Data

Retrieval & search

Embeddings, vector indexes, hybrid search, metadata filters, and reranking. PostgreSQL with pgvector, Elasticsearch, or dedicated vector databases can connect search to relevant business information.

Discuss your project

Relevant results must also be permitted results

Example question: How do I process a damaged delivery?

Delivery exception procedure
Approved operations guide · matches the task
Customer escalation policy
Approved support guide · related context
Restricted account notes
Excluded by access policy · not shown to the requester

Search quality includes relevance, freshness, and access. We evaluate the kinds of questions people actually ask and make sure a result remains linked to its source and permissions.

Combine signals around the query

Search approaches and their role
Signal	Useful for	Check carefully
Keyword match	Names, exact identifiers, specialist terms	Synonyms and vocabulary differences
Semantic similarity	Related concepts and paraphrased questions	Plausible but irrelevant matches
Metadata filters	Team, document type, date, access scope	Stale or missing metadata

How do you measure improvement?

Build a reviewed set of queries with expected relevant sources. Inspect misses and false positives, then evaluate changes to indexing, filters, and ranking against that same set.

Your project, scoped around the outcome