Custom AI development for pharma and biotech

Where AI pays off in the lab

AI earns its place where people repeat the same judgment many times a day. These are the projects we build most often.

How custom image analysis software is built, and where machine learning helps →

Private AI for biotech and pharma: your data stays yours

Most AI tools send your data to someone else’s servers. Ours do not have to. We build and train models on your proprietary data inside your own environment, whether that is an on-premises server, a private cloud tenancy or an air-gapped network. Data governance agreements are standard on every project.

That matters twice in pharma: once for protecting R&D data, and again at inspection, when you need to show exactly what data trained the model and where it lives.

Making lab instrument data AI-ready

AI projects in large pharma organizations rarely fail on the model. They fail on the data. Instrument output arrives in different formats, with metadata in file names, spreadsheets and people’s heads. Before any model is trained, that data has to be consistent, attributable and complete.

We start every AI engagement with a data readiness review. We map where instrument data is produced, how it is named and stored, which metadata is missing, and what ALCOA+ gaps would block a validated model. Then we build the pipeline that standardizes it. The output is useful even if the AI project pauses: clean, traceable data your existing reports can use too.

Validating AI in a GxP environment

Can AI-powered lab software comply with FDA and GxP regulations? Yes, when it is designed for it. Regulators expect the same things from an AI system that they expect from any computerized system: a defined intended use, a risk-based validation, controlled changes and a full audit trail. AI adds two more: the model’s behavior has to be explainable, and retraining has to be a controlled change, not a silent update.

We design for this from the start. Models are versioned and locked for release. Every prediction is logged with the model version that produced it. Systems follow a development lifecycle aligned with GAMP 5 principles and the FDA’s Computer Software Assurance guidance, support IQ/OQ/PQ, and are risk-classified against the EU AI Act.

How to navigate AI, 21 CFR and GMP compliance →

Case study: AI-assisted colony counter

Our first case study involved taking an existing piece of TotalLab software, our Phoretix Colony Counter, and using AI and machine learning to remove the manual steps users had to go through to detect colonies within an image:

As you can see above, enhancement of this particular product with AI and ML allowed us to massively reduce user intervention, and therefore the time taken for analysis, and to remove a source of potential inter-operator variability. The result? More reliable results, quicker.

As you can also see above, each detected colony can be interacted with and adjusted, and additional colonies can be manually added after detection, allowing users to fine-tune the analysis if they want to. AI enhancement is provided as a tool, but manual intervention still remains possible.

See Phoretix Colony Counter →

How to choose a custom AI partner for pharma

Four questions separate a pharma-ready AI partner from a general AI agency. Ask them before you sign.

Have they shipped software into a GMP lab? A strong demo on public data says little about whether the result will survive validation. Ask for a regulated deployment you can talk to.

How will the model be validated and changed? Ask to see what a validation package looks like, and how retraining is controlled after go-live.

Can they explain a single prediction? If the vendor cannot show why the model made a specific call, your QA team will not be able to either.

Do they understand the science? An agency that has to learn what a western blot or a colony plate is will spend your budget doing it. Our development teams have built lab image analysis software since Phoretix 1D in 1993.

How an AI project runs

We know pharma and biotech projects have tight governance, defined budgets and real regulatory consequences. Our process is built around that reality.

Step 1: Discovery and scoping. We start by understanding your workflow, your data and your compliance environment. You’ll leave with a clear picture of what’s possible and a proposed approach before any commitment is made.

Step 2: Prototype and validate. We build a working prototype rapidly, so you can see your solution in action early and give your feedback. This keeps you in control of direction and budget, and surfaces any technical or compliance considerations before full build begins.

Step 3: Build and integrate. Full development follows, with regular check-ins, transparent progress, and continuous integration with your existing systems, instruments and data infrastructure.

Step 4: Validation and deployment. We deliver validated, audit-ready software with complete documentation, with IQ/OQ/PQ support available where required. Handover includes documentation and staff training. See what a validation package should contain.

Most projects reach a working prototype in roughly 6 to 10 weeks from scoping, and full production deployment 3 to 6 months after that.

Who we work with

Pharmaceutical companies. From top-10 global pharma to mid-size specialty manufacturers. We help R&D, QC and digital transformation teams build AI that meets their internal governance requirements and external regulatory obligations.

Biotech and CRO organizations. Fast-moving teams who need capable, scalable AI tooling without the overhead of building an in-house software function. We act as your embedded development partner.

Medical device and diagnostics companies. AI-enabled image analysis, decision-support and data management tools built to IEC 62304 and relevant MDR/IVDR standards.

TotalLab is a registered supplier on Scientist.com, making procurement straightforward for R&D teams at large pharma organizations.

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