geo

GeoLook

Open-source, self-hosted platform for end-to-end GEO implementation

For a specific project: status analysis → diagnosis → strategy → implementation tickets → execution → verification

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GEO = Generative Engine Optimization: getting AI engines (ChatGPT, Perplexity, Gemini, DeepSeek, Doubao…) to proactively mention and cite your brand when answering user questions. Not geographic info, not classic SEO.

1. Problems it solves

More and more users ask AI directly — "best tools for X", "X vs Y, which one". If your brand:

2. Feature map

Four stages plus operations, all in one self-hosted dashboard:

Status — Engine performance across 17 engines (10 automated via API + 7 manual, incl. Google AI Overviews and Metaso): mention rate, rank, citation share, what each engine actually cites, sample replay of raw answers, suspected-negative flags; brand mention distribution (you vs. competitors, per engine and aggregated); competitor tables with each rival's strongest engine one click away — plus each rival's citation-source mix and a "channels they have that you don't" hit-list; a 7-category question bank with intent-group cards (buyer / educate / probe — see at a glance which intent class you're absent from) where every question gets a diagnosis type (suspected-negative > competitor-dominated > absent > low-ranked); a sample library where every AI answer's metadata is browsable and human-correctable (regex parsing misreads — name collisions, negation — get fixed here; corrections recompute metrics instantly and survive re-sampling), with per-sample citation-source breakdown (domain × count · share) and sampling-environment provenance (sandbox / incognito / dedicated profile / personal — personal auto-downgrades to "needs review").

Keyword mining — expand the question bank from real search demand: Baidu suggest (CN) + Google autocomplete (Global) terms from brand/competitor/category roots (free public endpoints, no keys). Each round is snapshot-diffed to flag rising demand (affects topic ordering, never metrics); alternative/vs phrasings mined from competitor roots feed the Competitors page. Candidates only — adding to the bank is always a manual check.

Diagnosis — Site audit organized as a four-layer dependency chain (Access → Orientation → Understanding → Quotability: each layer depends on the one above, and a failing Access layer makes everything downstream invisible to engines — the fix order is computed for you). The Access layer goes well beyond robots checks: RFC 9309-compliant robots parsing (catches wildcard-group blocks, shared UA groups and specificity overrides that line-by-line regexes miss), WAF/CDN differential probing with real AI-crawler UAs (robots may allow GPTBot while your CDN 403s it — invisible from a browser), X-Robots-Tag header noindex, llms.txt link validation, hreflang coverage, sitemap index-pollution and duplicate title/content detection. Passage-level quotability: retrieval picks passages, not pages — pages with sections but zero independently quotable passages get flagged with a concrete fix. Plus gap diagnosis (content → channels → facts); a channel map of 19 channels weighted by real citation-corpus data; and a brand facts library as the single source of truth that llms.txt, JSON-LD and content drafts are generated from.

Action — Structured tickets (rationale / owner / effort / window / acceptance criteria) carrying an independent risk grade (low-risk quick wins / observe with 7-14-28-day recheck / high-risk technical changes with backup & rollback discipline — priority says how important, risk says how careful), with "first-measured → current → target" progress bars and automatic reopening on regressions; tickets deep-link to the exact question they most need written; a content workbench (topic pool sorted by "not mentioned + no content", required extraction blocks and brand facts at hand, live citability pre-check, fabrication-risk lint for AI drafts, and a distribution checklist matching each piece to its target channels); deploy assets (llms.txt, JSON-LD, HTML snippets, plus an AI-traffic attribution pack: GA4 "AI engines" channel-group regex, server-log counting script, source-snapshot guidance — closing the loop from "cited" to "converts"); publishing with channels grouped General / China / Global — GitHub, WordPress drafts, WeChat OA drafts, webhook, plus real X (teaser tweet with auto-backlink to your latest long-form publish) and Reddit (full-markdown self-post) integrations, each with an in-dialog step-by-step credential guide; multi-channel checkbox publishing per article, and publish status synced back to the action plan, pending list and question bank. Platforms without a personally usable official API (Weibo, Xiaohongshu, Toutiao, Bilibili, LinkedIn, Facebook, Instagram) are deliberately not integrated — better absent than fake; the page says why and offers the webhook bridge instead. Publishing is always manually confirmed, article by article.

Results — Per-question before/after (all / CN / global tabs), task-level before/after, verification history; boss-ready one-pager, execution plan, and a complete client delivery package (HTML + CSV).

Operations — Scheduled full-cycle re-runs (every 7/14/30 days; register the dashboard as a macOS standing service with scripts/service.sh install — starts at login, restarts on crash, keeps running with every terminal closed, so scheduled re-r