Inference on your hardware. Index on your server.
ContextSage has no access to any of it.
Attorney-client privilege enforced by architecture, not a privacy policy.
What practitioners who've built this at scale say
Engineers who've built RAG for the Air Force, hedge funds, and 25-million-document corpora say the same things. We listened and built accordingly.
"You'll spend a LOT of time on chunk size, overlap, top K, ranking, and reranking. You won't get this right on the 1st go — or the 10th."
ContextSage ships a legal-tuned retrieval pipeline. Cross-encoder reranking trained on 500+ CUAD contracts. No tuning required.
"The #1 requirement is to have it on site. No data leaves the firm."
Inference runs in your browser tab via WebGPU. Your documents never touch our servers. Subpoena us — we have nothing.
"Sometimes when I ask about a certain customer, it can't find the chunks that are about this specific person."
Per-matter isolation + entity-aware reranking surface the right document, not a plausible-sounding one from a different case.
"85% of their data is in a pretty crappy state — especially the metadata."
Aspose-powered OCR and DocumentProcessor normalize scanned PDFs, remove running headers, and clean metadata on ingest.
"RAG quality needs to be nurtured. Bad data causes hallucinations and confusion."
Policy engine enforces grounding before the answer renders. If no chunk covers the question, the model says so — it doesn't invent.
"Security is consistently pushed to the bottom of the list… later never comes around."
Zero-trust by default. Per-matter isolation, PHI gatekeeper, and hallucination audit log ship on day one — not after launch.
Custom RAG builds for a single law firm run $25k–$72k plus $1–2k/month maintenance. ContextSage ships the same architecture, pre-tuned for legal, for a fraction of that.
WebLLM uses your GPU via WebAssembly. Inference happens in the browser tab. Nothing is sent to ContextSage servers.
Connect a local Ollama instance, a VPS, or a RunPod GPU pod. Pro runs a personal bridge. Teams share a firm bridge. You own the server.
We hold no client data — not encrypted on our servers, not on our servers at all. Subpoena us and we have nothing to hand over.
Documents, searches, and conversation history are siloed per matter. No cross-matter bleed. Call it a matter, a patient file, a study — your call.
Every document you ingest becomes part of your practice. Depositions, contracts, case notes — all searchable. The index grows with your work.
Every prompt is inspected before any cloud call. Sensitive content stays local or is redacted before leaving your network.
Google Drive, Gmail, OneDrive and more reconnect automatically. Silent token refresh — no re-auth interruptions mid-work.
When RAG mode is on, the model identifies which passages are relevant and assesses whether they cover the question before drafting an answer. Gaps in your documents are acknowledged — not filled from training knowledge.
The whole team queries the same index on your firm's server. One bridge, shared across devices, built on hardware you control.
A policy engine validates every [source] citation against the actual retrieved chunk before the answer renders. Fabricated or mismatched citations are flagged automatically — not caught after the fact.
Every time the model answers from training data instead of your documents, or cites a passage it never saw, the query and response are written to an inspector queue. Compliance has a timestamped record.
Before you ask a question, know what the document contains. Every ingested contract is scanned for 136 clause types. High-confidence finds, uncertain matches, and absent provisions are surfaced in a structured report — not buried in an answer.
Standard clause models are trained on generic corpora. Your M&A practice, your IP portfolio, your outsourcing agreements have their own language. We tune the retrieval model to your firm's document corpus — so the system finds the clauses your attorneys actually look for.
Most AI tools ask you to trust their privacy policy. ContextSage removes the need for trust entirely.
WebLLM runs inference in your browser using your GPU. The Bridge connects your local Ollama or a RunPod pod you spin up and control. ContextSage has no compute cluster. Your documents never transit our servers — there is no route for them to take.
Attorney-client privilege is not a feature you configure. It is enforced by the fact that we hold no client data. Not encrypted on our servers. Not on our servers at all. A court order directed at us would produce nothing because we have nothing.
Every deposition, contract, and case note you ingest becomes part of your practice's index. That index grows with your work. Switching to another tool means re-ingesting everything — months of work product, rebuilt from scratch. That cost is a feature, not a trap: it means your index is genuinely valuable.
Every other AI tool asks the model to behave. ContextSage enforces it at the pipeline level. A policy engine sits between your documents and the LLM — validating retrieval, enforcing citations, and logging every deviation before anything reaches your screen.
When the AI cites a document, a finite state machine verifies that citation against the actual retrieved passage before the answer reaches your screen. Invented source? Flagged. Wrong page? Flagged. The model cannot fabricate a citation and have it pass silently.
If nothing in your uploaded documents answers the question, the system says so. Not a guess from training data dressed up as a finding. The retrieval pipeline knows the difference between "not in this document" and "not in any document" and routes each correctly.
Every time the model tries to answer from training knowledge instead of your documents — or cites a passage that was never retrieved — the query, the response, and the retrieved chunks are written to an inspector queue. Compliance has a record. Nothing is silent.
The policy engine tracks which retrieval strategies succeed on your firm's document corpus. When accuracy degrades or new patterns emerge, it designs a candidate policy, tests it quietly against 10% of live traffic, and promotes it only if it outperforms the incumbent. Your index gets smarter without you touching configuration.
Why this matters for regulated industries. A hallucinated citation in a brief is a malpractice exposure. A wrong drug interaction sourced to training data is a patient safety risk. The policy engine does not reduce hallucination rates — it makes hallucinations detectable, logged, and structurally harder to produce in the first place.
Every RAG tool on the market gives the model instructions: cite your sources, say you don't know when you're unsure, stay grounded in the documents. The model may or may not comply. If it doesn't, you find out when a lawyer cites a hallucinated case.
| Prompt-only approach | ContextSage |
|---|---|
| System prompt says "only use the provided context" | FSMGuard retrieves content, then validates every citation against the actual retrieved chunks after the response is generated — not before |
| "I Don't Know" exit phrase written into the system prompt | Detection fires when the model was instructed to abstain but gave a substantive answer anyway — catches non-compliance regardless of what the model chose to do |
| No visibility when the model ignores its instructions | Every violation written to an audit log with the query, the full response, the retrieved content, and the flag reason |
| Static prompt — no improvement when it fails | Violation scores feed the policy engine. Policies that produce fewer violations automatically replace ones that don't |
The model can ignore a system prompt.
It cannot ignore the validator.