Private retrieval that never leaves your perimeter
Ground your agents on proprietary documents, contracts, and tickets — with retrieval that respects your permissions, encrypts every layer, and proves what it answered with.
- Vector store inside your VPC or on-prem
- Permission-aware retrieval
- Cited, traceable answers
- Encrypted at rest and in transit
Your corpus stays put
Most RAG demos quietly upload your knowledge base to a third-party index and call it secure. We run the whole retrieval stack — ingestion, chunking, embeddings, and the vector store — inside the boundary you already trust.
The model only ever sees the specific chunks a user is entitled to, retrieved at query time. Nothing is pre-shipped to a vendor, nothing is cached outside your control, and the index can be wiped or re-keyed on your schedule.
- Self-hosted vector DB (pgvector, Qdrant, Milvix-class)
- Embeddings generated in-network when required
- No vendor-side copy of your knowledge base
Security built into every layer
Retrieval is only as safe as its weakest control. We harden ingestion, the index, and the answer path.
Permission-aware retrieval
We mirror your ACLs and row-level security into the index and filter candidates by the caller's identity before the model sees a single chunk.
In-perimeter vector store
Embeddings and source text live in your VPC, on-prem cluster, or air-gapped network — never in a shared multi-tenant index.
Cited, auditable answers
Every response carries inline citations and a logged trail of the chunks retrieved, scores, and entitlements applied.
Retention & redaction
PII detection and redaction at ingest, configurable retention windows, and per-source policies governed centrally.
Model-agnostic grounding
Swap inference or embedding models without re-architecting retrieval — private endpoints or self-hosted, your call.
Hardened ingestion
Connectors pull from SharePoint, Confluence, S3, and databases over least-privilege credentials, with change-data capture to keep the index fresh.
What happens on every question
A single retrieval path, instrumented end to end.
Authenticate
The request arrives with the user's identity and entitlements resolved against your IdP.
Retrieve & filter
We search the in-perimeter index, then drop any chunk the user is not permitted to see.
Ground
Only the allowed chunks are passed to the model, which answers strictly from that context.
Cite & log
The answer returns with citations, and the full lineage is written to your audit store.
Generic RAG vs. secure RAG
Why an enterprise-grade retrieval layer is a different build.
| Generic RAG | Secure RAG by Automatic.co | |
|---|---|---|
| Where data lives | Third-party managed index | Inside your VPC, on-prem, or air-gapped |
| Access control | All-or-nothing | Per-user, ACL- and row-level aware |
| Answer trust | Plausible, unverifiable | Cited and traceable to source |
| Auditability | Little to none | Full query and retrieval lineage |
| Model choice | Locked to one vendor | Private endpoint or self-hosted, swappable |
Built for your compliance posture
Whether you answer to SOC 2, HIPAA, or an internal data-residency mandate, retrieval is designed around your boundary from the first conversation — not retrofitted after a breach review flags it.
We deploy into VPC-isolated environments, your own hardware, or fully air-gapped networks where even the inference model lives in-perimeter. The controls map directly to your existing security program.
- SOC-grade access and change controls
- Data-residency and retention guarantees
- Evidence ready for auditors and customers
Frequently asked questions
Does our data ever leave our environment with secure RAG?
No. The vector store, embeddings, and source documents all live inside your VPC, on-prem cluster, or air-gapped network. Only the model inference call leaves — and even that can run on a private endpoint or a model hosted in your own perimeter.
How do you stop the model from leaking documents a user shouldn't see?
Retrieval is permission-aware. We propagate your existing ACLs and row-level security into the index, then filter candidates by the requesting user's identity before anything reaches the model. A document a user can't open in the source system can't surface in an answer.
Can we prove what the agent retrieved and why it answered the way it did?
Yes. Every query, the chunks retrieved, the relevance scores, the user's entitlements, and the final grounded answer are logged with citations. That decision lineage is exportable for SOC 2, HIPAA, or internal review.
Which embedding and inference models do you use?
Whatever your compliance posture allows — Anthropic or other providers over a private endpoint, or fully self-hosted open-source embedding and generation models when nothing may leave the network. We design retrieval to be model-agnostic so you can swap without re-indexing.
Explore the private-AI stack
Secure RAG is one piece of a perimeter-respecting agentic platform.
Ground your agents without giving up your data
Bring a knowledge base and a compliance requirement. We'll map a secure retrieval architecture that keeps both intact.