All solutions
E-commerce & Marketplaces

Search That Understands Intent

Search and discovery rebuilt around what customers actually type, blending keyword and semantic matching, with relevance you can measure rather than argue about.

To first release
2 weeks
Relevance baseline
Measured
Built with
  • OpenSearch
  • pgvector
  • Claude
  • TypeScript
  • Next.js

The problem

Site search is usually the highest-intent surface on a product and the least invested in. The symptoms are familiar: no results for plural forms or synonyms, the best-selling item buried on page three, and a team unable to say whether last month of tuning made anything better because there was never a baseline to compare against.

How we would sequence it

  1. 01Mine your own search logs for the queries that fail, ranked by how much traffic they carry
  2. 02Blend lexical and semantic retrieval rather than replacing one with the other
  3. 03Build a relevance judgement set from real queries, so improvements are measurable
  4. 04Handle synonyms, misspellings and category intent explicitly rather than hoping the model infers them
  5. 05Ship behind a flag and compare against the current engine on live traffic

This solution describes our method for a class of problem. It is not a record of a completed client engagement, and the figures above are scope estimates rather than measured results.

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