Add presentation_creation.md — full brief for 6 PPT/PDF variants

presentation_creation.md

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+# TenderIQ — Presentation Creation Brief
+
+> **Purpose of this file:** Give a fresh Claude context everything it needs to generate
+> 5–6 distinct, high-quality presentations (PPT and/or PDF) for the CRPF Hackathon
+> submission. The creator should produce all variants in one session so the user can
+> pick the best one.
+
+---
+
+## 0. How to use this file
+
+1. Read sections 1–5 carefully — they contain all project context, slide content,
+   and data.
+2. Read section 6 — it defines exactly 6 visual styles to produce.
+3. Read section 7 — it gives technical guidance for python-pptx and reportlab.
+4. Produce all 6 variants, saving them to `deck/` as:
+   - `deck/TenderIQ_v1_dark_professional.pptx`
+   - `deck/TenderIQ_v2_clean_minimal.pptx`
+   - `deck/TenderIQ_v3_government_official.pptx`
+   - `deck/TenderIQ_v4_modern_gradient.pdf`
+   - `deck/TenderIQ_v5_data_forward.pptx`
+   - `deck/TenderIQ_v6_infographic.pdf`
+5. Each variant uses the **same slide content** (section 4) but different visual
+   treatment (section 6). Do not cut content between variants.
+
+**DO NOT** reuse the existing `deck/TenderIQ_Pitch.pdf` — it was generated by a
+previous low-quality script and should be ignored entirely.
+
+---
+
+## 1. Project Summary
+
+**Name:** TenderIQ  
+**Tagline:** Explainable AI for Government Tender Evaluation  
+**Event:** CRPF Hackathon — Theme 3: AI-Based Tender Evaluation and Eligibility
+Analysis for Government Procurement  
+**Organisation:** Central Reserve Police Force, Ministry of Home Affairs,
+Government of India  
+
+**One paragraph description:**
+TenderIQ automates the eligibility evaluation of bidders against government tender
+criteria. A procurement officer uploads a tender PDF; the system extracts each
+eligibility criterion using an LLM, processes bidder documents through a three-tier
+OCR pipeline (handling everything from typed PDFs to blurry scanned certificates),
+evaluates each bidder against each criterion with combined confidence scoring, and
+surfaces ambiguous cases for human review — all with a complete, exportable audit
+trail. The app is built on Streamlit and is deployable to a public URL in minutes.
+
+---
+
+## 2. The Problem (use these facts on the problem slide)
+
+- A procurement committee manually reading tender documents and bidder submissions
+  can spend **3–5 days per tender**
+- Two evaluators reviewing the same bid **regularly reach different conclusions**
+- Documents arrive in **mixed formats**: typed PDFs, scanned certificates,
+  photographs of documents taken on phones
+- There is **no consistent audit trail** — decisions cannot be traced to specific
+  evidence
+- Government procurement is worth **₹50 lakh crore+ annually** in India
+- Manual evaluation is a bottleneck that **delays project execution**
+
+---
+
+## 3. Key Differentiators (highlight these)
+
+1. **Three-tier OCR robustness** — most systems assume digital text; TenderIQ
+   handles scanned and photographed documents via a progressive pipeline:
+   PyMuPDF (typed PDF, instant) → Tesseract OCR (scans) → DeepSeek Vision LLM
+   (low-confidence scans, ~95% accuracy). Every page records which tier read it.
+
+2. **Never silent disqualification** — the safety rule: if combined confidence is
+   between 0.55 and 0.80 and the verdict is `not_eligible`, it is automatically
+   downgraded to `needs_review`. A bidder is never automatically disqualified at
+   medium confidence.
+
+3. **Criterion-level explainability** — every verdict is traceable to a specific
+   document, page number, OCR tier, extracted value, and plain-English reason.
+   Not just "pass/fail" — the officer can see exactly why.
+
+4. **Complete audit trail** — every action (extraction, OCR invocation, evaluation,
+   human review) is logged with timestamp, model version, actor, and payload to
+   SQLite. Exportable as CSV.
+
+5. **Works without internet** — pre-computed fallback JSON is shipped with the
+   repo. If the API goes down during a demo, the app continues seamlessly. Sidebar
+   turns amber to indicate fallback mode.
