Enterprise AI · Reference architecture

Leveraging AI for authoring regulatory documents

AI can accelerate how regulated information is extracted, authored, and reviewed. The harder problem is connecting it to the right evidence, workflow, requirements, and accountable people.

Winston Nilumol4 min read

The opportunity

Scientific and regulated teams already create the information needed for decisions and downstream artifacts. Friction appears when people must locate that information across systems, reconstruct its context, and reshape it for a new audience or controlled use.

The central idea

LLMs serve as a component to a broader infrastracture that supports adopting AI. Software needs to enable tightly controlled flow of information and structured outputs.

A useful architecture must improve the business workflow, retrieve the right source information, preserve the applicable regulatory boundary, and produce an output people can verify and use.

A concrete workflow

Consider a regulated professional who needs to turn an existing document into both a cross-functional summary and reusable controlled content. Today, that may require finding the correct document, interpreting its tables and attachments, checking context with colleagues, and manually reformatting the same information for different audiences.

Current information flow
Source documents · Clinical Safety Report-2048
narrative · tables · references · metadata · attachments
    │
    ▼
Human reconstruction
find · reconcile · interpret · summarize · format · review
    │
    ├──→ Stakeholder-ready summary
    └──→ Reviewed controlled document

The same source evidence is interpreted and reformatted for multiple downstream uses.

Why this is difficult

  • Functional: improve how scientists and reviewers work—not simply add another interface.
  • Source information: identify which documents, records, attachments, and versions are authoritative.
  • Technical: handle APIs, identity, permissions, mappings, provenance, and failures explicitly.
  • Regulatory: apply controls based on intended use and the relevant requirements.

A controlled AI pattern

The reference architecture starts with a document in an existing system of record. The application authenticates the user, establishes permissions and the task boundary, then coordinates source extraction, business logic, regulatory context, model calls, and qualified review.

Document-to-output reference architecture
Controlled AI architecture for transforming a source document into reviewed outputsA user request and document system of record feed a controlled application boundary. Authorized context supplies templates and requirements. The workflow retrieves sources, applies logic, orchestrates model calls, and retains provenance before human review produces two evidence-linked outputs.Controlled Application BoundaryIdentity · permissions · task boundary · auditabilityUser RequestNatural language prompt + Document IDSystem of RecordDocumentcontent · metadata · tables · attachmentsAuthorized ContextTemplates · example documentsgold standards · terminologyapplicable regulatory requirements1. Authenticate & Define ContextUser · permissions · intended use · accessible records2. Retrieve & Extract Source EvidenceResolve Document ID · retrieve authorized content · retain provenance3. Apply Business & Regulatory LogicDocument boundaries · mappings · required contentDocument structures · templates · formatting rules4. Orchestrate LLM CallsSummarize · transform · draft · check5. Assemble Evidence-Linked DraftsStructured output · claim-to-source links · audit recordAI Platform · LLMTask-specific model callsNo direct system-of-record accessQualified Human Review & DecisionVerify · revise · approve · rejectStakeholder OutputDecision-ready summaryTechnical OutputReviewed controlled documentAPI retrievalcontrolled contextprompt + evidencemodel response

The application—not the model—controls access, source retrieval, business logic, regulatory context, provenance, and the handoff to accountable reviewers.

The flow can remain read-only during an initial proof. If an approved result later needs to be written back to a controlled system, that bidirectional path would require explicit status mapping, reconciliation, audit-trail behavior, and validation.

Three architecture decisions

  1. Establish trust before generation

    Confirm identity, permissions, record status, version, provenance, and intended use at the application boundary.

  2. Control the workflow, not only the prompt

    Combine extraction, templates, deterministic logic, model instructions, source-linked checks, and qualified review.

  3. Evaluate the task in context

    Measure extraction accuracy, coverage, source support, unsupported claims, reviewer corrections, and appropriate refusal.

How I can help move this forward

My experience across regulated biopharma operations, enterprise scientific software, implementation, and AI-assisted regulatory authoring helps me connect the workflow, technical, and adoption questions surrounding the model.

  1. Discovery

    Map the user, source evidence, current friction, exceptions, baseline, and desired business result.

  2. Design

    Define system boundaries, APIs, authoritative objects, permissions, provenance, transformations, and review.

  3. Validate

    Turn intended use and risk into evaluation cases, acceptance criteria, control evidence, and workflow outcomes.

  4. Repeatability

    Convert field learning into reusable integrations, architecture patterns, evaluation frameworks, and playbooks.

What I would test next

Build one bounded example using experimental data: retrieve an authorized experiment from an ELN by experiment_id, preserve references to its results and attachments, and produce a stakeholder brief plus reviewed technical-report content from the same verified evidence.

Measure the workflow—not the volume of AI output

Compare evidence-gathering time, reviewer effort, right-first-time content, unsupported claims, and time to a usable decision.