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Cambridge produces more validated science than it can productise. The bottleneck is almost never the discovery — it is the data infrastructure, the traceability and the engineering discipline that turn a result into something a regulator, a partner or a customer can rely on.
The Cambridge landscape
Biomedical research, pharmaceutical development and deep tech sit within a few miles of each other. The shared problem is provenance: knowing exactly where a number came from, and being able to prove it later.
Clinical services, research institutes and industry sit together, which is scientifically productive and governance-heavy. The same dataset may be usable for care, for approved research and for nothing else — and the system has to enforce that distinction, not merely record it.
Software supporting regulated development inherits regulated expectations: attributable, contemporaneous records, validated changes and audit trails that survive an inspection years later. We build to that from the outset because retrofitting it is close to a rewrite.
Cambridge produces a steady flow of companies built on a genuine technical advance and very little production engineering. The first serious build is usually about making the advance repeatable rather than adding features.
We take provenance seriously. In practice that means immutable audit trails, versioned datasets and analysis code, environments that can be rebuilt exactly, and validation evidence produced during development. It is slower in month one and dramatically faster at the point where someone asks how a number was produced.
Research-grade data engineering, built to be inspected.
Capture, validation and analysis pipelines with full provenance, versioned datasets and reproducible environments.
Learn more →Model development on biological, imaging and instrument data, with reproducibility and evaluation held to a scientific standard.
Learn more →FHIR interfaces to clinical systems and connectors to laboratory instruments, with reconciliation you can audit.
Learn more →Requirement traceability, validation evidence and controlled change for software supporting regulated development.
Learn more →Taking a proven method to a supportable product — architecture, tenancy, deployment and the operational surface a prototype lacks.
Learn more →Sector focus
Three variations on the same underlying requirement: provenance.
Data capture and analysis infrastructure where every value must be attributable, timestamped and defensible years later.
Typical start: data provenance review, 3-4 weeks
Systems supporting regulated pipelines, with validation evidence and controlled change produced during the build.
Typical start: validation planning, 4 weeks
Making a technical advance repeatable, deployable and supportable by someone other than its inventor.
Typical start: technical due diligence, 2-3 weeks
Compliance posture
Regulated research has a long memory. These practices are what make a question three years from now answerable.
Seeking basic information? Our FAQ section is a ready reckoner with precise answers to the most probable queries.
Bring us the data problem behind the result. That is usually the real blocker.
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