Pharma-grade AI workflows for the work trapped in inboxes, PDFs, and legacy dossiers.
RegulaVita helps life sciences teams transform unstructured medical information requests, pharmacovigilance inbox traffic, SmPCs, and Module 3 CTD documents into structured, reviewable, audit-ready outputs.
Live workflow console
PV Inbox Responses
Classifies inquiry type, product, country, and triggers.
Searches approved source libraries and literature.
Creates a structured response with traceable evidence.
Routes output to human-in-the-loop decision points.
Grounded in real regulated workflows
Built around the shape of actual pharma work, not a generic chatbot wrapper.
The product language from the original site is still here: PV inbox intake, researched responses, IDMP extraction, RIM migration, secure deployment, and validation readiness. The redesign makes those claims specific and inspectable.
429 emails containing 433 individual records since go-live.
Sustained daily adoption across the operating team.
Qualitative, quantitative, packaging, and language fields.
P.1, P.3.1, P.7, and S.2.1/R.3 workstreams.
Modular platform
Switch on the workflows you actually need.
The safest commercial framing is modular: case intake and response generation are related, but they solve different operational problems and can be priced, validated, and governed separately.
Drug safety operations
PV Inbox Case Intake
Digitalise incoming cases from inboxes and unstructured attachments into predefined data categories for downstream review.
AI reads email bodies, PDFs, Word files, logs, images, and other supporting material, then maps the content into pharmaceutical-specific schemas with coding, redaction, classification, and human-in-the-loop review as defined by corporate guidance.
The live MI workflow has handled hundreds of inquiries while preserving structured fields for later audit and trend analysis.
Interactive workflow
From manual inbox drag to governed AI throughput.
Toggle between the legacy operating pattern and the RegulaVita augmented version. The goal is not replacing medical judgement; it is removing the repetitive conversion, search, formatting, and routing work around it.
Email, PDF, Word, image, logs
Pydantic-style schemas and vocabularies
SmPCs, PubMed, EMBASE, Cochrane, internal data
Risk-based checkpoints and reviewer edits
Decision trail, references, and structured output
Country-product hotspots and unmet need trends
Savings model
Explore the economics using the original operating assumptions.
The prior model used 8.3 hours and about $33 per manually processed request, compared with under 5 minutes and under $3 for AI-assisted processing. Adjust monthly volume to see the order of magnitude.
Trust architecture
Regulated AI needs visible control, not magic.
The system is presented as validation-ready, configurable, and reviewable. That is the more credible posture for GxP environments than pretending the model itself is the compliance programme.
Schema first
Structured output models are defined before automation, so the system produces auditable records instead of loose AI prose.
Human governed
Critical decision points stay with trained reviewers, with AI actions captured in traceable review files.
Source controlled
Literature and response generation can be restricted to approved medical databases, SmPCs, and internal source libraries.
Deployable by sensitivity
Cloud API, private Azure, and on-prem options match the sensitivity of public SmPCs, confidential quality data, or patient-linked material.
Fastest start for low-sensitivity public documents.
Useful where confidential product data must stay in a controlled tenant.
Reserved for high-sensitivity environments with strict network boundaries.
- 1Scope the workflow, source systems, SOPs, and review thresholds.
- 2Define schemas, controlled vocabularies, and expected output formats.
- 3Run a bounded sample against real documents and human-reviewed gold data.
- 4Deploy the MVP through secure email forwarding, API integration, or private cloud.
- 5Measure throughput, cost, reviewer edits, and signal value before expanding modules.
Research anchors
Framed around the standards and platforms buyers already know.
Validation-ready demo
Show potential customers a system that feels as serious as the problem it solves.
Discuss PV inbox automation, medical information response generation, IDMP extraction, or a scoped compliance workshop around your existing documents and SOPs.