Semantic search that finds it when the words do not match.
Embedding and index infrastructure that retrieves by meaning as well as by keyword — sized against your corpus rather than against a vendor benchmark.
Where keyword search runs out
Keyword search fails when the searcher and the document use different vocabulary — which is most of the time in a business with its own terminology, several generations of naming, and documents written by different departments. The information exists and is unfindable.
- People cannot find documents they know exist, because they guessed different words.
- Search returns hundreds of results ordered by relevance nobody agrees with.
- Synonyms and internal jargon defeat the existing search entirely.
- A vector database was introduced and results did not noticeably improve.
What we build
Retrieval infrastructure combining semantic and keyword matching, with the embedding strategy and index chosen against your corpus and your latency and cost constraints.
- Embedding model selection evaluated on your content, not on a public benchmark
- Chunking that respects document structure rather than a fixed character count
- Hybrid retrieval combining vector and keyword signals
- Metadata filtering so results respect entity, date and permission scope
- Incremental re-embedding as source documents change
- Latency and cost sized against your query volume
How it runs
Measured on your corpus at every stage, because embedding quality is content-dependent.
- 01Characterise the corpus
Volume, document types, structure and update frequency. These decide the architecture more than any product choice.
- 02Evaluate embeddings
Candidate models scored on your content against known-correct results, not on a leaderboard.
- 03Chunk with structure
Headings, sections and tables preserved, because a chunk that splits a table loses the meaning of both halves.
- 04Combine the signals
Vector and keyword retrieval together, since exact identifiers and part numbers still need literal matching.
- 05Keep the index current
Incremental re-embedding on change, so the index tracks the documents rather than diverging from them.
What changes once it is running
What semantic retrieval changes about finding things.
Vocabulary stops being a barrier
A search finds the document even when the searcher used entirely different words.
Identifiers still work
Hybrid retrieval means part numbers and reference codes match literally, which pure vector search handles poorly.
Results respect scope
Metadata filtering keeps results inside the entity, period and permission the searcher is working within.
AI grounding improves
Better retrieval is the largest single lever on the quality of any grounded AI system built on top.
How an engagement is shaped
Evaluation before infrastructure. Buying a vector database first is the common expensive mistake.
Corpus evaluation
Two weeks characterising the content and scoring embedding and chunking options against known-correct results.
Build the index
Pipeline, index and hybrid retrieval implemented, with refresh and monitoring.
Tune and operate
Ongoing evaluation as the corpus grows, since strategies that suit ten thousand documents may not suit a million.
Common questions
The things buyers ask before they commit. If yours is not here, it is a good first question for the assessment.
- Which vector database should we use?
- Possibly none. Several databases you already run support vector search adequately at mid-market volumes. A dedicated store earns its place at scale or under specific latency requirements, and we size that rather than assuming it.
- Is vector search better than keyword search?
- Different, not better. It handles vocabulary mismatch well and exact identifiers badly. Hybrid retrieval is almost always the right answer in a business context.
- How often does the index need rebuilding?
- Incremental updates on change are usually sufficient. A full rebuild is needed when the embedding model or chunking strategy changes, which is a planned event.
Bring the search that keeps failing.
A real query and the document that should have been returned. That pair tells us whether retrieval is the problem.