Search that understands what people mean.
Keyword search returns documents that contain the words. AI search returns the answer. We build semantic and hybrid search — relevance-tuned and grounded — for products, support, and internal knowledge.
Your users are asking questions, not typing keywords.
Modern users expect search to behave like an answer engine. When it returns a wall of weak keyword matches, they bounce.
The fix is rarely 'add AI' — it's combining semantic understanding with the precision of lexical search, then reranking the results and, where it helps, generating a direct grounded answer. We tune relevance against your real queries instead of trusting defaults.
How we build search that converts.
Query understanding
Spelling, synonyms, intent classification, and query rewriting so 'cheap flights ny' works.
Hybrid retrieval
Dense embeddings for meaning + BM25 for exact terms, fused for the best of both.
Reranking
A cross-encoder reorders the top results — the single biggest precision lever.
Answer generation
Optional: a grounded, cited answer above the results for question-style queries.
Relevance tuning
We build a judgment set from your real queries and measure NDCG, not vibes.
Speed
Caching, ANN tuning, and payload trimming to hold sub-200ms at your scale.
Where this shows up.
E-commerce search
Intent-aware product discovery that lifts conversion and surfaces the long tail.
Help center / deflection
Answer the question before it becomes a ticket.
Documentation search
Developers find the right page and snippet, fast.
Enterprise search
One search box across wikis, drives, and tickets — with permissions respected.
Content & archive search
Semantic search over articles, transcripts, and media metadata.
Structured + unstructured
Search that spans both your database and your documents.
Ways to engage.
- Relevance evaluation of current search
- Judgment-set + NDCG baseline
- Prioritized improvement plan
- Hybrid retrieval + reranking
- Query understanding
- Relevance tuning + monitoring
- 30-day support
- Relevance iterations on real queries
- New content sources
- A/B testing support
From a typo to the right answer.
Query understanding, hybrid retrieval, and reranking — the pipeline that turns 'retrn polcy' into the right result.
const q = await understand(rawQuery) // spell-fix, intent, synonymsconst dense = await index.knn(embed(q.text), 50)const lexical = await index.bm25(q.text, 50)const fused = rrf(dense, lexical) // reciprocal rank fusionconst ranked = await reranker.rank(q.text, fused) // precision liftreturn ranked.slice(0, 10)We measure NDCG against a judgment set built from your real queries — so 'better' is a number, not an opinion.
Often we build on top of what you have.
Elasticsearch or OpenSearch already handle your lexical search and infrastructure well. We add the semantic layer and reranking on top.
You keep the operational tooling you trust and gain search that understands intent — without a risky migration.
Start a search projectCommon questions.
Do we replace Algolia/Elasticsearch?
How do you measure 'better'?
Will it be fast enough?
Can it answer, not just list?
What are people failing to find?
Send us a handful of real queries that return bad results. We'll show you why — and what better looks like.