+
+---
+
+## 4. Slide Content (use exactly this for all 6 variants)
+
+### SLIDE 1 — Title Slide
+- **Main title:** TenderIQ
+- **Subtitle:** Explainable AI for Government Tender Evaluation
+- **Event line:** CRPF Hackathon · Theme 3
+- **Tagline (small):** From days to minutes. Every decision traceable.
+- **Visual suggestion:** The ⚖️ scales emoji large, or an abstract representation
+  of documents flowing through a pipeline
+
+---
+
+### SLIDE 2 — The Problem
+- **Title:** The Problem with Manual Tender Evaluation
+- **Three pain points (use as large visual callouts or icon+text cards):**
+  1. **3–5 Days** per tender evaluation by committee
+  2. **Inconsistent** — two evaluators, two different conclusions
+  3. **No audit trail** — decisions untraceably made
+- **Supporting points (smaller text or bullets):**
+  - Mixed document formats: typed PDFs, scans, phone photographs
+  - Government procurement worth ₹50 lakh crore+ annually in India
+  - Project delays traced directly to procurement bottlenecks
+
+---
+
+### SLIDE 3 — Our Solution
+- **Title:** TenderIQ — Four Stages, End to End
+- **Four stage cards (equal weight, horizontal or 2×2 layout):**
+
+  **Stage 1 — Extract**
+  DeepSeek LLM reads the tender PDF and returns each criterion as structured JSON:
+  category, mandatory flag, threshold rule, source clause, query hints.
+
+  **Stage 2 — OCR & Index**
+  Three-tier pipeline handles any document format.
+  All text chunked and indexed for semantic retrieval.
+
+  **Stage 3 — Evaluate**
+  Vector search finds relevant evidence. LLM produces a verdict with confidence.
+  Safety rule prevents silent disqualification.
+
+  **Stage 4 — Review & Audit**
+  Borderline cases go to a human review queue. Every action logged.
+  Full audit trail exportable as CSV.
+
+- **Bottom line (callout box):**
+  "Minutes, not days. Every verdict traceable to a document and page."
+
+---
+
+### SLIDE 4 — Architecture
+- **Title:** System Architecture
+- **Diagram description (reproduce this as a visual flow diagram):**
+
+```
+  Tender PDF                    Bidder Documents
+      │                         (PDFs · scans · photos)
+      ▼                                  │
+  DeepSeek LLM                   3-Tier OCR Pipeline
+  (Extract Criteria)             ① PyMuPDF   (typed PDF)
+      │                          ② Tesseract (scans)
+      ▼                          ③ Vision LLM (low conf.)
+  Criteria JSON                          │
+  (C1–C5 structured)             Vector Index (in-memory)
+      │                          all-MiniLM-L6-v2 embeddings
+      └──────────────────────────────────┘
+                          │
+                    DeepSeek LLM
+                    (Evaluate each criterion)
+                    combined confidence score
+                          │
+            ┌─────────────┴──────────────┐
+        eligible /                 needs_review
+       not_eligible             Human Review Queue
+            │                          │
+            └───────── SQLite Audit Log ────────┘
+```
+
+- **Key technical facts (sidebar or footnotes on slide):**
+  - Single-process Streamlit app — no separate backend
+  - Deployable to Streamlit Cloud or HuggingFace Spaces
+  - All storage is local: SQLite + in-memory vector index
+  - Only external dependency: DeepSeek API
+
+---
+
+### SLIDE 5 — The OCR Demo (the centrepiece)
+- **Title:** Three-Tier OCR — Handling Any Document Format
+- **Three tier cards with visual progression:**
+
+  **Tier 1 — PyMuPDF**
+  - Trigger: Document is a typed/digital PDF
+  - Cost: Free, instant
+  - Confidence: 1.0 (lossless text extraction)
+  - Source label in UI: 📄 Typed PDF
+
+  **Tier 2 — Tesseract**
+  - Trigger: Scanned PDF or image file
+  - Cost: Free, local, fast
+  - Confidence: Mean of per-word OCR scores
+  - Source label in UI: 🔍 Tesseract
+
+  **Tier 3 — DeepSeek Vision LLM**
+  - Trigger: Tesseract confidence < 65%
+  - Cost: One API call
+  - Confidence: 0.95
+  - Source label in UI: 👁 Vision LLM
+  - Action: `vision_ocr_invoked` logged to audit
+
+- **Demo scenario callout (use a highlighted box):**
+  > **Bidder C submits a blurry, rotated CA certificate scan.**
+  > Tesseract reads it at ~55% confidence.
