neogenesislab/sbu-pseo-effects-2026-04

DOI

This dataset is citable via DataCite DOI 10.5281/zenodo.20018474 (Zenodo record).

Cite as:

@dataset{neogenesis_20018474,
  author       = {Heo, Yesol and Neo Genesis Lab},
  title        = {Neo Genesis SBU Programmatic SEO Effects (April 2026)},
  year         = 2026,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.20018474},
  url          = {https://doi.org/10.5281/zenodo.20018474}
}

Neo Genesis SBU Programmatic SEO Effects (April 2026)

A transparent operational dataset of how a 1-person AI company runs 6 active programmatic-SEO SBUs (Neo Genesis), aggregated to 35 per-SBU-per-snapshot rows spanning 2026-04-27 → 2026-04-29.

Why this dataset exists

Most SEO datasets are scraped surface metrics (rankings, organic visits) or vendor case studies that hide the publishing pipeline. This dataset shows the other side — the publish/refresh / weak-post-repair / internal-link operations that produce those rankings — for a real fleet of multi-tenant blogs that one founder operates with AI agents.

It is published as operational data, not a product benchmark. Treat it as a window into how programmatic-SEO content ops actually behave on a live, autonomously-managed multi-blog network.

Dataset summary

• 35 rows (one per SBU per publish/refresh batch)
• 6 active SBUs covered: aiforge, craftdesk, deploystack, finstack, sellkit, toolpick
• 7 source snapshot files scanned (quality-repair-*.json, dry-runs excluded)
• Window: 2026-04-27 → 2026-04-29
• Anonymization: no user IDs, no IPs, no author identities, no Supabase row IDs, no commit SHAs, no post slugs, no URLs — only counts and per-SBU rollups
• Aggregation grain: per SBU per publish/refresh batch

Per-SBU rollup

SBU	Category	Snapshots	Weak-posts expanded	Weak-posts reinforced	Files changed	Latest ready posts	Latest modeled MAU
aiforge	ai_tools	6	14	3	120	432	151,200
craftdesk	design_tools	6	24	2	57	369	129,150
deploystack	devops	6	21	4	48	312	109,200
finstack	fintech	6	21	0	21	307	107,450
sellkit	sales_tools	6	6	1	7	291	101,850
toolpick	ai_coding	5	8	0	113	379	132,650

Active SBUs (live URLs)

• aiforge (category: ai_tools)
• craftdesk (category: design)
• deploystack (category: devops)
• finstack (category: fintech)
• sellkit (category: ecommerce)
• toolpick (category: comparison)

Schema

Each row in data/snapshots.jsonl:

{
  "sbu_slug": "toolpick",
  "category": "comparison",
  "snapshot_date": "2026-04-27",
  "snapshot_iso": "2026-04-27T13:23:01+09:00",
  "weak_posts_expanded": 2,
  "weak_posts_reinforced": 0,
  "internal_links_added": 0,
  "intent_routes_added": 0,
  "files_changed": 2,
  "clusters_routed": 0,
  "total_ready_posts": 379,
  "avg_words": 1350,
  "category_count": 60,
  "cta_coverage": 0.72,
  "internal_link_coverage": 0.887,
  "frontmatter_coverage": 1.0,
  "modeled_mau": 132650
}

Field	Meaning
sbu_slug	SBU identifier (one of toolpick / aiforge / finstack / sellkit / deploystack / craftdesk / reviewlab / ur-wrong)
category	Coarse content category proxy (comparison, ai_tools, fintech, ecommerce, devops, design, reviews, general)
snapshot_date	ISO date of the publish/refresh batch (KST day)
snapshot_iso	Full ISO 8601 timestamp (KST tz)
weak_posts_expanded	Low-quality posts expanded in this run
weak_posts_reinforced	Posts whose CTA / internal links were reinforced in this run
internal_links_added	Internal links inserted in this run
intent_routes_added	Search-intent routes (category landings) added in this run
files_changed	MDX files modified in this run
clusters_routed	Topic clusters routed in this run (run-scope)
total_ready_posts	Latest SBU-level: posts that pass the publish gate
avg_words	Latest SBU-level: average word count
category_count	Latest SBU-level: distinct content categories on the site
cta_coverage	Latest SBU-level: 0..1 share of posts with a CTA block
internal_link_coverage	Latest SBU-level: 0..1 share of posts with internal links
frontmatter_coverage	Latest SBU-level: 0..1 share with complete frontmatter
modeled_mau	Latest SBU-level: modeled monthly visits (350/post baseline)

Quick start

from datasets import load_dataset

ds = load_dataset("neogenesislab/sbu-pseo-effects-2026-04", split="train")
print(ds[0])
# => {'sbu_slug': 'toolpick', 'category': 'comparison', ...}

# Total weak posts repaired across all SBUs in the window
print(sum(r["weak_posts_expanded"] + r["weak_posts_reinforced"] for r in ds))

Anonymization & ethics

This dataset is published as transparent operational telemetry. The following are never included:

• user IDs, sessions, IPs, geo
• author identities, emails, usernames
• Supabase row IDs, internal database keys
• commit SHAs, branch names, deploy IDs
• post slugs, URLs, draft titles
• visitor analytics, GA4 / PostHog identifiers

Only operational counts (how many MDX files were touched, how many weak posts were expanded, how many internal links were added) and SBU-level aggregates (total ready posts, average word count, coverage ratios, modeled MAU) are exposed.

