Chat with your documents, inside your tenant
Single-corpus document chat that stays inside your environment. Ideal for legal matter files, M&A data rooms, internal knowledge bases, or research libraries — the data goes in, the answers come out, nothing leaves your tenant. Citations link back to the source document, every time.
What You Get from a Private RAG Deployment
Six outcomes companies see when they move document chat off vendor “chat with files” products and onto a private RAG stack tuned for their corpus.
Real-World Document Ingestion
PDFs (including scanned), Word, PowerPoint, Excel, tables, footnotes, multi-column layouts, and OCR — the messy real-world inputs vendor RAG products quietly drop chunks of.
Hybrid Retrieval, Tuned to Your Corpus
Hybrid search (BM25 + vector), cross-encoder re-ranking, query rewriting, and multi-query fanout — tuned on your corpus and real questions, with recall@k measured on a labeled eval set.
Grounded, Cited Answers
Every claim links back to the source paragraph in the original document. Refuses gracefully when the corpus doesn't have the answer. Hallucinations drop an order of magnitude vs naive RAG.
Self-Hosted in Your Tenant
Ingestion, embeddings, vector store, and generation all run in your VPC, on-prem, or air-gapped. Document content never leaves your environment to be embedded or indexed by a third party.
Bring Your Own Embedding + LLM
Choose your embedding model (hosted or self-hosted) and your generation model (OpenAI / Anthropic / Gemini, or self-hosted Llama / Mistral / Qwen) per corpus and per team.
Per-Corpus Access Control
Each corpus gets its own access policy mapped to SSO group membership. Legal, M&A, HR, and research libraries are walled off by default. Full audit trail of every query and retrieval.
Why Vendor “Chat With Your Docs” Products Miss
Vendor “chat with files” products (Anthropic Files, ChatGPT Projects, Glean RAG, Hebbia, Harvey, and vendor copilots) were built around a single assumption: the median customer has a clean, modest-sized corpus and is fine letting their documents sit in the vendor’s multi-tenant cloud. That works for a personal knowledge base. It stops working the moment the corpus is large, sensitive, or messy enough that out-of-the-box retrieval starts missing answers — or the moment legal and compliance ask where the documents and embeddings actually live.
A private RAG deployment is the open-source path.
Ingestion + embeddings + vector store + generation as one tuned pipeline. Same chat-with-citations UX, plus a retrieval pipeline tuned on your corpus, BYO embedding and BYO LLM, per-corpus access controls, and a full audit trail — except it runs in your tenant on infrastructure you already own. Documents stay in your tenant, answers come back cited, and your compliance team gets the audit trail they need.
The 8 Capabilities We Build
Eight capabilities your private RAG stack delivers — every part of the chat-with-documents pipeline running inside your tenant. Documents in, citations out, nothing leaves your environment.
Ingestion for messy real-world documents
PDFs, Word docs, PowerPoint, Excel, scanned images, emails, OCR’d contracts, transcripts, code, and HTML — parsed, chunked, and normalized into a clean retrieval index. We handle the parts off-the-shelf RAG products quietly skip: tables in PDFs, embedded images, footnotes, multi-column layouts, and redaction-friendly OCR for legal and clinical content.
Embeddings generated inside your tenant
Choose your embedding model — OpenAI text-embedding-3 via your enterprise contract, Cohere or Voyage, or fully self-hosted (BGE, Nomic, E5, Stella) running on GPUs inside your perimeter. For sensitive corpora the embedding pass never leaves the environment, and your vectors don’t end up in someone else’s multi-tenant index.
Self-hosted vector store sized for your corpus
pgvector (when Postgres is the right answer), Qdrant, Weaviate, or Milvus deployed in your tenant — sized to your document count and query volume, with the ANN index tuning that keeps p95 latency under 500 ms. Hybrid search (BM25 + vector) by default, because pure-vector retrieval misses keyword matches more often than vendors admit.
Retrieval that actually returns the right chunks
Hybrid search, cross-encoder re-ranking, query rewriting, and multi-query fanout where it pays off. We tune the retrieval pipeline on your corpus and your real questions — not the vendor’s median customer’s — and we measure recall@k on a labeled eval set so improvements are real, not vibes.
Grounded answers with inline citations
Every claim in the model’s answer links back to the source paragraph in the original document. No hallucinated facts, no opaque “the AI said it” responses. The system prompt enforces “answer only from retrieved context” and refuses gracefully when the corpus doesn’t have the answer.
BYO-LLM for the generation step
Plug in OpenAI, Anthropic, Gemini, or AWS Bedrock via your enterprise contract for premium quality — or route to self-hosted Llama, Mistral, Qwen on vLLM / Ollama for sensitive workloads and cost control. Per-corpus and per-team routing rules, A/B model evaluation, and a unified token-usage and cost dashboard.