+  > Vision LLM transcribes the turnover figure correctly.
+  > Combined confidence = 0.58 → routed to human review.
+  > This is intentional — borderline evidence requires a human.
+
+---
+
+### SLIDE 6 — Explainability & Compliance
+- **Title:** Every Decision is Explainable and Auditable
+- **Two columns:**
+
+  **Left — Criterion-Level Verdicts**
+  Each (bidder × criterion) pair shows:
+  - Which criterion was checked
+  - Which document and page provided the evidence
+  - What value was extracted (e.g. "INR 6.2 Cr")
+  - Which OCR tier read the document
+  - Combined confidence score (0–100%)
+  - Plain-English reason
+
+  **Right — Audit Trail**
+  Every action logged with:
+  - UTC timestamp
+  - Action type (criteria_extracted / bidder_processed / criterion_evaluated /
+    human_review_action / vision_ocr_invoked / precomputed_fallback_used)
+  - Model version
+  - Actor (system / officer)
+  - Full payload JSON
+  - Exportable as CSV
+
+- **Safety rule callout (prominent, in a coloured box):**
+  > **The Safety Rule:**
+  > If combined confidence is 0.55–0.80 AND verdict is `not_eligible`,
+  > the verdict is automatically downgraded to `needs_review`.
+  > A bidder is **never silently disqualified** at medium confidence.
+
+---
+
+### SLIDE 7 — Demo Results
+- **Title:** Demo: Three Bidders, Three Outcomes
+- **Three bidder cards side by side:**
+
+  **Bidder A — Apex Constructions Pvt. Ltd.**
+  Result: ✅ ELIGIBLE
+  - C1 Turnover: INR 6.37 Cr avg (threshold: 5 Cr) — PASS
+  - C2 Projects: 5 completed including CRPF barracks �� PASS
+  - C3 GST: GSTIN 27AABCA1234F1Z5, Active — PASS
+  - C4 ISO 9001:2015: Valid June 2027 — PASS
+  - All typed PDFs, confidence ≥ 93% on all criteria
+
+  **Bidder B — BuildRight Enterprises**
+  Result: ❌ NOT ELIGIBLE
+  - C1 Turnover: INR 1.5 Cr avg (threshold: 5 Cr) — FAIL
+    "Average annual turnover of INR 1.5 Cr is below the required
+    minimum of INR 5 Cr."
+  - C2–C4: All pass
+  - Automatically disqualified with high confidence (95%)
+
+  **Bidder C — Shree Constructions & Services**
+  Result: ⚠️ NEEDS REVIEW
+  - C1 Turnover: Submitted as blurry scan
+    Tesseract ~55% → Vision LLM transcribes INR 5.4 Cr
+    Combined confidence 0.58 → needs review (safety rule)
+  - C2: Exactly 3 projects (borderline)
+  - C3–C4: Pass
+
+- **Bottom metric strip:**
+  | Metric | Value |
+  |--------|-------|
+  | Criteria extracted | 5 |
+  | Bidder documents processed | 15 |
+  | LLM evaluation calls | 15 |
+  | Vision OCR invocations | 1 |
+  | Human review items | 1 |
+  | Total audit entries | 20+ |
+
+---
+
+### SLIDE 8 — Tech Stack & Future Work
+- **Title:** Stack, Impact & What's Next
+- **Left side — Tech Stack (as a clean table):**
+
+  | Component | Technology |
+  |-----------|------------|
+  | UI & orchestration | Streamlit 1.39 |
+  | LLM | DeepSeek API (OpenAI-compatible) |
+  | OCR Tier 1 | PyMuPDF 1.24 |
+  | OCR Tier 2 | Tesseract |
+  | OCR Tier 3 | DeepSeek Vision LLM |
+  | Semantic retrieval | sentence-transformers all-MiniLM-L6-v2 |
+  | Data validation | Pydantic v2 |
+  | Audit log | SQLite |
+  | Deployment | Streamlit Cloud / HuggingFace Spaces |
+
+- **Right side — Future Work (as bullets):**
+  - Multi-tender workspace — same bidder pool, multiple tenders
+  - GeM portal API integration — live tender ingestion
+  - Automated bidder ranking with weighted scoring
+  - LayoutLM for complex financial tables in scanned statements
+  - Multi-evaluator workflow with role-based approval
+  - Review queue email/SMS notifications
+  - Audit PDF export for procurement oversight submissions
+
+- **Bottom — Impact callout:**
+  > **3–5 days → minutes.**
+  > Every verdict traceable to a document, page, and model version.