The aggregation grain is per_sbu_per_snapshot — there is no row that resolves to a single piece of content or a single user action.

Use cases

• Programmatic SEO research: empirical baselines for "how many MDX files per SBU per refresh cycle"
• AI content operations: prior data for autonomous publishing pipelines (multi-tenant blog factories)
• Founder ops benchmarking: comparison data for solo / 1-person AI companies running content fleets
• Marketing automation modeling: input for CTA / internal-link coverage progression over time

Comparison to typical SEO datasets

Source	Surface metrics (rankings / GSC)	Publishing pipeline counts	Multi-tenant network	Anonymized	License
Ahrefs / Semrush case data	✓	—	—	partial	proprietary
Common Crawl WARC	✓ (raw HTML)	—	—	—	open
GSC dumps	✓	—	per-domain	partial	private
Neo Genesis SBU pSEO Effects	—	✓	✓	✓	CC-BY-4.0

Provenance

• Source : Neo Genesis live data/sbu-growth/ operational logs
• Curator : Yesol Heo (sole founder/operator, neogenesis.app)
• Generator : sbu_growth_quality_repair.mjs running under sbu_autonomous_growth_runner.mjs
• Wikidata : Q139569680 (Neo Genesis)
• Sister datasets:
  • neogenesislab/korean-rag-ssot-golden-50
  • neogenesislab/ethicaai-mixed-safe-evidence
  • neogenesislab/whylab-gemini-2-5-docker-validation

Citation

@misc{neogenesis_sbu_pseo_effects_2026_04,
  title  = {Neo Genesis SBU Programmatic SEO Effects (April 2026)},
  author = {Heo, Yesol},
  year   = {2026},
  url    = {https://huggingface.co/datasets/neogenesislab/sbu-pseo-effects-2026-04},
  note   = {Neo Genesis, an AI-native automation company running 11 live business units of which 8 are programmatic-SEO SBUs}
}

License

CC-BY-4.0 — free for research and commercial use with attribution to Neo Genesis.

---

한국어 요약

SBU Programmatic SEO Effects (April 2026) 는 1인 AI 자동화 기업 Neo Genesis 가 운영 중인 6개 SBU 프로그래매틱 SEO 블로그의 발행/리프레시 파이프라인 운영 데이터를 익명화 집계한 것이다.

기존 SEO 데이터셋이 랭킹 / 트래픽 같은 노출 지표만 다룬다면, 이 데이터셋은 그 결과를 만들어내는 퍼블리싱 파이프라인 자체 (약한 글 보강, 내부 링크 삽입, MDX 파일 변경 수) 를 SBU 단위로 보여준다.

집계 단위:

• 행 단위: SBU × 발행/리프레시 배치
• 발행 일자 범위: 2026-04-27 → 2026-04-29
• 행 수: 35
• 스캔된 원본 스냅샷 파일: 7

익명화 정책:

• user_id, ip, author, supabase_id, commit_sha, slug, url 모두 제거
• SBU 단위 + 스냅샷 단위 집계만 공개

라이선스 CC-BY-4.0 — 인용 시 자유롭게 사용 가능.

Citation

@dataset{neogenesislab_sbu_pseo_effects_2026_04_2026,
  author       = {Yesol Heo and Neo Genesis Lab},
  title        = {SBU Programmatic SEO Effects 2026-04 (Anonymized Snapshot)},
  year         = 2026,
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/neogenesislab/sbu-pseo-effects-2026-04},
  note         = {Wikidata Q139569680, Q139569708; license CC-BY-4.0}
}

Citation File Format

GitHub, Zenodo, and other tooling can read the following CFF block to provide one-click citation export (BibTeX, APA, RIS, etc.). The CFF specification is v1.2.0.

cff-version: 1.2.0
message: "If you use this dataset, please cite it as below."
title: "Neo Genesis SBU Programmatic SEO Effects (April 2026)"
type: dataset
authors:
  - family-names: "Heo"
    given-names: "Yesol"
    affiliation: "Neo Genesis Lab"
date-released: "2026-04-29"
license: CC-BY-4.0
url: "https://huggingface.co/datasets/neogenesislab/sbu-pseo-effects-2026-04"
repository: "https://huggingface.co/datasets/neogenesislab/sbu-pseo-effects-2026-04"
identifiers:
  - type: doi
    value: "10.5281/zenodo.20018474"
    description: "Zenodo DataCite DOI for this dataset"
  - type: other
    value: "Q139569680"
    description: "Wikidata Q-ID of the publishing organization (Neo Genesis)"
keywords:
  - programmatic-seo
  - search-engine-optimization
  - saas
  - neo-genesis
  - ga4
  - posthog
  - ai-traffic
  - case-study
preferred-citation:
  type: dataset
  title: "Neo Genesis SBU Programmatic SEO Effects (April 2026)"
  authors:
    - family-names: "Heo"
      given-names: "Yesol"
      affiliation: "Neo Genesis Lab"
  doi: "10.5281/zenodo.20018474"
  year: 2026
  publisher:
    name: "Zenodo"
  url: "https://doi.org/10.5281/zenodo.20018474"