Life sciences first. Heavy-data sectors second.
Our founder's PhD is in bioinformatics. That's where our deepest experience sits — biotech, pharma, healthcare. Beyond that, we work where the data is complex, the regulatory load is real, and a careful read on AI matters more than a fast one.
Where we have the deepest experience
Life sciences is our lead vertical — biotech, pharma, healthcare. We also work in finance, manufacturing and enterprise where the data is heavy and the regulatory bar is high.
Lead vertical · life sciences
See our life-sciences work in depthBiotechnology
Most biotech AI projects fail at the data layer, not the model layer. We start by auditing what you actually have — genomics, proteomics, assay outputs — before recommending anything. If the foundation isn't there, we help you build it first.
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Pharmaceutical
Drug discovery pipelines generate enormous data volumes with uneven quality. We help you determine which AI applications will meaningfully compress your timelines — and which ones will create new compliance headaches without proportionate ROI.
Key Use Cases:
Healthcare
Healthcare data sits across many systems that don't share well, and most of its value is lost at the join. We help you determine which AI use cases are worth the integration cost — and scope the ones that are.
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Adjacent sectors
Finance
Fraud detection and risk models are only as good as the features you feed them. We've seen many firms train models on data that looked right but reflected historical process failures. We fix the feature set before we touch the model.
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Manufacturing
Unplanned downtime is expensive and sensor data is usually messy. The question isn't whether predictive maintenance is worth it — it's whether your data is clean enough to build on. That's what we find out first.
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Enterprise
Large organisations have the data and the budget, but rarely the internal capacity to distinguish an AI project worth running from one that will drain both. We provide that judgment — before the contract is signed, not after.
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What AI actually changes
Not the sales-deck promises — the four shifts we've actually seen show up in production.
Decision speed
Better-quality decisions taken faster, because the data work is done before the meeting starts — not after.
Cost discipline
Automation aimed at the high-volume, low-variance work that should never have been manual in the first place.
Compressed cycles
R&D, discovery and analysis cycles shortened by removing the wait-states between human judgement calls.
Defensible risk
Predictive models for risks you can already name — built so an auditor, regulator or board can follow how they work.