+  > Built in one hackathon session. Deployable today.
+
+---
+
+## 5. Narrative Arc (how the slides tell a story)
+
+The deck should flow as:
+1. **Hook** (Slide 1) — big, confident title
+2. **Pain** (Slide 2) — make the problem visceral with the 3 numbers
+3. **Solution** (Slide 3) — 4 clean stages, not overwhelming
+4. **Credibility** (Slide 4) — architecture shows it's real engineering
+5. **Differentiator** (Slide 5) — the OCR story is unique and concrete
+6. **Trust** (Slide 6) — explainability + audit builds confidence with judges
+7. **Proof** (Slide 7) — real outcomes, real numbers, real bidder scenarios
+8. **Vision** (Slide 8) — grounded stack + forward-looking future work
+
+Every slide should have **one dominant visual** and **limited text**. Judges skim.
+The most important information should be readable in 3 seconds.
+
+---
+
+## 6. The Six Visual Styles
+
+Produce one presentation per style. All use the same slide content from section 4.
+
+---
+
+### Style 1 — Dark Professional (PPTX)
+**File:** `deck/TenderIQ_v1_dark_professional.pptx`
+
+**Palette:**
+- Slide background: `#0D1B2A` (deep navy)
+- Primary text: `#F1F5F9` (near white)
+- Secondary text: `#94A3B8` (muted blue-grey)
+- Accent / headings: `#F0A500` (gold)
+- Eligible green: `#22C55E`
+- Not eligible red: `#EF4444`
+- Needs review amber: `#F59E0B`
+- Card backgrounds: `#1E3A5F` (lighter navy)
+- Borders: `#2D4A6B`
+
+**Typography:**
+- Headings: Calibri Bold or Arial Bold, 28–32pt, gold
+- Body: Calibri or Arial, 16–18pt, near-white
+- Captions/labels: 12–13pt, muted blue-grey
+
+**Style rules:**
+- Dark background on every slide
+- Title slide: large gold ⚖️ emoji, gold title text on navy
+- Section headings have a thin gold left border or underline
+- Cards/boxes: slightly lighter navy background (#1E3A5F) with gold border
+- Verdict chips: coloured filled rectangles (green/red/amber) with white text
+- Progress/confidence: horizontal bar in gold on dark track
+- The OCR tier cards (Slide 5): three columns, each with a different accent colour
+  (blue for Tier 1, purple for Tier 2, orange for Tier 3)
+- Architecture diagram (Slide 4): use white-on-dark text boxes connected with
+  gold arrows
+
+---
+
+### Style 2 — Clean Minimal (PPTX)
+**File:** `deck/TenderIQ_v2_clean_minimal.pptx`
+
+**Palette:**
+- Slide background: `#FFFFFF`
+- Primary text: `#111827`
+- Secondary text: `#6B7280`
+- Accent: `#2563EB` (blue)
+- Light accent background: `#EFF6FF`
+- Eligible: `#059669`
+- Not eligible: `#DC2626`
+- Needs review: `#D97706`
+- Dividers/borders: `#E5E7EB`
+
+**Typography:**
+- Headings: Inter or Calibri Light Bold, 28–32pt, #111827
+- Body: Inter or Calibri, 15–16pt, #374151
+- Captions: 11–12pt, #9CA3AF
+
+**Style rules:**
+- White background throughout
+- Large amounts of whitespace — never fill the slide
+- Title slide: small ⚖️ followed by large "TenderIQ" in #111827, subtitle in grey
+- Section headings: simple left-aligned text with a 3px blue left border
+- Cards: white with a 1px #E5E7EB border and very subtle shadow (simulate with
+  slightly off-white fill)
+- No gradients, no heavy fills — colour used sparingly as accent only
+- Verdict chips: light fill (green/red/amber at 15% opacity) with bold coloured text
+- Numbers/stats (Slide 2): very large (80–96pt), blue accent colour, minimal
+  surrounding text
+- Architecture diagram (Slide 4): use grey boxes with blue connector arrows,
+  clean and uncluttered
+
+---
+
+### Style 3 — Government Official (PPTX)
+**File:** `deck/TenderIQ_v3_government_official.pptx`
+
+**Palette:**
