Building the tools for Pharma 5.0
I build free, open-source tools that make Pharma 5.0 accessible to every lab, quality team and finance office. And I help pharma and biotech companies deploy AI locally: models trained on their own proprietary data and built to keep that data on their premises, verified on the hardware they run on.
Originator of the Pharma 5.0 thesis: Pharma 4.0 digitized the work. Pharma 5.0 makes humans and machines accountable to each other.
Working with leading pharma and biotech
From digitization to collaboration
Pharma 4.0 was digitization. It moved paper to systems, batch records to electronic logs, manual review to dashboards. Necessary work, and still unfinished. The records are digital, yet many of the calculations and checks that decide things still happen in spreadsheets and by hand, where a number can be wrong in ways that look right.
Pharma 5.0 is human and machine collaboration, with accountability on both sides. It can only be built on work that is already digital and defensible. So the path has two steps: finish Pharma 4.0 with open tools anyone can use, then bring AI into the workflow on your own infrastructure, with experts making every consequential decision.
Free, open-source tools for Pharma 5.0
Access to Pharma 5.0 should not depend on budget. I am committed to building a series of free, open-source tools that finish the work of Pharma 4.0: deterministic, transparent, and running in your browser or on your own computer, with nothing you type leaving your machine.
Ligant bench tools
Four browser tools for cell and gene therapy labs: an Antigen Density Calculator, a Molarity Converter, an Antibody Titration Planner and a Dilution Planner. Each states its assumptions and refuses to return a number the inputs do not support.
ABM Invoice Analytics
A desktop app that checks every invoice against everything billed before and flags the duplicates. It runs entirely on your computer, and every flag shows exactly why it was raised.
ABM Local AI
A private AI server for your own computer. It downloads, verifies and runs models so ABM’s free apps can use them, and it listens only on your machine.
Private AI, trained on your data, on your premises
For work that cannot leave the building, I help pharma and biotech companies build and deploy their own AI, trained on proprietary data and running on their own infrastructure. Every use case starts with a declared context of use and a declared status: critical or non-critical.
The right model for the risk
Frozen, deterministic models for critical use, with determinism measured on the deployed environment rather than assumed. Small language models only for non-critical work such as summaries, under continuous human oversight, and never on the critical path. Numbers come from the deterministic tool, not the model.
Your data, your model, your premises
Models, reference data and workflows installed on your own servers and workstations. That data stays inside your environment is verified at the network level on the deployed hardware, not inferred from a configuration file. A model trained on your data stays in your environment and never informs another engagement.
Validation evidence for regulated use
I produce the validation evidence against Annex 11 and your own CSV/CSA framework, designed for readiness under the draft Annex 22 on artificial intelligence, with test data kept independent of training data. Your quality unit executes and approves it.
Coaching for IT and QA
Working alongside IT and quality teams on what validation means for an AI system, and on where to narrow what a model is permitted to decide.
From requirement to local app
Your requirement
Bring the workflow you want to improve, the data it touches and the constraints that matter. Together we declare its context of use and whether it is critical or non-critical.
URS, drafted and approved
My AI agents, running on local AI, draft the User Requirements Specification. Your subject-matter expert reviews and approves it. Nothing is built until they do.
Built and delivered
I build the local app to the approved URS and deliver it onto the GPU hardware we set up for you, inside your own environment.
Deployed for your team
The app goes live locally for everyone who needs it, with validation evidence for your quality unit to execute and approve, and change control for every update.
Bring me your questions
Whether you’re curious about Pharma 5.0, working out how to validate an AI system in a regulated environment, or wondering what local AI can already do for pharma and biotech teams today, I’d like to hear from you.
Pharma 5.0 · Validating AI in GxP · Local AI for pharma and biotech
You don’t need a project in mind. Send a question, an idea, or the problem you’re working through, and I’ll reply personally. The more people who understand what’s possible, the faster this industry moves.