Retrieval your agents — and your auditors — can trust
A vector database is the memory an agent reaches into before it acts. We build that memory on your systems of record, with access control, freshness, and lineage baked into every query.
- Engine selection & benchmarking
- ACL-aware retrieval at query time
- CDC-driven freshness
- Retrieval lineage & evals
The index is where agents go wrong or go right
Most agent failures aren't reasoning failures — they're retrieval failures.
An agent is only as good as what it can pull into context. Hand it a vector store stuffed with duplicate PDFs, stale policy docs, and naively chunked tables, and it will confidently act on the wrong information. The model isn't hallucinating; it's faithfully using bad retrieval.
We treat the vector database as production infrastructure, not a demo notebook. That means deliberate chunking, evaluated embeddings, metadata that mirrors your access rules, and a retrieval layer that's measured the way you'd measure any other query path — on precision, recall, latency, and cost.
A retrieval stack, not just an index
Six layers that turn raw documents into grounded, governed context for your agents.
Ingestion & chunking
Parsers tuned per source — tables, PDFs, tickets, code — with chunking that preserves structure instead of slicing mid-thought.
Embedding strategy
We benchmark embedding models on your corpus and queries, then standardize on the one that wins on recall and cost, not hype.
Hybrid retrieval
Dense vectors plus keyword (BM25) and metadata filters, with rerankers to push the right chunks to the top of the context window.
ACL-aware queries
Source-document permissions travel with every chunk and are enforced at query time, so retrieval respects who's asking.
Systems-of-record sync
Change-data-capture from your CRM, ERP, and wikis keeps the index current and purges deleted records automatically.
Retrieval lineage
Every answer links back to the exact chunks and source documents that produced it — traceable for review and audit.
From corpus to grounded agent
A measured path that proves retrieval quality before anything reaches production.
Profile
We inventory your sources, access rules, update cadence, and the questions agents actually need answered.
Benchmark
We test engines and embeddings against a labeled query set drawn from your real corpus.
Wire
We connect ingestion to your systems of record and enforce ACLs and freshness end to end.
Evaluate
We track precision, recall, latency, and cost in CI, so regressions surface before users do.
Retrieval that respects who's asking
A shared vector index is a quiet way to leak data. If every chunk is searchable by every agent run, a finance bot can surface a chunk from an HR file and no one notices until it's in a response.
We carry each source document's permissions into the index as metadata and filter on the requesting user's entitlements before the model ever sees a result. The agent retrieves exactly what the caller is allowed to read — nothing more — and every query is logged with the identity it ran under.
- Per-chunk ACLs synced from the source system
- Entitlement filtering applied before reranking
- Identity-scoped query logs for every retrieval
A demo RAG store vs. a production index
The gap between a weekend prototype and something you'd put in front of customers.
| A demo RAG store | An Automatic.co index | |
|---|---|---|
| Chunking | Fixed character splits | Structure-aware, source-specific |
| Access | Everything visible to everyone | ACL-filtered at query time |
| Freshness | Re-indexed by hand | CDC-driven, deletes propagate |
| Quality | Eyeballed once | Precision/recall evals in CI |
| Traceability | Opaque answers | Chunk-level retrieval lineage |
Frequently asked questions
Which vector database should we use?
It depends on scale, latency, and where your data must live. We deploy pgvector when you want one less system to run, Qdrant or Milvus for billion-vector workloads, and Pinecone or Weaviate when managed ops matter more than control. We benchmark on your corpus before committing.
How do you stop an agent from retrieving data a user shouldn't see?
Access control is enforced at query time, not just at ingest. Every chunk carries the source document's ACLs as metadata, and retrieval is filtered by the requesting user's entitlements before results ever reach the model — so the agent can't surface a record the caller couldn't open themselves.
How do you keep the index fresh as systems of record change?
We wire ingestion to change-data-capture or event streams from your CRM, ERP, wiki, and ticketing systems, so edits and deletes propagate within minutes. Stale or tombstoned chunks are evicted, not left to mislead the agent.
Can we run this entirely inside our own environment?
Yes. The database, the embedding models, and the retrieval service can all run in your VPC, on-prem, or air-gapped. No document text or embeddings need to leave your perimeter.
Connect the rest of the stack
Vector retrieval rarely ships alone — here's where it plugs in.
Bring your corpus. Leave with a retrieval benchmark.
One working session to profile your sources and the access rules an agent has to honor before it can be trusted to act.