +``` + +**`with_brain` flag behaviour (fully specified):** +- `with_brain: true` → system prompt includes: company name (Rivanly), active brain version, relevant skill in markdown, all decision conditions, forbidden actions, escalation chain +- `with_brain: false` → system prompt contains ONLY the raw scenario text. No company name. No skills context. No Rivanly-specific information. No hint that a brain exists. The goal is to produce a generic response from the base model that demonstrates what agents do WITHOUT the compilation layer. This is the "before" panel in the side-by-side comparison. + +**`GET /compile/stream?job_id=uuid`** — SSE event schema: +``` +event: node_start +data: {"node": "ingest_slack", "timestamp": "2026-05-04T09:30:01Z"} + +event: node_complete +data: {"node": "ingest_slack", "duration_ms": 312, "output_count": 47} + +event: node_error +data: {"node": "extract_decisions", "error": "llm_malformed_json", "retrying": true} + +event: compile_complete +data: { + "brain_version": "1.3.0", + "total_skills": 12, + "stale_skills": 0, + "duration_ms": 54200, + "skills_below_threshold": 1 +} + +event: compile_error +data: {"error": "llm_unavailable", "checkpoint_saved": true, "resume_job_id": "uuid"} +``` + +**`GET /diff/:v1/:v2`** +``` +Response: { + "from_version": "1.2.0", "to_version": "1.3.0", + "added_skills": [], "removed_skills": [], + "modified_skills": [ + { "id": "handle_refund_request", + "changes": [{"field": "decision_logic[1].action", + "from": "approve_prorated_refund", "to": "escalate_to_am"}] } + ] +} +``` + +**`POST /sources/upload`** +``` +Request: multipart/form-data: files[], company_id +Response: { "uploaded": ["notion_refund_sop.md"], "hashes": {"notion_refund_sop.md": "sha256:a1b2c3..."} } +``` + +--- + +## 8. AI System Requirements + +### 8.1 Tool & Model Requirements + +| Component | Tool / Model | Reason | +|---|---|---| +| All LLM extraction calls | `RedHatAI/Qwen2.5-72B-Instruct-FP8-dynamic` via vLLM | Best instruction following; FP8 = 1.5× throughput, ~72GB VRAM | +| Skill matching (v0) | `all-MiniLM-L6-v2` via `sentence-transformers` (in-memory, CPU) | Zero infra overhead; sufficient for 12 skills | +| Skill matching (v1) | pgvector on Supabase | Multi-tenant, persistent, scalable | +| LLM fallback | `Llama-3.3-70B BF16` | If Qwen2.5 unavailable on MI300X | +| Serving | vLLM on AMD MI300X (192GB VRAM) | Parallel batch inference, OpenAI-compatible API | + +### 8.2 Extraction Prompts — Requirements + +- All extractors demand: "Output ONLY structured JSON. Do not summarize. Do not generalize beyond what the text explicitly supports." +- All extractors include: output schema definition + 1-shot example in system prompt +- Temperature: 0.1 (deterministic extraction) +- Max tokens: 4096 per call + +### 8.3 Evaluation Strategy + +| Eval | Target | How to test | +|---|---|---| +| Brain agent correct action | 12 / 12 (100%) | Run all 12 scenarios against ground truth table | +| Evidence coverage | 100% of `decision_logic` entries have ≥ 1 `evidence_sources` | JSON schema validation post-compile | +| Contradiction recall | ≥ 2 contradictions flagged | Plant 2 deliberate contradictions in synthetic dataset | +| Confidence calibration | Well-sourced skills ≥ 0.7, single-source skills < 0.75 | Inspect post-compile | +| LLM JSON validity | 0 uncaught malformed responses in 10-run stress test | Run compile 10× on same dataset | +| "Without brain" failure rate | ≥ 8 of 12 scenarios produce generic/wrong response | Verify demo panel contrast is meaningful | + +--- + +## 9. Implementation Decisions + +**`with_brain: false` — Full Specification** + +When `POST /agent/handle` is called with `"with_brain": false`, the system prompt sent to Qwen2.5-72B contains ONLY this: + +``` +You are a helpful customer support assistant. +The customer says: {scenario} +{context if provided} +Respond appropriately. +``` + +No company name. No skills. No Rivanly. No hint that any compiled knowledge exists. The base model responds from its training data alone. This produces a generic, policy-free response — which is the "before" state that makes the "with brain" response look like magic. + +**Other key architectural decisions:** + +- `ingest_join` + `Send` API pattern required for correct LangGraph fan-in. Direct edges ingest→extract cause synthesis to fire before all extractors complete. +- `Annotated[List, operator.add]` on all extraction output fields required for parallel writes to merge correctly rather than overwrite. +- `await compiled_graph.ainvoke(initial_state)` — not `.invoke()` — in FastAPI background task. Without async, nodes block the event loop and parallelism is lost. +- Skills file is the only source of truth. The agent never reads raw source files at query time. +- `skills_files.is_current` enforced via partial unique index — only one row per company can be `true` at a time. +- `compile_runs` table is append-only. No updates. + +--- + +## 10. Technical Specifications + +### 10.1 Architecture Overview + +``` +FILE UPLOAD (Next.js) + │ + ▼ POST /sources/upload +FASTAPI API LAYER + │ + ▼ POST /compile → ainvoke +LANGGRAPH ENGINE (BrainState) + │ + ├── INGESTION (parallel, CPU) + │ ├── ingest_slack → normalized_events[] + │ ├── ingest_notion → structured_sops[] + │ └── ingest_tickets → resolved_cases[] + │ └── ingest_join (barrier) + │ + ├── EXTRACTION (parallel, async, AMD MI300X) + │ ├── extract_decisions → raw_decisions[] + │ ├── extract_workflows → workflow_steps[] + │ ├── extract_exceptions → exception_rules[] + │ └── detect_contradictions → contradictions[] + │ + └── COMPILATION + VALIDATION (sequential) + ├── synthesize_skills → draft_skills[] + ├── link_evidence → skills_with_evidence[] + ├── score_confidence → confidence per skill + └── write_brain → skills_file.json → Supabase + +BRAIN AGENT (query time) + POST /agent/handle + → sentence-transformers match → skill JSON → markdown + → single vLLM call → structured response JSON +``` + +### 10.2 Screen-to-Screen User Flow + +**Primary flow (Ops Owner compiling and testing for the first time):** + +``` +[Upload Sources page] + → Upload 3–8 files (drag + drop or file picker) + → See file list with SHA-256 hash status (new / unchanged / changed) + → Click "Done — Go to Dashboard" + ↓ +[Brain Dashboard] + → See: company name, current brain version (or "No brain yet"), last compiled timestamp + → See: source files uploaded (count) + → Click "Build Company Brain" button + ↓ +[SSE Progress overlay — renders in-place on Dashboard] + → Real-time: each node appears as it starts, gets checkmark when complete + → ingest_slack ✓ → ingest_notion ✓ → ingest_tickets ✓ → [join] + → extract_decisions ✓ (parallel) extract_workflows ✓ extract_exceptions ✓ detect_contradictions ✓ + → synthesize_skills ✓ → link_evidence ✓ → score_confidence ✓ → write_brain ✓ + → "Brain compiled: v1.3.0 in 58 seconds" + ↓ +[Brain Dashboard — updated state] + → Version badge updated: v1.3.0 + → Last compiled: just now + → 12 skills / 6 departments / 0 stale + → Click "View Skills" (or nav to Skills in sidebar) + ↓ +[Skills Viewer] + → 6 department groups, 12 skill cards + → Each card: name, confidence bar, stale badge (if applicable) + → Click any skill card → detail panel expands right + → Detail: all conditions, forbidden actions, escalation chain, evidence panel (source + excerpt) + → Click "Try a scenario" button (appears in detail panel) + ↓ +[Demo Agent Panel] + → Left panel: "Without Brain" — base model response (generic) + → Right panel: "With Brain" — rule trace + evidence + action + → Scenario input pre-filled from skill that was clicked (optional convenience) + → Submit → both panels render simultaneously + → Judge reads both — value is self-evident + → Click "What changed?" link (appears in top nav after ≥ 2 brain versions exist) + ↓ +[Version Diff View] + → Select v1 and v2 from dropdowns (pre-seeded with v1.1.0 and v1.2.0) + → See: modified skills (yellow), new skills (green), removed (red) + → Click modified skill → see field-level diff of changed conditions + → Click "← Back to Dashboard" (always accessible from nav) + ↓ +[Brain Dashboard] + → Modify a source file → re-upload → stale badge appears on affected skills + → Click "Build Company Brain" → recompile cycle repeats +``` + +**Critical path for demo (8-step script):** +Upload Sources → Dashboard → Build → SSE feed → Skills Viewer → Evidence panel → Demo Agent (side-by-side) → Change + Rebuild → Diff view + +**Navigation rules:** +- Sidebar always visible: Dashboard | Skills | Agent | Diff +- "Try a scenario" shortcut from Skills Viewer pre-fills the Agent panel's skill context +- "What changed?" link only appears when ≥ 2 brain versions exist (prevents confusion when first compiled) +- All pages accessible from nav at any time — no forced linear flow outside the demo script + +### 10.3 Integration Points + +| Integration | v0 | v1 | +|---|---|---| +| LLM | vLLM on AMD MI300X (private IP:8000) | Same + failover | +| Database | Supabase Postgres | Same + RLS per company | +| File storage | Supabase Storage | Same | +| Auth | None | Clerk | +| Queue | None (direct ainvoke) | Redis/Upstash | +| Connectors | File upload only | Slack OAuth, Notion API, Zendesk | +| Checkpointing | MemorySaver (in-memory) | PostgresSaver | + +### 10.4 Security & Privacy + +**v0 (hackathon):** All data is synthetic (Rivanly is fictional). No PII. vLLM on private AMD cloud IP. No RLS needed. + +**v1 (required before real customer data):** +- Clerk auth on all endpoints +- Supabase RLS: `company_id` row-level isolation +- vLLM behind VPC — not publicly accessible +- No customer message content stored permanently — only extracted rules and evidence excerpts + +--- + +## 11. Data Model (Supabase) + +```sql +CREATE TABLE companies ( + id TEXT PRIMARY KEY, + name TEXT NOT NULL, + created_at TIMESTAMPTZ DEFAULT now() +); + +CREATE TABLE skills_files ( + id UUID PRIMARY KEY DEFAULT gen_random_uuid(), + company_id TEXT REFERENCES companies(id), + version TEXT NOT NULL, + brain_json JSONB NOT NULL, + source_hashes JSONB NOT NULL, + compiled_at TIMESTAMPTZ DEFAULT now(), + is_current BOOLEAN DEFAULT false +); +CREATE UNIQUE INDEX idx_skills_files_current ON skills_files(company_id) WHERE is_current = true; + +CREATE TABLE skills ( + id TEXT NOT NULL, + company_id TEXT REFERENCES companies(id), + skills_file_id UUID REFERENCES skills_files(id), + name TEXT NOT NULL, + domain TEXT NOT NULL, + version TEXT NOT NULL, + confidence FLOAT NOT NULL, + stale BOOLEAN DEFAULT false, + review_required BOOLEAN DEFAULT false, + skill_json JSONB NOT NULL, + PRIMARY KEY (id, company_id, skills_file_id) +); + +CREATE TABLE source_files ( + id UUID PRIMARY KEY DEFAULT gen_random_uuid(), + company_id TEXT REFERENCES companies(id), + filename TEXT NOT NULL, + sha256 TEXT NOT NULL, + storage_path TEXT NOT NULL, + uploaded_at TIMESTAMPTZ DEFAULT now() +); + +CREATE TABLE compile_runs ( + id UUID PRIMARY KEY DEFAULT gen_random_uuid(), + company_id TEXT REFERENCES companies(id), + status TEXT NOT NULL CHECK (status IN ('started','running','complete','error')), + started_at TIMESTAMPTZ DEFAULT now(), + completed_at TIMESTAMPTZ, + duration_ms INTEGER, + result_version TEXT, + error_detail TEXT +); + +CREATE INDEX idx_skills_files_company ON skills_files(company_id, compiled_at DESC); +CREATE INDEX idx_skills_company ON skills(company_id); +``` + +--- + +## 12. Testing Decisions + +### Ground Truth Test Suite — All 12 Scenarios (COMPLETE) + +**Owner: Abhijith. Must be run and passing before demo day. All 12 must return correct `action`.