Pharmaceutical Intelligence

Shaping the Future of AI in medicine manufacturing

AI agents that operate your lab software directly, connecting instruments, running analyses, and delivering audit-ready results. Entirely behind your firewall.

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Air-gapped by default
Local AI models, no data leaves your machine
21 CFR Part 11 ready

Traction

Validated by pilots. Backed by the ecosystem.

4.5 / 5 CPI product-market fit score
5 / 5 Pilot objectives achieved
35+ Lab tool connectors
“Project Wroxham was a great opportunity to get a first look at some cutting-edge AI. IntelligenceQ were fantastic to work with and I’m excited to see where they go next!”
Jess Andrews Technical Lead, CPI UK
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Our Story

Born in the lab. Built for the lab.

IntelligenceQ started when our founder, a formulation scientist with a decade in pharmaceutical R&D, spent more time fighting software than doing science. The same pattern repeated at every company: export data from one tool, paste it into another, reformat for a third, and hope nothing was lost in translation. The institutional knowledge behind every decision lived in someone’s head, never in a system.

We built GyaniMed to fix that. Not a chatbot, not a dashboard. An operating system where AI agents directly control the lab software scientists already use, preserve every decision as queryable institutional knowledge, and deliver audit-ready results without a single copy-paste. Local-first, air-gapped, and built by the people who lived the problem.

Our mission: make breakthroughs compound instead of being forgotten.

Team

Domain experts. Building from inside the problem.

Swapnil Khadke, PhD

Swapnil Khadke, PhD

Founder & CEO

10+ years pharmaceutical R&D. Built GyaniMed end-to-end. PhD, Aston University.

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Advisory Board

Neil Robertson

Neil Robertson

Ex-CTO and Technical Advisor

Software architecture & AI. Designed the GyaniMed engine. Ex-J.P. Morgan. AWS Solutions Architect.

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Dennis Christensen, PhD

Dennis Christensen, PhD

Scientific Advisor

Vaccine R&D, CAF® adjuvant platform. Head of Vaccines, Croda. Research Professor, Statens Serum Institut.

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David Steiner

David Steiner

Technical Advisor

Ex-Validus Risk Management, ex-J.P. Morgan.

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Chris Rushworth

Chris Rushworth

Product Advisor

Enterprise product. Oracle, NHS.

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Priya Kalia, PhD

Priya Kalia, PhD

Communications Advisor

Science media. The Science Tribe podcast. PhD, University of Cambridge.

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What We Built

AI agents for pharmaceutical sciences.

GyaniMed is a domain-specific AI operating system for regulated pharmaceutical R&D and manufacturing. It connects to scientific software, runs on the scientist’s machine, and produces deterministic, audit-ready outputs. Local AI. Local data. No cloud dependency.

Two-Plane Architecture

The semantic plane reasons. The truth plane decides. Every output is deterministic, hash-verified, and evidence-bound.

35+ Protocol Connectors

Model Context Protocol (MCP) connectors to lab instruments, analysis software, electronic lab notebooks, and quality systems.

Air-Gapped by Default

Language models run on-device. Databases are local. No outbound connection required. Cloud features are opt-in only, never opt-out.

Governance Built-In

Human-in-the-loop validation. Scoped agent permissions. Hash-chained audit trails. 21 CFR Part 11 ready.

Impact

The deliverables your team produces, in a fraction of the time.

Every pharmaceutical organisation produces the same critical deliverables: deviation reports, stability analyses, batch comparisons, regulatory submissions. Today, each one requires a scientist to manually operate multiple specialist tools, transfer data between them, and compile the output. GyaniMed compresses that entire workflow into a single agent-driven interaction.

01

Deviation Investigation Report

Weeks → Same day
Without

Scientist manually queries 4–5 systems, cross-references batch records, writes narrative. Weeks of elapsed time.

With GyaniMed

Agent traces knowledge graph, identifies root cause, generates structured report with evidence provenance.

02

Formulation Design Space

Months → Hours
Without

Design of Experiments (DoE) software requires months of specialist training. Scientist runs one tool at a time. Optimisation cycles take months.

With GyaniMed

Agent coordinates DoE modelling, particle sizing, and stability data simultaneously. Maps Critical Quality Attribute (CQA) – Critical Material Attribute (CMA) – Critical Process Parameter (CPP) causality.

03

Batch Comparison Analysis

Days → Minutes
Without

Export data from each batch run, open in separate analysis tools, manually align parameters, spot differences in spreadsheets.

With GyaniMed

Deterministic parameter comparison across the canonical study substrate. Every delta surfaced, every value hash-verified.

04

Regulatory Submission (Chemistry, Manufacturing and Controls Section)

Weeks → Hours
Without

Weeks of manual document assembly from multiple sources. CMC sections copy-pasted between systems.

With GyaniMed

Agent generates CMC documentation directly from the knowledge graph with full traceability to source data.

05

Technology Transfer Package

Months → Weeks
Without

6–12 months. Knowledge lives in people’s heads and scattered files. Critical process understanding lost in translation between sites.

With GyaniMed

Knowledge graph preserves institutional expertise. Transferable, queryable, complete. Process understanding travels with the data.

The Bottleneck

Your scientists are spending more time on software than on science.

Every instrument comes with specialist software that takes months to learn. Once trained, the work is still manual: export from the instrument, open the analysis tool, copy the outputs, paste into a report. Each step is a context switch and a chance for error. Senior scientists end up buried in operational overhead instead of doing science.

Months of training per tool

Every instrument comes with specialist software that takes months to learn. A scientist trained on one platform cannot simply pick up another. The result: expertise bottlenecks and overloaded senior staff.

Knowledge trapped in people, not systems

When your most experienced scientist retires or transfers, their hard-won expertise leaves with them. Which formulation parameters matter, which process settings work: it was never captured anywhere a colleague could find it.

Cloud AI cannot enter the building

Pharmaceutical intellectual property cannot be sent to external servers. Generic AI tools assume internet access. In regulated environments with air-gapped networks and compliance requirements, they are architecturally incompatible.

The Gap

The tools exist. The coordination doesn’t.

Your lab already runs world-class instruments and best-in-class record-keeping systems. The problem is not the tools themselves: it is that none of them can operate the others. Each system is an island. GyaniMed is the bridge.

Record-Keeping

Electronic Lab Notebooks, LIMS, QMS

Excellent systems of record, but they cannot run an analysis, trigger an instrument export, or execute a Design of Experiments (DoE) workflow. They store results. They do not produce them.

High IP Risk

Cloud AI Platforms

Powerful reasoning capabilities, but they cannot access your instruments on-premises. Every prompt sends proprietary formulation data through external servers. Incompatible with air-gapped regulated environments.

Search, Not Action

Retrieval & Chat Tools

They search your documents. They do not operate your software. Finding a protocol and executing it are fundamentally different capabilities.

Get Started

Ready to give your lab an operating system?

Request access. Connect your tools. Ask your first scientific question.

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References

Primary sources for standards and programs

Links to the official programs and regulations mentioned on this page.