Locatable Evidence
Maps facts to stable URLs, heading paths, sections, fields, dates, units, identifiers, tables, metadata, and source references.
ClawSEO · AI SEO for Agentic Discoverability
ClawSEO audits page evidence, tests supported deterministic retrieval, and traces supplied claim provenance, then prioritizes documented AI SEO work.
ClawSEO audits public page evidence, tests supported deterministic retrieval paths, and traces supplied claim provenance. The goal is to make relevant documents easier for agents to locate, understand, verify by source, and select.
This goes beyond checking whether a page can be crawled or summarized. ClawSEO treats each page as a machine-queryable information environment. It examines whether an agent can isolate the exact claim, date, specification, relationship, or source it needs without reading unrelated content.
Maps facts to stable URLs, heading paths, sections, fields, dates, units, identifiers, tables, metadata, and source references.
Tests a supplied local evidence index through BM25, weighted field retrieval, and deterministic structured lexical hybrid retrieval.
Analyzes imported repeated AI observations inside matched conditions instead of presenting one answer as a permanent rank.
Traces claim origins and source families before repeated statements are treated as independent confirmation.
ClawSEO keeps measured evidence separate from interpretation and recommended changes. It can turn discoverability findings into prioritized work for page purpose, section structure, explicit fields, entity terminology, metadata, schema, internal links, source clarity, and next-step investigation.
The objective is stronger Agentic Discoverability and more relevant organic discovery. ClawSEO does not promise a permanent position in an AI answer or claim traffic growth without measurement. It creates an evidence-based path for improving how the right document can be found and selected.
ClawSEO v2.10.0 provides deterministic BM25, weighted field, and structured lexical hybrid retrieval for a supplied local evidence index. Dense retrieval, embeddings, and vector similarity are not claimed. AI visibility metrics require imported observations from a controlled repeated-run experiment.
Public inspection and private reports can run without changing a website. Publishing or other external work remains gated by authorized access, approval of the exact action, configured execution, and independent verification of the real result.