** + +| # | Scenario | Key Context | Expected `action` | Expected `skill_matched` | +|---|---|---|---|---| +| 1 | Enterprise customer, 18 months tenure, $1,200 refund requested | plan=enterprise, tenure=18mo, amount=1200 | `escalate_to_am_within_1hr` | `handle_refund_request` | +| 2 | Annual plan customer, day 10 of subscription, $300 refund requested | plan=annual, days_since_purchase=10, amount=300 | `approve_full_refund` | `handle_refund_request` | +| 3 | New customer, 2 months tenure, $600 refund requested | plan=monthly, tenure=2mo, amount=600 | `escalate_to_founder` | `handle_refund_request` | +| 4 | Loyal annual customer, 14 months tenure, $150 refund outside window | plan=annual, tenure=14mo, amount=150 | `approve_prorated_refund` | `handle_refund_request` | +| 5 | Lifetime deal customer requesting any refund | plan=lifetime, amount=any | `deny_refund_ltd_terms` | `handle_refund_request` | +| 6 | Customer contact during active platform outage | context=outage_active | `send_incident_response_template` | `respond_to_outage` | +| 7 | Startup customer requesting 40% discount | customer_type=startup, discount_requested=40% | `escalate_to_ae` | `evaluate_discount_request` | +| 8 | P0 bug reported on dashboard module by enterprise customer | bug_severity=P0, customer_plan=enterprise | `page_oncall_engineer_immediately` | `prioritize_bug_report` | +| 9 | Customer SLA breached by 2 hours, enterprise plan | sla_breach_hours=2, plan=enterprise | `notify_am_and_eng_lead` | `handle_sla_breach` | +| 10 | Customer showing 3 churn signals in last 30 days (no login, support ticket, downgrade inquiry) | signals=3, timeframe=30days | `schedule_am_call_within_24h` | `evaluate_churn_risk` | +| 11 | Engineering candidate — completed 2 rounds, needs offer approval | stage=offer, role=engineer | `get_founder_approval_before_sending` | `hiring_process_engineering` | +| 12 | Vendor invoice for $3,500 needs payment approval | amount=3500, vendor_type=software | `route_to_ops_lead_approval` | `approve_vendor_payment` | + +**How to run:** +```python +# Run before demo. All 12 must pass. +for scenario in GROUND_TRUTH_SCENARIOS: + response = client.post("/agent/handle", json=scenario["input"]) + assert response.json()["action"] == scenario["expected_action"] + assert response.json()["skill_matched"] == scenario["expected_skill"] +``` + +### Module Test Matrix + +| Module | Test Type | What to Test | +|---|---|---| +| Source parsers | Unit | Given raw fixture file → correct normalized output shape | +| SHA-256 hasher | Unit | Same content → same hash; changed content → different hash | +| Skill matcher | Unit | Given 12 known queries → each returns correct `skill_id` | +| JSON→Markdown converter | Unit | Given skill object → output contains all conditions and forbidden actions, under 800 tokens | +| `POST /compile` | Integration | Returns `job_id` and `stream_url`; sets compile_run status to "started" | +| `GET /skills` | Integration | Returns exactly 12 skills for Rivanly | +| `POST /agent/handle` | Integration | All 12 ground-truth scenarios return correct `action` | +| `GET /diff/:v1/:v2` | Integration | Pre-seeded v1.1.0 and v1.2.0 → returns expected `modified_skills` | +| Full pipeline | End-to-end | 8 source files → 12 skills in Supabase, `is_current: true`, all with evidence | +| LLM output | Eval | 10-run stress test → zero uncaught malformed JSON | + +--- + +## 13. Non-Functional Requirements + +- Full compilation: under 90 seconds (target: 60s) +- Brain agent response: under 8 seconds +- SSE feed: real-time node events, no polling +- Skill matching: under 200ms (in-memory cosine similarity) +- LangGraph MemorySaver checkpointing: compile state survives crash +- Fallback model: Llama-3.3-70B BF16 if Qwen2.5 unavailable +- vLLM health check queried before accepting `/compile` requests + +--- + +## 14. Success Metrics + +### Hackathon v0 — Measurable Targets + +| Metric | Target | Verification | +|---|---|---| +| End-to-end pipeline | Completes without error | Run 3× in final 2 hours | +| Skills produced | Exactly 12 | Check `skills_file.json` | +| Skills with confidence ≥ 0.7 | ≥ 10 of 12 | Check confidence field | +| Agent correct action | 12 / 12 | Run ground truth suite | +| Agent latency | < 8 seconds | Time on demo day | +| Compilation time | < 90 seconds | Dashboard display | +| Live URL accessible | Yes | Test on fresh device before submission | +| Demo video submitted | Yes | Render early, keep backup | +| Public posts | 2 minimum | During hours 8–16 and 16–28 | + +### The 8-Step Demo — Ring 1 Acceptance Test + +1. Show source files — "Rivanly's scattered knowledge." +2. Click "Build Company Brain" — watch SSE feed in real time. +3. Show compilation time — "12 skills in 58 seconds on AMD MI300X." +4. Open Skills Viewer — 6 departments, 12 skills, confidence bars. +5. Click `handle_refund_request` — show evidence panel. +6. Submit enterprise refund scenario to agent panel. +7. Show side-by-side: without brain (generic) vs. with brain (rule trace + evidence). +8. Change one SOP rule → Rebuild → same scenario → different outcome. **This is the moment.