Vector, full-text and hybrid search on turbopuffer's object-storage-backed engine. We bring a background in search engineering to namespace design, relevance, ingestion and migrations - so your RAG systems and agents retrieve the right context.
turbopuffer keeps data in object storage and caches what is queried on NVMe SSD and in memory. Getting the most from it means designing namespaces, schemas and query paths around that model.
Namespace boundaries drawn around what is queried together - per tenant, workspace or corpus.
Vector and BM25 queries fused into one ranked list that feeds RAG systems and agents.
Query paths designed around warm caches, with cold namespaces warmed before users need them.
Judgment lists and offline metrics, so retrieval changes are proven before they ship.
The search layer behind assistants and agents that must answer from your data.
One namespace per customer, so each tenant searches only its own documents.
Per-repository or per-workspace indexes that combine semantic and exact-term matching.
Hybrid search over large collections of reports, tickets and content.
Semantic search across help centers, wikis and internal knowledge.
Moves from another vector or search engine, validated against your real queries.
Review your data, queries and tenancy model, and agree on latency, quality and cost targets.
Prove the namespace layout, schema and hybrid queries against your real queries.
Ingestion, backfill and cutover with evaluation, monitoring and cache warming in place.
Continuous relevance and cost tuning - or a clean handover with runbooks and training.
Designing, building and running search on turbopuffer: namespace and attribute schema design, vector, BM25 full-text and hybrid queries, ingestion pipelines, cache and cost behaviour, migrations from other engines, RAG and agent integration, and retrieval evaluation.
turbopuffer fits RAG when your data splits naturally into many namespaces (for example one per customer, workspace or codebase), you want vector and BM25 search in one engine, and you can live with a slower first query on a cold namespace. Its storage lives in object storage with memory and NVMe SSD caching in front, so namespaces that are not being queried do not need to sit in memory.
A cold query reads the namespace from object storage, so it is slower; after that the data is cached on NVMe SSD and in memory and queries are much faster. turbopuffer lets you send a cache-warming hint before a latency-sensitive interaction, and we design query paths so users rarely hit a cold namespace.
Yes. turbopuffer has BM25 full-text search on attributes you enable for it, approximate nearest neighbour vector search, and multi-queries that run several subqueries against the same snapshot. Results can be fused server-side with reciprocal rank fusion and reranked natively or in your own code.
Yes. We map your existing indexes and metadata to turbopuffer namespaces and attribute schemas, backfill with batched writes, run both systems side by side against your real queries, and cut over once relevance and latency match or beat the current setup.
It depends on scope. We offer fixed-scope projects, time-and-materials engagements and monthly retainers. Book a call and we will give you a clear estimate for your situation.
Tell us about your data and what your search or agents need to find. In a free 30-minute call we will review where you are, what it would take, and how we would measure success.