Air-gapped, on-prem, or VPC deployment
The full RAG stack — ingestion, embeddings, vector store, generation — runs in your VPC, on-prem, or fully air-gapped. One Kubernetes namespace or Docker Compose stack. For air-gapped deployments we pair the pipeline with self-hosted embeddings and self-hosted LLM serving, so no document, vector, or prompt ever crosses your perimeter.
Per-corpus access control and full audit
Each corpus gets its own access policy mapped to your SSO group membership — legal matter rooms, M&A files, HR documents, research libraries are walled off by default. Full audit trail of who queried what, which documents were retrieved, which model answered, and which citations were returned. The pack your CISO and regulator both expect.
Talk to a Private RAG Expert
Bring us your corpus mix (PDFs, Word, scanned docs, custom systems), your document count, your sensitivity profile, and the kinds of questions your users actually ask. We’ll come prepared with the right ingestion shape, embedding choice, LLM routing, and a directional read on retrieval quality you can hit in your tenant.
Book a Strategy Session →When You Need a Private RAG Deployment, Not Vendor Chat-With-Files
Anthropic Files, ChatGPT Projects, Glean RAG, and vendor “chat with files” features cover the median user well — small corpora, generic questions, vendor-hosted everything. That’s enough if your documents aren’t sensitive and your corpus is clean. But teams winning on document chat need things vendor RAG can’t deliver:
A private RAG deployment is the open-source path. Build it once for your corpus, tune it on your questions, and your document chat is a capability you own — with the accuracy, audit, and access controls vendor “chat with files” products structurally can’t deliver.
Frequently Asked Questions
Vendor products (Anthropic Files, ChatGPT Projects, Glean RAG, Hebbia, Harvey, etc.) ship a single retrieval pipeline tuned for the median customer. They work well on clean corpora and generic questions. A private RAG deployment runs in your tenant, gets tuned for your documents and your questions, lets you swap embeddings or the generation model when it pays to, and gives you the audit and citation trail your compliance team needs. The difference shows up the moment your corpus is large, messy, or sensitive enough that out-of-the-box ranking starts missing answers.
Anywhere the corpus is private and the answers need to be cited. Common patterns: legal matter files and contract review, M&A data rooms, regulatory and policy libraries, clinical guidelines and SOPs, research and patent libraries, internal knowledge bases, historical board materials, customer-support knowledge bases, and engineering / architecture documentation. We’ve shipped private RAG for corpora from a few hundred documents to several million.
Depends on language coverage, domain, and whether the embedding pass can call out to a hosted API. For English-only general corpora, OpenAI text-embedding-3 or Voyage usually leads. For multilingual or domain-specific (legal, biomedical, code), BGE-M3, E5-Mistral, or fine-tuned domain embeddings often beat the generalist hosted models — and they’re self-hostable, which matters when the corpus is sensitive. We benchmark on a labeled slice of your corpus before locking the model.
Three things together. (1) Retrieve aggressively: hybrid search, query rewriting, multi-query fanout, and a re-ranker so the top-k chunks actually contain the answer. (2) Constrain generation: system prompts that enforce “answer only from retrieved context, otherwise say you don’t know,” plus structured output where it helps. (3) Cite everything: every claim links to a source chunk, and the UI makes citations visible so users (and auditors) can verify. The combination drives hallucination rates down by an order of magnitude versus naive RAG.
Yes. We pair the pipeline with self-hosted embeddings (BGE, E5, Stella, or a domain-tuned variant) and self-hosted LLM serving via vLLM, SGLang, or Ollama on GPUs inside your perimeter. The full data path — ingestion, chunking, embedding, retrieval, generation — runs without an outbound internet connection. We’ve shipped to classified, IL5, sovereign-cloud, and on-prem SCIF environments.
Every engagement includes deployment, an ingestion pipeline for your specific document mix, embedding-model and LLM selection, retrieval tuning against a labeled eval set, SSO / RBAC setup, and a launch playbook. After that, an optional managed retainer covers ingestion of new corpora, retrieval-quality monitoring, model upgrades, and quarterly evaluation reviews. Or you can take it in-house — we hand off the eval set, the IaC, and the runbook either way.
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Learn more →AI Transformation Workshop
Half-day strategy workshop to map your corpus, embedding choice, LLM routing, and private RAG deployment shape.
Book a workshop →AI Strategy Session
30-minute scoping call. We’ll talk through your corpus, document mix, and sensitivity profile, then sketch the right private RAG deployment.
Book a session →AI Consultant vs In-House Team
Honest tradeoffs on bringing a private RAG deployment in-house versus engaging a partner for build + retrieval-tuning + managed retainer.
Read the comparison →Ready to Deploy Private RAG?
A 30-minute strategy call. We’ll walk through your corpus, sensitivity profile, embedding and LLM preferences, and the kinds of questions you expect — then come back with a concrete ingestion shape, retrieval-tuning plan, and rollout sequence.