+- Primary: `#003580` (deep government blue, similar to NIC / India.gov.in)
+- Secondary: `#FFFFFF`
+- Accent: `#FF9933` (saffron, from the Indian tricolour)
+- Third accent: `#138808` (India green)
+- Background: `#F5F5F0` (off-white, like a government document)
+- Text: `#1A1A1A`
+- Borders: `#003580`
+
+**Typography:**
+- Headings: Times New Roman Bold or Cambria Bold, 26–30pt, #003580
+- Body: Arial or Calibri, 14–15pt, #1A1A1A
+- Official labels: small caps, 11pt, #003580
+
+**Style rules:**
+- Header bar on every slide: deep blue (#003580) band at top with white text for
+  slide title; thin saffron line below the header band
+- Footer on every slide: "TenderIQ · CRPF Hackathon · Theme 3" in small text
+  on the blue header colour
+- Title slide: formal layout — emblem/logo area top left, large title centred,
+  "Ministry of Home Affairs" sub-line
+- Slide content area: off-white background, clean margins
+- Tables: blue header row (#003580, white text), alternating white/#F0F4FF rows
+- Callout boxes: thin blue border, very light blue fill (#EBF0FF)
+- Verdict indicators: use formal language labels ("ELIGIBLE", "NOT ELIGIBLE",
+  "UNDER REVIEW") in coloured text, no emoji
+- This style should feel like an official government presentation, not a startup deck
+
+---
+
+### Style 4 — Modern Gradient (PDF via reportlab)
+**File:** `deck/TenderIQ_v4_modern_gradient.pdf`
+
+**Palette:**
+- Gradient 1 (title slide): `#667EEA` → `#764BA2` (purple-blue)
+- Gradient 2 (content slides background strip): `#0EA5E9` → `#2563EB`
+- Card fills: `#FFFFFF` with coloured top accent border
+- Text on gradient: `#FFFFFF`
+- Text on white: `#0F172A`
+- Eligible: `#10B981`
+- Not eligible: `#F43F5E`
+- Needs review: `#FBBF24`
+
+**Typography:**
+- Headings on gradient: white, bold, 24–28pt
+- Body on white cards: dark, 12–14pt
+- Stat numbers: 48–56pt, gradient-coloured
+
+**Style rules (reportlab specific):**
+- Title slide: full-page gradient background (use `canvas.linearGradient` if
+  available, or approximate with filled rectangles stepping from #667EEA to #764BA2)
+- Content slides: white background with a gradient-filled header band (top 20% of
+  slide) for the slide title
+- Cards: white rectangles with a 4px top border in a theme colour, subtle grey
+  border on other sides
+- Stat numbers on Slide 2: very large, rendered in the gradient colours
+- OCR tiers on Slide 5: three cards with top borders in blue, purple, orange
+- Arrows in architecture diagram: use curved lines in gradient blue
+- Avoid heavy outlines — use fill and spacing instead
+- Page numbers bottom right in muted colour
+
+---
+
+### Style 5 — Data Forward (PPTX)
+**File:** `deck/TenderIQ_v5_data_forward.pptx`
+
+**Palette:**
+- Background: `#FAFAFA`
+- Primary: `#1E293B`
+- Accent: `#6366F1` (indigo)
+- Chart colours: `#22C55E`, `#EF4444`, `#F59E0B`, `#3B82F6`, `#8B5CF6`
+- Grid lines: `#E2E8F0`
+- Text: `#334155`
+
+**Typography:**
+- Data labels: 14–16pt bold, #1E293B
+- Axis labels / captions: 10–11pt, #64748B
+- Slide titles: 24pt bold, indigo
+
+**Style rules:**
+- This variant leads with data visualisation on every slide where possible
+- Slide 2 (Problem): use a simple bar chart showing "Days per tender" comparison
+  (manual: 3–5 days, TenderIQ: minutes represented as <0.1 days)
+- Slide 3 (Solution): use a horizontal process flow with numbered circles
+- Slide 5 (OCR): use a stacked bar or table showing accuracy by tier
+- Slide 7 (Demo results): use a verdicts breakdown chart — 3 bidders × 5 criteria
+  as a colour-coded matrix (green/red/amber cells)