** + +### Post-Hackathon Business Metrics (v1) + +- 3 paying pilot customers within 60 days of v1 launch +- Activation: first brain compiled + agent handles 1 scenario correctly +- Retention: brain recompiled at least once within 30 days +- Revenue: $200/month Starter, $500/month Growth + +--- + +## 15. Competitive Landscape + +*Updated with companies identified in YC/LinkedIn Company Brain thread.* + +| Company | What they do | Differentiation | +|---|---|---| +| **Notion AI** | Q&A over documents | Retrieves chunks, doesn't compile operational judgment | +| **Guru / Confluence** | Knowledge base search | Human-maintained, not executable by AI agents | +| **Glean** | Enterprise search | Search-first, not compilation; no executable output | +| **Sugarwork** (sugarwork.com) | Surfaces tacit knowledge for AI | Adjacent; watch closely | +| **BrandOS** (getbrandos.site) | Company brain for marketing teams | Vertical-specific; not full company coverage | +| **Context AI** | Operational knowledge for agents | Direct competitor — monitor | +| **LineageOne** (NEXT'26) | Fragmented operations → live operational model | Direct competitor | +| **AutoBase** | Building this for 7 months | Direct competitor | +| **Company Brain** | Full compilation layer, all departments, versioned, evidence-linked | Evidence trail, stale detection, parallel AMD compilation | + +**Observation:** Multiple teams are building in this space. This validates the market. The race is to who ships the most complete, demo-able, production-credible version. Company Brain's differentiator is the combination of: evidence-linked rules (not just structured outputs), stale detection, version diffing, and the clean "compiler not assistant" framing that competitors haven't articulated. + +--- + +## 16. Risks & Mitigation + +| Risk | Likelihood | Mitigation | +|---|---|---| +| Knowledge that was never captured cannot be extracted | **High — acknowledged** | Scope v0 to knowledge that exists in digital form; call out in pitch as known limitation; v1 adds call transcription | +| Extraction agents produce low-quality skills | Medium | Dataset authored backward from desired output; eval suite catches failures before demo | +| vLLM setup on AMD cloud takes too long | Low | Kubernetes on AMD course completed; fallback to Fireworks API | +| LangGraph parallel fan-in bug | Low | Fixed using `Send` API + `ingest_join` barrier node | +| Demo breaks during judging | Medium | Pre-recorded fallback video; deploy to stable URL 24h before submission | +| Qwen2.5-72B FP8 unavailable | Low | `RedHatAI/Qwen2.5-72B-Instruct-FP8-dynamic` confirmed on HuggingFace | +| Frontend/backend API contract mismatch | Medium | Both parties agree on F-12 schemas before writing frontend code | +| Synthetic dataset too shallow | Medium | Each file: ≥ 4 edge cases, ≥ 1 planted contradiction; reviewed together before kickoff | +| Competitors ship demo before May 11 | Low | Multiple are building but none have shipped a demo yet; Company Brain's AMD + parallel compile angle is unique | + +--- + +## 17. v2 Roadmap — Insights from LinkedIn Thread + +*Insights from practitioners who responded to Tom Blomfield's YC RFS post that should inform v2 product decisions.* + +**Execution boundaries (Horizon Labs insight):** The skills file is currently advisory — the agent reads it and acts. In v2, the skills file should become constraining — the agent should not be able to take actions not in the admissible action set. This is the difference between a knowledge map and an execution boundary. Add to v2: `forbidden_actions` enforced at the runtime level, not just injected as prompt guidance. + +**The stale knowledge divergence problem (Matan Elmalam insight):** Teams build the map once, ship the agent, and within six weeks reality diverges. Our stale detection addresses this for captured knowledge. For v2: active monitoring — compare agent actions against skills file weekly and surface divergences as "possible new skills" for human review. + +**Call transcription (Paul Breuler gap):** Knowledge that exists only in spoken conversations will never be in Slack or Notion. In v2: integrate with Fireflies/Otter/Grain to pull meeting transcripts as a first-class source type. This closes the most common knowledge capture gap. + +**Audit trail (Josh Jefferd insight):** Every agent action should be logged with which skill rule was applied and which evidence excerpt justified it. This is the compliance and trust layer. Add to v2 roadmap as a first-class feature, not an afterthought. + +--- + +## 18. Open Questions — All Resolved + +| Question | Resolution | +|---|---| +| Who owns frontend vs. pipeline? | Abhijith = pipeline + API. Harshit = all frontend. | +| Supabase schema? | Defined in Section 11. | +| SSE disconnect/reconnect handling? | Frontend: exponential backoff (1s, 2s, 4s). Fallback: `GET /brain/status` for final state. | +| Synthetic dataset ownership? | Both — 4 files each, authored before May 4 kickoff. | +| Ground truth table complete? | Yes — all 12 scenarios in Section 12. Run before demo. | +| `with_brain: false` behaviour? | Fully specified in F-10 and Section 9. | +| Screen-to-screen user flow? | Defined in Section 10.2. | + +--- + +*This document supersedes company_brain_PRD_v3.md. All three audit issues resolved. Competitive landscape updated with real companies from LinkedIn thread. No scope changes after May 4 kickoff.