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Secure AI virtual data roomsThe vector store is the foundation. Get it right.
Pinecone, Weaviate, Qdrant, Milvus, or pgvector — we pick the right engine for your scale and stack, then tune the indexing, filtering, and hybrid search so retrieval is fast and accurate at production volume.
Most retrieval problems are vector-store problems.
When a RAG or search system returns the wrong results, the cause is usually upstream of the LLM — in how vectors are indexed, filtered, and queried.
The wrong distance metric, an un-tuned HNSW index, missing metadata filters, or pure-dense search on a corpus full of exact-match terms (IDs, codes, names) all quietly tank precision. We treat the vector database as an engineering problem with measurable answers, not a checkbox you tick once.
Choosing the right engine.
| If you need… | We usually recommend |
|---|---|
| You're already on Postgres | pgvector — no new infra, transactional consistency |
| Zero ops, managed scale | Pinecone — serverless, predictable |
| Self-hosted hybrid search | Qdrant or Weaviate |
| Billion-vector scale | Milvus or Turbopuffer |
| Cheap cold storage of vectors | Turbopuffer / object-store-backed |
What we set up.
Engine selection
Benchmarked on your data volume, query patterns, latency target, and budget — with a TCO comparison.
Index tuning
HNSW/IVF parameters, distance metric, and quantization tuned for your recall/latency tradeoff.
Metadata + filtering
Schema for pre/post-filtering so queries respect tenancy, permissions, and freshness.
Hybrid search
Dense + sparse (BM25) retrieval fused with reciprocal rank fusion for real-world precision.
Ingestion pipeline
Embedding, upsert, and re-index workflows — including backfills and incremental updates.
Monitoring
Latency, recall, and cost dashboards so you catch drift before users do.
Ways to engage.
- Engine recommendation + TCO
- Index + metadata design
- Ingestion pipeline starter
- Hybrid search + reranking
- Tuned indexing at your scale
- Monitoring + eval harness
- Review of an existing setup
- Recall + latency + cost findings
- Prioritized fix plan
Hybrid retrieval, right in your database.
If you're on Postgres, you often don't need new infrastructure — pgvector plus full-text gets you hybrid search.
-- Hybrid search in Postgres: pgvector + full-textSELECT id, title, 0.6 * (1 - (embedding <=> $1)) + -- dense similarity 0.4 * ts_rank(tsv, query) AS score -- lexical matchFROM documents, plainto_tsquery($2) queryWHERE tsv @@ query OR (embedding <=> $1) < 0.35ORDER BY score DESCLIMIT 10;We tune the weights, index parameters, and distance metric against your data — and recommend a dedicated engine only when you'll actually feel the difference.
The defaults are almost never right.
Out-of-the-box HNSW parameters, the wrong distance metric, and missing metadata filters quietly cap your recall.
We benchmark recall, latency, and cost on your corpus and query patterns, then tune the index until retrieval is both fast and accurate at production volume.
Plan your vector DBCommon questions.
Do we even need a dedicated vector DB?
Pure-vector or hybrid search?
Can you fix our existing slow/inaccurate setup?
How do you handle multi-tenant data?
Bring us your retrieval problem.
Tell us your corpus size, query patterns, and latency target. We'll recommend an engine and an indexing strategy — and tell you if you don't need a vector DB at all.