+- Slide 8 (Stack): use a visual table with technology icons (text-based approximation)
+- Charts should be built with python-pptx chart objects (not images) where possible,
+  or use matplotlib to embed PNG charts
+
+**Key chart specs:**
+- Demo results matrix (Slide 7): 3 rows (bidders) × 5 columns (criteria), each cell
+  filled green/red/amber with a 1-letter code (E/N/R)
+- OCR confidence comparison (Slide 5): simple bar chart showing
+  Tier 1: 100%, Tier 2: ~55–65%, Tier 3: ~95%
+- Problem scale (Slide 2): two-bar chart, Manual vs TenderIQ, logarithmic scale
+  or just text-anchored bars
+
+---
+
+### Style 6 — Infographic (PDF via reportlab)
+**File:** `deck/TenderIQ_v6_infographic.pdf`
+
+**Palette:**
+- Background: `#FFFFFF`
+- Section stripe: `#F8FAFC`
+- Primary icon colour: `#2563EB`
+- Icon accents: `#22C55E`, `#EF4444`, `#F59E0B`, `#8B5CF6`
+- Text: `#0F172A`
+- Subtext: `#64748B`
+
+**Typography:**
+- Large numbers: 48–60pt, bold, primary colour
+- Section labels: 10pt, all-caps, letter-spaced, muted
+- Body: 12pt, dark
+
+**Style rules:**
+- Every slide is built around a large central icon or number
+- Slide 2 (Problem): three large numbers (3–5, ✗, ?) each with a one-line label
+- Slide 3 (Solution): four large icons (📄 → 🔍 → ⚖️ → 📋) with stage labels
+- Slide 4 (Architecture): a vertical flow infographic, not a box diagram —
+  icon per stage, connecting lines, short labels
+- Slide 5 (OCR): three large tier icons stacked with an arrow between them,
+  confidence % shown as a circular progress indicator (drawn with arc)
+- Slide 7 (Demo): three large outcome icons (✅ ❌ ⚠️) each with 3 bullet points
+- Slide 8 (Future): icon grid of 6 future directions, each with a 1-line label
+- No heavy borders — whitespace is the separator
+- Use reportlab's `canvas.drawString`, arcs for circular indicators, and
+  filled rectangles for bars
+
+---
+
+## 7. Technical Implementation Notes
+
+### python-pptx (for Styles 1, 2, 3, 5)
+
+```python
+from pptx import Presentation
+from pptx.util import Inches, Pt, Emu
+from pptx.dml.color import RGBColor
+from pptx.enum.text import PP_ALIGN
+from pptx.util import Inches, Pt
+
+# Slide size — widescreen 16:9
+prs = Presentation()
+prs.slide_width = Inches(13.33)
+prs.slide_height = Inches(7.5)
+
+# Add a blank slide
+slide_layout = prs.slide_layouts[6]  # blank
+slide = prs.slides.add_slide(slide_layout)
+
+# Add a filled rectangle
+from pptx.util import Inches
+shape = slide.shapes.add_shape(
+    MSO_SHAPE_TYPE.RECTANGLE,  # or use 1
+    Inches(0), Inches(0), Inches(13.33), Inches(7.5)
+)
+shape.fill.solid()
+shape.fill.fore_color.rgb = RGBColor(0x0D, 0x1B, 0x2A)
+shape.line.fill.background()  # no border
+
+# Add text box
+from pptx.util import Inches, Pt
+txBox = slide.shapes.add_textbox(Inches(1), Inches(2), Inches(11), Inches(2))
+tf = txBox.text_frame
+tf.word_wrap = True
+p = tf.paragraphs[0]
+p.text = "TenderIQ"
+p.alignment = PP_ALIGN.CENTER
+run = p.runs[0]
+run.font.size = Pt(54)
+run.font.bold = True
+run.font.color.rgb = RGBColor(0xF0, 0xA5, 0x00)  # gold
+
+# Save
+prs.save("deck/TenderIQ_v1_dark_professional.pptx")
+```
+
+**Key python-pptx patterns to use:**
+- `slide.shapes.add_shape(1, ...)` — adds a rectangle (MSO_SHAPE_TYPE.RECTANGLE = 1)
+- `shape.fill.solid()` + `shape.fill.fore_color.rgb = RGBColor(r, g, b)` — fill colour
+- `shape.line.fill.background()` — remove border
+- `slide.shapes.add_textbox(left, top, width, height)` — text box
+- `tf.paragraphs[0].runs[0].font.color.rgb` — font colour
+- `slide.shapes.add_picture(image_path, left, top, width, height)` — embed image