* diff --git a/data/sources/rivanly-inc/notion_cs_playbook.md b/data/sources/rivanly-inc/notion_cs_playbook.md new file mode 100644 index 0000000000000000000000000000000000000000..a3f479cb2e82294ba1bdcaea38a37404f944f612 --- /dev/null +++ b/data/sources/rivanly-inc/notion_cs_playbook.md @@ -0,0 +1,10 @@ +# Customer Success Playbook +**Department:** Customer Success +**Last Updated:** May 2026 + +## 1. Churn Risk Evaluation +It is critical to identify and intervene when accounts show signs of churning. +- **Rule:** If a customer exhibits 3 or more churn signals (e.g., no logins, support ticket escalations, downgrade inquiries) within a 30-day timeframe, you must schedule an AM call within 24 hours. + +## 2. Enterprise Onboarding +- **Rule:** For all new Enterprise customers, the onboarding process must include a dedicated kickoff call, a customized training session, and a 30-day check-in. diff --git a/data/sources/rivanly-inc/notion_eng_runbook.md b/data/sources/rivanly-inc/notion_eng_runbook.md new file mode 100644 index 0000000000000000000000000000000000000000..fea7a4bf309d847ccaa41995fd25d5ee05ffb1e6 --- /dev/null +++ b/data/sources/rivanly-inc/notion_eng_runbook.md @@ -0,0 +1,17 @@ +# Engineering Runbook & SLAs +**Department:** Product & Engineering +**Last Updated:** February 2026 + +## 1. Bug Triage +- **P0 (Critical):** System down, data loss, or core workflow broken. + - **Rule:** If a P0 bug is reported by an Enterprise customer, page the on-call engineer immediately. +- **P1 (High):** Major feature broken but workaround exists. + - **Rule:** P1 bugs must be resolved within 4 hours. +- **P2 (Medium/Low):** UI glitches, minor inconveniences. Add to the backlog. + +## 2. SLA Breach Handling +- **Standard Process:** If a customer SLA is breached by more than 1 hour, notify the support lead. +- **Enterprise Exceptions:** If an Enterprise plan customer SLA is breached by 2 hours or more, you must notify both the Account Manager and the Engineering Lead immediately. + +## 3. Outage Response +- If a customer contacts support during an active platform outage, do not troubleshoot. Send the standard incident response template and link to the status page. diff --git a/data/sources/rivanly-inc/notion_hr_playbook.md b/data/sources/rivanly-inc/notion_hr_playbook.md new file mode 100644 index 0000000000000000000000000000000000000000..b895e7636ae204741b13e2048832be8b5aaefd36 --- /dev/null +++ b/data/sources/rivanly-inc/notion_hr_playbook.md @@ -0,0 +1,17 @@ +# HR & Hiring Playbook +**Department:** HR +**Last Updated:** January 2026 + +## 1. Engineering Hiring Process +The standard hiring process for Engineering roles: +1. Recruiter Screen +2. Technical Interview (Pair Programming) +3. Systems Design Interview +4. Culture Fit with Founders +5. Offer Stage + +**Critical Rule:** For any engineering candidate at the offer stage, you must get Founder approval before sending the final offer letter. + +## 2. Performance & PIPs +- A Performance Improvement Plan (PIP) is triggered if an employee misses their core KPIs for two consecutive quarters. +- If an employee is placed on a PIP, HR must schedule a formal review with the department head within 5 business days. diff --git a/data/sources/rivanly-inc/notion_pricing_policy.md b/data/sources/rivanly-inc/notion_pricing_policy.md new file mode 100644 index 0000000000000000000000000000000000000000..0a3a2bbb47cc0f75bbd090d1caa982cbbb4b3440 --- /dev/null +++ b/data/sources/rivanly-inc/notion_pricing_policy.md @@ -0,0 +1,14 @@ +# Pricing & Discount Policy +**Department:** Revenue +**Last Updated:** April 2026 + +## 1. Overview +This document outlines standard pricing exceptions and discount approval chains. + +## 2. Discount Authority +- **Standard Discount:** Support and CS can apply up to a 10% discount to save a churning customer. +- **Startup Discount:** If a customer identifies as an early-stage startup (pre-seed or seed), you may approve up to a 20% discount on the Annual plan for the first year. +- **Large Discounts:** If a customer requests a discount greater than 30%, it must be escalated to an Account Executive (AE) for approval. Support cannot approve this. + +## 3. Custom Pricing +- **Enterprise Custom Pricing:** Enterprise customers requesting custom feature bundles or volume-based pricing must be routed to the VP of Sales. diff --git a/data/sources/rivanly-inc/notion_refund_sop.md b/data/sources/rivanly-inc/notion_refund_sop.md new file mode 100644 index 0000000000000000000000000000000000000000..63c4a5ddc66859dbc32d718910afc464f6e6a7aa --- /dev/null +++ b/data/sources/rivanly-inc/notion_refund_sop.md @@ -0,0 +1,16 @@ +# Refund Standard Operating Procedure (SOP) +**Department:** Support +**Last Updated:** March 2026 + +## 1. Core Policy +Our refund policy is designed to balance customer satisfaction with revenue retention. Always aim to understand the root cause before processing a refund. + +## 2. Refund Eligibility & Rules +- **Annual Plans (First 14 days):** If a customer on an annual plan requests a refund within the first 14 days of purchase, approve a full refund immediately. No questions asked. +- **Annual Plans (After 14 days):** If a