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.
Design grounded retrieval to abstain when the source documents do not support a claim, attach passage-level citations, and escalate high-stakes or low-confidence queries for human review. Test refusal and citation behaviour explicitly before release.
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 to require from a regulated-knowledge pilot
Measurement standards, not client averages or guaranteed results. Acceptance thresholds must be agreed with subject-matter, security, and compliance owners.
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.
Prepare the evidence reviewers need
Process documentation, decision logs, evaluation reports, escalation paths, access controls, and human-review checkpoints give your compliance and assurance reviewers a concrete basis for assessment.
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Common questions
Can the AI confidently say "I do not know"?
Abstention can be designed and tested, but it is not guaranteed by prompting alone. Define unsupported and ambiguous cases in the evaluation set, set retrieval and answer thresholds, route high-stakes uncertainty to people, and track both incorrect answers and unnecessary refusals.
Will this work over scanned documents?
It can, using OCR and layout-aware extraction, but quality depends on scan resolution, handwriting, tables, annotations, and document consistency. Test representative documents first and route low-quality or low-confidence extraction to review rather than assuming all scans are suitable.
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. How we scope evidence, controls, and accountability is described on our trust, safety and governance page.
Where can this retrieval pattern apply?
The pattern can apply to clinical protocols, legal and contract material, financial policies and filings, and engineering standards. The governance, validation, hosting, access, and citation requirements differ materially by domain, so feasibility and review requirements must be established with the relevant subject-matter owners. Policy wordings and claims material on the broker side are covered in AI for insurance brokers, and accounting standards and firm policy material in AI for accountants and accounting firms.
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.