+- For tables: `slide.shapes.add_table(rows, cols, left, top, width, height)`
+- All measurements: use `Inches()` or `Pt()` or raw `Emu` (1 inch = 914400 Emu)
+
+**Avoid:**
+- `pptx.chart` (complex, often renders poorly) — use coloured shapes instead
+- Embedded images from URLs — use only local files or draw shapes
+
+---
+
+### reportlab (for Styles 4 and 6)
+
+```python
+from reportlab.pdfgen.canvas import Canvas
+from reportlab.lib.pagesizes import A4, landscape
+from reportlab.lib import colors
+from reportlab.lib.units import cm, mm
+
+W, H = landscape(A4)  # 841.89 x 595.28 points (29.7 x 21 cm landscape)
+
+c = Canvas("deck/TenderIQ_v4_modern_gradient.pdf", pagesize=landscape(A4))
+
+# Filled rectangle
+c.setFillColor(colors.HexColor("#0D1B2A"))
+c.rect(0, 0, W, H, fill=1, stroke=0)
+
+# Text
+c.setFillColor(colors.white)
+c.setFont("Helvetica-Bold", 48)
+c.drawCentredString(W/2, H/2, "TenderIQ")
+
+# Line
+c.setStrokeColor(colors.HexColor("#F0A500"))
+c.setLineWidth(3)
+c.line(2*cm, H - 3*cm, W - 2*cm, H - 3*cm)
+
+# New page
+c.showPage()
+
+# Save
+c.save()
+```
+
+**Key reportlab patterns:**
+- `c.rect(x, y, w, h, fill=1, stroke=0)` — filled rectangle, no border
+- `c.roundRect(x, y, w, h, radius, fill=1, stroke=0)` — rounded rectangle
+- `c.drawCentredString(x, y, text)` — centred text at point
+- `c.drawString(x, y, text)` — left-aligned text
+- `c.drawRightString(x, y, text)` — right-aligned text
+- `c.setFont("Helvetica-Bold", size)` — font (built-in: Helvetica, Times-Roman, Courier)
+- `c.arc(x1, y1, x2, y2, startAng, extent)` — arc (for circular indicators)
+- `c.line(x1, y1, x2, y2)` — line
+- `c.showPage()` — new slide/page
+- **Coordinate system:** origin is bottom-left; y increases upward
+  - To position from top: use `H - y_from_top`
+
+**Text wrapping in reportlab:**
+```python
+from reportlab.platypus import Paragraph
+from reportlab.lib.styles import ParagraphStyle
+
+style = ParagraphStyle('body', fontSize=12, leading=16, textColor=colors.white)
+p = Paragraph("Long text that wraps automatically.", style)
+p.wrapOn(c, width, height)
+p.drawOn(c, x, y)
+```
+
+---
+
+## 8. Quality Checklist
+
+Before saving each variant, verify:
+
+- [ ] All 8 slides are present with content from section 4
+- [ ] Title is readable in 2 seconds on slide 1
+- [ ] The three pain point numbers are prominent on slide 2
+- [ ] The safety rule callout is visually distinct on slide 6
+- [ ] The three bidder outcomes are clearly colour-coded on slide 7
+- [ ] No slide has walls of text — maximum ~6 bullet points per slide
+- [ ] Font sizes: headings 24–32pt, body 14–16pt minimum (readable when projected)
+- [ ] Consistent margin — at least 1 inch (reportlab: 2.5cm) from all edges
+- [ ] Consistent colour palette within each variant (no accidental colour mixing)
+- [ ] File saves without error and opens cleanly
+
+---
+
+## 9. Output Summary
+
+| File | Format | Style | Tool |
+|------|--------|-------|------|
+| `deck/TenderIQ_v1_dark_professional.pptx` | PPTX | Dark navy + gold | python-pptx |
+| `deck/TenderIQ_v2_clean_minimal.pptx` | PPTX | White + blue, minimal | python-pptx |
+| `deck/TenderIQ_v3_government_official.pptx` | PPTX | Government blue + saffron | python-pptx |
+| `deck/TenderIQ_v4_modern_gradient.pdf` | PDF | Purple-blue gradient | reportlab |
+| `deck/TenderIQ_v5_data_forward.pptx` | PPTX | Charts + data viz | python-pptx |
+| `deck/TenderIQ_v6_infographic.pdf` | PDF | Large icons + numbers | reportlab |
+
+All output to `deck/`. Delete `TenderIQ_Pitch.pdf` (the old bad one) after creating the new files.