PLAYBOOK · LIFE SCIENCES

A regulatory submission assistant built on Living Knowledge.

Regulatory submissions stitch together CMC, clinical, non-clinical, and labeling content. This playbook puts every document into a Living Knowledge graph and lets specialist agents draft and validate each module against agency guidance — all without sending data outside.

Living KnowledgeSpecialist AgentsValidation LoopsSEEMR
VDF Data Overview for life sciences
The problem

Submissions are knowledge graphs in disguise

An eCTD module references batches, studies, methods, and labels. Authoring teams reassemble that web by hand every cycle.

The VDF AI approach

One Living Knowledge graph, many specialists

Indexed documents, extracted entities, and relationships sit in a graph. Specialist agents draft each module; a validator checks consistency. Authoring leads review and approve.

REFERENCE ARCHITECTURE

Graph in, draft modules out

CMC · Clinical · Labeling Docs
Living Knowledge Graph
Entities · Relationships · Vectors
Section Drafter Agents
CMC Specialist
Clinical Specialist
Labeling Specialist
Consistency Validator
Submission Network
Intent: draft-module
Draft sections + citations
PLAYBOOK · STEP BY STEP

From document pile to draft submission

1

Ingest CMC, clinical, and labeling content

VDF Data extracts entities (batches, studies, methods, products) and relationships into the knowledge graph alongside the vector index.

2

Define specialist agents

Each specialist owns a sub-area of the submission and follows a strict outline aligned to agency templates.

3

Wire validation loops

A Consistency Validator checks batch numbers, study identifiers, and dosing across drafted sections, flagging mismatches before the authoring lead sees them.

4

Author and approve in the Portal

Each drafted section ships with citations to the source documents and to the agency guidance it satisfies.

5

Operate under audit

Live Execution Monitoring stores every decision. SEEMR routes heavy reasoning to your high-capability private model.

Submission network execution monitoring
OUTCOMES

Submission cycles compress, quality holds

−35%

authoring cycle time per module.

+50%

cross-section consistency issues caught pre-review.

0

proprietary CMC or clinical content leaves the perimeter.

SEEMR REFERENCE

The graph keeps learning across cycles

SEEMR's Knowledge Graph mode incorporates every approved section as a future retrieval signal. Subsequent submissions start with stronger context.

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You Have Questions

Tell us what you’re trying to achieve—governed AI Networks, enterprise RAG, deep integrations, or on‑premise deployment. We’ll help you map the right architecture, security posture, and rollout path. If you’re moving beyond AI pilots and need scalable, auditable execution, reach out—our team is ready to help.