customer on an annual plan requests a refund after 14 days, approve a prorated refund for the remaining unused months. +- **Enterprise Customers:** If any Enterprise customer requests a refund of any amount, DO NOT process it immediately. You must escalate to the Account Manager (AM) within 1 hour. +- **Lifetime Deals (LTD):** Under no circumstances do we process refunds for lifetime deal accounts. Deny the request citing LTD terms. +- **Monthly Plans (New Customers):** If a customer on a monthly plan with a tenure of less than 3 months requests a refund over $500, escalate to the Founder. + +## 3. Strict Time Limits +**CRITICAL:** We offer absolutely no refunds after 30 days of purchase for any customer tier. If the purchase was more than 30 days ago, deny the refund. diff --git a/data/sources/rivanly-inc/slack_export_ops.json b/data/sources/rivanly-inc/slack_export_ops.json new file mode 100644 index 0000000000000000000000000000000000000000..18fff6a432fbeae4b106fe84941a5c6db14ce1bf --- /dev/null +++ b/data/sources/rivanly-inc/slack_export_ops.json @@ -0,0 +1,20 @@ +[ + { + "channel": "finance-ops", + "user": "jessica_fin", + "text": "I have an incoming vendor invoice for $3,500 from Datadog (software vendor). Who needs to approve this?", + "timestamp": "2026-02-15T09:30:00Z" + }, + { + "channel": "finance-ops", + "user": "david_ops_lead", + "text": "Any software vendor invoice of $3,500 or more needs to be routed to the ops lead for approval before finance pays it. Send it my way.", + "timestamp": "2026-02-15T09:45:00Z" + }, + { + "channel": "finance-ops", + "user": "jessica_fin", + "text": "Got it, routing it to you.", + "timestamp": "2026-02-15T09:46:00Z" + } +] diff --git a/data/sources/rivanly-inc/slack_export_support.json b/data/sources/rivanly-inc/slack_export_support.json new file mode 100644 index 0000000000000000000000000000000000000000..c219f81b09c5ad8217a8913a24a5c2084b39902d --- /dev/null +++ b/data/sources/rivanly-inc/slack_export_support.json @@ -0,0 +1,26 @@ +[ + { + "channel": "support-triage", + "user": "sarah_am", + "text": "Hey team, Acme Corp (been with us 4 years) is asking for a refund for last month's invoice due to the billing mixup. It's been 45 days since that charge, I know SOP says 30 days max.", + "timestamp": "2026-03-12T10:00:00Z" + }, + { + "channel": "support-triage", + "user": "mike_lead", + "text": "For loyal customers over 2 years tenure, we can bypass the 30-day rule. Go ahead and approve the refund for Acme Corp.", + "timestamp": "2026-03-12T10:05:00Z" + }, + { + "channel": "support-triage", + "user": "alex_support", + "text": "We have an active platform outage affecting all EU servers. Customers are opening tickets left and right.", + "timestamp": "2026-04-01T14:20:00Z" + }, + { + "channel": "support-triage", + "user": "mike_lead", + "text": "Do not try to troubleshoot individual EU tickets right now. Just send the incident response template and close the tickets.", + "timestamp": "2026-04-01T14:22:00Z" + } +] diff --git a/data/sources/rivanly-inc/zendesk_tickets.json b/data/sources/rivanly-inc/zendesk_tickets.json new file mode 100644 index 0000000000000000000000000000000000000000..065da099e38ff027707c4ee8d420432c41bafa18 --- /dev/null +++ b/data/sources/rivanly-inc/zendesk_tickets.json @@ -0,0 +1,23 @@ +[ + { + "id": "TICKET-1042", + "subject": "Dashboard not loading", + "description": "I cannot access the main dashboard. It just spins forever.", + "resolution": "P0 bug confirmed. Paged on-call engineer immediately as this is an Enterprise customer.", + "tags": ["bug", "P0", "enterprise"] + }, + { + "id": "TICKET-1045", + "subject": "Export feature failing", + "description": "When I try to export reports to CSV, it fails.", + "resolution": "Confirmed P1 bug for Enterprise customer GlobalCorp. Escalated to Eng and resolved same-day (within 12 hours), outside the normal 4-hour SLA but acceptable for this specific complex issue for Enterprise.", + "tags": ["bug", "P1", "enterprise"] + }, + { + "id": "TICKET-1088", + "subject": "SLA breached on ticket 1087", + "description": "We have been waiting 6 hours for a response.", + "resolution": "SLA breached by 2 hours for Enterprise customer. Notified AM and Eng Lead immediately.", + "tags": ["sla_breach", "enterprise"] + } +] diff --git a/frontend/.gitignore b/frontend/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..5ef6a520780202a1d6addd833d800ccb1ecac0bb --- /dev/null +++ b/frontend/.gitignore @@ -0,0 +1,41 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +# dependencies +/node_modules +/.pnp +.pnp.* +.yarn/* +!.yarn/patches +!.yarn/plugins +!.yarn/releases +!.yarn/versions + +# testing +/coverage + +# next.js +/.next/ +/out/ + +# production +/build + +# misc +.DS_Store +*.pem + +# debug +npm-debug.log* +yarn-debug.log* +yarn-error.log* +.pnpm-debug.log* + +# env files (can opt-in for committing if needed) +.env* + +# vercel +.vercel + +# typescript +*.tsbuildinfo +next-env.d.ts diff --git a/frontend/AGENTS.md b/frontend/AGENTS.md new file mode 100644 index 0000000000000000000000000000000000000000..8bd0e39085d5260e7f8faffcad2fdc45e10aef33 --- /dev/null +++ b/frontend/AGENTS.md @@ -0,0 +1,5 @@ + +# This is NOT the Next.js you know + +This version has breaking changes — APIs, conventions, and file structure may all differ from your training data. Read the relevant guide in `node_modules/next/dist/docs/` before writing any code. Heed deprecation notices. + diff --git