The problems you already know about
Regulated industries cannot use generic LLMs over critical content. They can use grounded retrieval done properly. The difference is in how the system is built.
Critical knowledge is locked in PDFs and silos
Clinical protocols, regulatory filings, contracts, policy manuals, technical standards. The most important documents in regulated industries are also the hardest to search. Subject matter experts spend hours finding what they need.
Retrieval indexed over the document formats that actually matter (PDF, scanned images, structured filings, internal CMS) with semantic search that understands context, not just keywords. Experts find the answer in seconds with the source attached.
Generic LLMs are unsafe over regulated content
Public LLMs make things up. They cite sources that do not exist. They cannot tell you which version of a policy applies. In regulated work, a wrong answer is not just embarrassing; it is a legal, clinical, or compliance event.
Grounded retrieval that refuses to answer when the source documents do not support the claim. Every answer cites the exact paragraph it came from. Confidence thresholds escalate to human review for high-stakes queries. The system says "I do not know" when that is the correct answer.
Permissions are non-negotiable
Different users see different content based on jurisdiction, role, clearance, or contract. A research scientist sees one slice; the compliance officer sees another. The AI cannot ignore that distinction.
Permission-aware retrieval at the document and section level. The AI sees what the user is allowed to see, replicated from your existing access controls. Cross-jurisdiction queries respect cross-jurisdiction rules.
Auditors need to see what the AI did and why
Regulators ask: how did this AI reach this answer, what did it consider, and who reviewed it. Generic AI tools give you outputs without a defensible record of how they were produced.
Every retrieval is logged with query, retrieved sources, ranking scores, generation prompt, model output, and any human review. Auditors can trace any AI-produced answer back to the underlying evidence. The audit trail is the deliverable, not an afterthought.
What results look like
These are the improvements our clients typically see within the first 3 months.
How it works
We design retrieval around your governance model
Document classification, permission tiers, retention requirements, citation standards. The retrieval system reflects your real compliance posture, not a generic baseline.
We build the eval harness before we ship the answer
Subject matter experts contribute test cases (the questions that matter, with the answers they expect). The system is graded against that harness before it goes live, and continuously after.
You get a system regulators understand
Process documentation, decision logs, eval reports, escalation paths, and human-review checkpoints. We have walked auditors through this exact stack. The system passes review because it was built for review.
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Common questions
Can the AI confidently say "I do not know"?
Yes, and this is critical. We engineer the system to abstain when retrieval does not support a confident answer. Generic LLMs are tuned to always produce something; ours is tuned to refuse when the evidence is not there. Refusal rate is one of the metrics we track and optimise.
Will this work over scanned documents?
Yes. We use OCR plus layout-aware extraction (table-of-contents, section headers, footnotes preserved) so retrieval works over scanned PDFs, contracts with handwritten amendments, and other non-text formats. Quality depends on scan quality, but we work with it; we do not require pristine source documents.
Can we host this on our own infrastructure?
Yes, when required. We build deployments on cloud providers your security team has approved (AWS, Azure, GCP) including with VPC isolation and customer-managed encryption keys. For the most sensitive deployments we support fully on-prem or air-gapped configurations using open-weight models. We do not require sending your data to consumer AI APIs.
Which industries have you done this for?
Healthcare and clinical operations (clinical protocols, drug labelling, medical device documentation), legal and contracts (matter management, contract review, discovery), financial services (policy documents, regulatory filings, risk frameworks), and engineering standards (technical specifications, compliance documents). The retrieval pattern is similar; the governance and citation standards differ.
How do we measure that the AI is actually accurate?
Three layers. First, subject-matter experts contribute a test set of questions and expected answers; the system is graded against this set continuously. Second, citation completeness is automatically verified (every claim must have a source). Third, periodic human review samples live queries to validate that AI behaviour holds up in production. You see all three in a quality dashboard.
Production-grade RAG, built for review.
Book a free 15-minute call. We will scope which knowledge base in your organisation is the right starting point.