silkra

retrieval

Search sites like an LLM. Not a keyword tool.

Every page gets chunked and embedded the moment it's crawled — the same chunks a language model would retrieve. Run a query against a client, a prospect, or a competitor and see exactly which chunks rank, and why.

Google DeepMindbuilt in embedding model
Get startedUse cases
  • Query against real embedded chunks, not keyword lists
  • Compare retrieval winners client by client
  • Trace every hit back to the exact page and passage
Silkra chunk retrieval view in light mode

capabilities

Retrieval you can explain to a client

Silkra turns crawl data into a searchable embedding index the moment pages finish rendering. That means retrieval tests reflect what a model would actually pull — not a proxy built from titles and meta tags.

    Run semantic queries against any saved crawl without exporting to another tool
    Compare two sites on the same terms and see who wins passage by passage
    Hand stakeholders proof tied to the exact chunk, URL, and crawl state

query

Search content like a model would — cosine similarity against real chunks, not a keyword tool.

compare

Stack a client against their competitors and see who wins the retrieval, term by term and passage by passage.

locate

Every result points back to the exact chunk and page it came from — proof you can hand a client, not just a claim.

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Get started locally

Download the local semantic spider that maps any site by meaning, simulates how AI retrieves it, and turns crawl data into audits, sheets, and AI-ready context.

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free to start