a/frontend/CLAUDE.md b/frontend/CLAUDE.md new file mode 100644 index 0000000000000000000000000000000000000000..43c994c2d3617f947bcb5adf1933e21dabe46bb5 --- /dev/null +++ b/frontend/CLAUDE.md @@ -0,0 +1 @@ +@AGENTS.md diff --git a/frontend/README.md b/frontend/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e215bc4ccf138bbc38ad58ad57e92135484b3c0f --- /dev/null +++ b/frontend/README.md @@ -0,0 +1,36 @@ +This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app). + +## Getting Started + +First, run the development server: + +```bash +npm run dev +# or +yarn dev +# or +pnpm dev +# or +bun dev +``` + +Open [http://localhost:3000](http://localhost:3000) with your browser to see the result. + +You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file. + +This project uses [`next/font`](https://nextjs.org/docs/app/building-your-application/optimizing/fonts) to automatically optimize and load [Geist](https://vercel.com/font), a new font family for Vercel. + +## Learn More + +To learn more about Next.js, take a look at the following resources: + +- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API. +- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial. + +You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js) - your feedback and contributions are welcome! + +## Deploy on Vercel + +The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js. + +Check out our [Next.js deployment documentation](https://nextjs.org/docs/app/building-your-application/deploying) for more details. diff --git a/frontend/eslint.config.mjs b/frontend/eslint.config.mjs new file mode 100644 index 0000000000000000000000000000000000000000..05e726d1b4201bc8c7716d2b058279676582e8c0 --- /dev/null +++ b/frontend/eslint.config.mjs @@ -0,0 +1,18 @@ +import { defineConfig, globalIgnores } from "eslint/config"; +import nextVitals from "eslint-config-next/core-web-vitals"; +import nextTs from "eslint-config-next/typescript"; + +const eslintConfig = defineConfig([ + ...nextVitals, + ...nextTs, + // Override default ignores of eslint-config-next. + globalIgnores([ + // Default ignores of eslint-config-next: + ".next/**", + "out/**", + "build/**", + "next-env.d.ts", + ]), +]); + +export default eslintConfig; diff --git a/frontend/next.config.ts b/frontend/next.config.ts new file mode 100644 index 0000000000000000000000000000000000000000..e9ffa3083ad279ecf95fd8eae59cb253e9a539c4 --- /dev/null +++ b/frontend/next.config.ts @@ -0,0 +1,7 @@ +import type { NextConfig } from "next"; + +const nextConfig: NextConfig = { + /* config options here */ +}; + +export default nextConfig; diff --git a/frontend/package-lock.json b/frontend/package-lock.json new file mode 100644 index 0000000000000000000000000000000000000000..39877dc9ce57cfddee5c790aaf9203e3336bfbfb --- /dev/null +++ b/frontend/package-lock.json @@ -0,0 +1,6585 @@ +{ + "name": "frontend", + "version": "0.1.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "frontend", + "version": "0.1.0", + "dependencies": { + "next": "16.2.5", + "react": "19.2.4", + "react-dom": "19.2.4" + }, + "devDependencies": { + "@tailwindcss/postcss": "^4", + "@types/node": "^20", + "@types/react": "^19", + "@types/react-dom": "^19", + "eslint": "^9", + "eslint-config-next": "16.2.5", + "tailwindcss": "^4", + "typescript": "^5" + } + }, + "node_modules/@alloc/quick-lru": { + "version": "5.2.0", + "resolved": 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"frontend", + "version": "0.1.0", + "private": true, + "scripts": { + "dev": "next dev", + "build": "next build", + "start": "next start", + "lint": "eslint" + }, + "dependencies": { + "next": "16.2.5", + "react": "19.2.4", + "react-dom": "19.2.4" + }, + "devDependencies": { + "@tailwindcss/postcss": "^4", + "@types/node": "^20", + "@types/react": "^19", + "@types/react-dom": "^19", + "eslint": "^9", + "eslint-config-next": "16.2.5", + "tailwindcss": "^4", + "typescript": "^5" + } +} diff --git a/frontend/postcss.config.mjs b/frontend/postcss.config.mjs new file mode 100644 index 0000000000000000000000000000000000000000..61e36849cf7cfa9f1f71b4a3964a4953e3e243d3 --- /dev/null +++ b/frontend/postcss.config.mjs @@ -0,0 +1,7 @@ +const config = { + plugins: { + "@tailwindcss/postcss": {}, + }, +}; + +export default config; diff --git a/frontend/public/file.svg b/frontend/public/file.svg new file mode 100644 index 0000000000000000000000000000000000000000..004145cddf3f9db91b57b9cb596683c8eb420862 --- /dev/null +++ b/frontend/public/file.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/public/globe.svg b/frontend/public/globe.svg new file mode 100644 index 0000000000000000000000000000000000000000..567f17b0d7c7fb662c16d4357dd74830caf2dccb --- /dev/null +++ b/frontend/public/globe.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/public/next.svg b/frontend/public/next.svg new file mode 100644 index 0000000000000000000000000000000000000000..5174b28c565c285e3e312ec5178be64fbeca8398 --- /dev/null +++ b/frontend/public/next.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/public/vercel.svg b/frontend/public/vercel.svg new file mode 100644 index 0000000000000000000000000000000000000000..77053960334e2e34dc584dea8019925c3b4ccca9 --- /dev/null +++ b/frontend/public/vercel.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/public/window.svg b/frontend/public/window.svg new file mode 100644 index 0000000000000000000000000000000000000000..b2b2a44f6ebc70c450043c05a002e7a93ba5d651 --- /dev/null +++ b/frontend/public/window.