AI product Open source
GeoLook is an open-source, self-hosted platform for end-to-end Generative Engine Optimization (GEO), focused on whether AI engines mention and cite a brand. Its workflow runs from status analysis through diagnosis, strategy, implementation tickets, execution, and verification.
The platform samples answers from multiple AI engines and measures mention rate, rank, citation share, competitor visibility, and citation sources. It classifies question-level problems such as absence, low ranking, competitor dominance, and suspected negative mentions; stores browsable sample metadata; and allows human corrections to parser results that recompute metrics and persist across resampling. It can expand question banks using Baidu suggestions and Google autocomplete, with candidates requiring manual review.
GeoLook audits sites through an Access → Orientation → Understanding → Quotability dependency chain, covering crawler access, extraction blocks, citation-channel gaps, and brand-fact or messaging inconsistencies. It also performs checks such as robots and WAF/CDN behavior, noindex headers, llms.txt links, hreflang, sitemaps, duplicate content, and passage-level quotability. The action stage produces structured implementation tickets with rationale, ownership, effort, timing, acceptance criteria, risk grades, progress tracking, and regression-based reopening; it also provides content planning, deployable llms.txt and JSON-LD assets, and client-facing reports and execution plans. Subsequent sampling rounds compare results before and after implementation.
1 use taken from transcripts — each links to the moment in the video.
Turns generative engine optimization into an execution loop by measuring brand visibility across AI engines, auditing citation gaps, and producing implementation tickets and deployable assets such as llms.txt and JSON-LD. Later runs recrawl the site and reopen regressed work.
1 in the library.