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/app/compile/[jobId]/page.tsx b/frontend/src/app/compile/[jobId]/page.tsx new file mode 100644 index 0000000000000000000000000000000000000000..82358c33457e6525dbfdca2d7daabf2ac7bdb7ff --- /dev/null +++ b/frontend/src/app/compile/[jobId]/page.tsx @@ -0,0 +1,115 @@ +"use client"; + +import { useEffect, useState, use } from "react"; +import { useRouter } from "next/navigation"; + +interface LogEvent { + timestamp: string; + type: string; + data: any; +} + +const STAGE_LABELS: Record = { + pipeline_start: "🚀 Pipeline Started", + LOADING_DOCS: "📂 Loading Documents", + CHUNKING: "✂️ Chunking Documents", + CHUNKING_DONE: "✅ Chunking Complete", + EMBEDDING: "🧠 Embedding & Clustering", + EMBEDDING_DONE: "✅ Clustering Complete", + SYNTHESIZING_SKILLS: "⚡ Synthesizing Skills", + QUALITY_CHECK: "🔍 Quality & Confidence Scoring", + QUALITY_CHECK_DONE: "✅ Quality Check Complete", + WRITING_DB: "💾 Writing to Database", + DONE: "✅ Pipeline Complete", + pipeline_complete: "🎉 Compilation Finished", + pipeline_error: "❌ Pipeline Error", +}; + +export default function CompileViewer({ params }: { params: Promise<{ jobId: string }> }) { + const resolvedParams = use(params); + const jobId = resolvedParams.jobId; + const [logs, setLogs] = useState([]); + const [status, setStatus] = useState("Connecting..."); + const router = useRouter(); + + useEffect(() => { + if (!jobId) return; + + const eventSource = new EventSource(`http://localhost:8080/compile/${jobId}/stream`); + + eventSource.onmessage = (event) => { + const parsed = JSON.parse(event.data); + const eventType = parsed.event; + const eventData = parsed.data; + + setLogs((prev) => [ + ...prev, + { timestamp: new Date().toLocaleTimeString(), type: eventType, data: eventData }, + ]); + + // Update the status bar based on event type + if (eventType === "stage") { + const stageName = eventData.name || ""; + const label = STAGE_LABELS[stageName] || stageName; + const detail = eventData.detail || ""; + setStatus(`${label}${detail ? ` — ${detail}` : ""}`); + } else if (eventType === "pipeline_start") { + setStatus(STAGE_LABELS.pipeline_start); + } else if (eventType === "pipeline_complete") { + setStatus(STAGE_LABELS.pipeline_complete); + eventSource.close(); + } else if (eventType === "pipeline_error") { + setStatus(`❌ Error: ${eventData.error || "Unknown"}`); + eventSource.close(); + } + }; + + eventSource.onerror = () => { + eventSource.close(); + }; + + return () => eventSource.close(); + }, [jobId]); + + return ( +
+
+

Pipeline Stream

+
+ + {status} + + +
+
+ +
+ {logs.map((log, i) => { + const isStage = log.type === "stage"; + const stageName = isStage ? log.data?.name : log.type; + const label = STAGE_LABELS[stageName] || stageName; + const detail = isStage ? log.data?.detail || "" : JSON.stringify(log.data); + const isError = stageName?.includes("error") || stageName?.includes("Error"); + + return ( +
+ [{log.timestamp}]{" "} + {label}{" "} + {detail} +
+ ); + })} +
+
+ ); +} diff --git a/frontend/src/app/demo/[companyId]/page.tsx b/frontend/src/app/demo/[companyId]/page.tsx new file mode 100644 index 0000000000000000000000000000000000000000..9fe5501f02c33d781c4ca67bfd48bd3297af98da --- /dev/null +++ b/frontend/src/app/demo/[companyId]/page.tsx @@ -0,0 +1,269 @@ +"use client"; + +import { useState, use } from "react"; +import { useRouter } from "next/navigation"; + +type AgentResponse = { + recommended_action?: string; + rule_applied?: string; + evidence?: string[]; + skill_matched?: string; + confidence?: number; + retrieval_scores?: number[]; + reasoning?: string; + error?: string; +}; + +export default function QueryDemo({ params }: { params: Promise<{ companyId: string }> }) { + const resolvedParams = use(params); + const companyId = resolvedParams.companyId; + const [scenario, setScenario] = useState(""); + const [contextJson, setContextJson] = useState("{}"); + const [loading, setLoading] = useState(false); + + const [withBrainResponse, setWithBrainResponse] = useState(null); + const [withoutBrainResponse, setWithoutBrainResponse] = useState(null); + + const router = useRouter(); + + const handleQuery = async (e: React.FormEvent) => { + e.preventDefault(); + if (!scenario) return; + setLoading(true); + setWithBrainResponse(null); + setWithoutBrainResponse(null); + + let parsedContext = {}; + try { + if (contextJson.trim()) { + parsedContext = JSON.parse(contextJson); + } + } catch { + alert("Invalid JSON in context field"); + setLoading(false); + return; + } + + try { + const [resWithBrain, resWithoutBrain] = await Promise.all([ + fetch("http://localhost:8080/agent/handle", { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ company_id: companyId, scenario, context: parsedContext, with_brain: true }), + }), + fetch("http://localhost:8080/agent/handle", { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ company_id: companyId, scenario, context: parsedContext, with_brain: false }), + }), + ]); + + setWithBrainResponse(await resWithBrain.json()); + setWithoutBrainResponse(await resWithoutBrain.json()); + } catch (err) { + console.error(err); + alert("Query failed — is the backend running?"); + } finally { + setLoading(false); + } + }; + + const confidenceColor = (c: number) => { + if (c >= 0.75) return "bg-green-500"; + if (c >= 0.5) return "bg-yellow-500"; + if (c >= 0.25) return "bg-orange-500"; + return "bg-red-500"; + }; + + return ( +
+
+
+

Brain Query Demo

+ +
+ +
+
+
+ +