AI automation for Cambridge businesses.
Full code custom AI for the Cambridge Cluster: deeptech, life sciences, and research.
Cambridge runs on deeptech, life sciences, and research, where the data is sensitive, the technical bar is high, and sending information to a third-party service is rarely acceptable. We are an engineering-first team with five pending UK patents, and we build private, on-device custom AI that keeps research and IP inside your environment.
Built for the way Cambridge works.
Deeptech and life-sciences teams sit on data they cannot send anywhere, and on routine work that consumes expensive technical and scientific time. We build private systems that remove the routine load without the data ever leaving your control.
Biotech and life sciences
For the Cambridge Biomedical Campus and the wider cluster: clinical and research document processing, lab and trial data handling, regulatory submission assembly, and pharmacovigilance. All built private, so research data and unpublished results stay inside your environment.
Deeptech and R&D
For research-driven and hardware-adjacent teams: private retrieval over years of technical documentation, internal research, and specifications, with citations back to source and nothing leaving your infrastructure.
Semiconductors and hardware
For the chip and hardware ecosystem: processing dense technical documents, specifications, and test data at scale. Our own work is built on GPU and heterogeneous compute, so the engineering register is one your teams will recognise.
AI and university spinouts
For spinouts and applied-research teams: production-grade systems that turn a research prototype into infrastructure you can run and own. Five pending UK patents sit beneath the compute architecture we build on.
Healthcare and genomics
For clinical and genomics teams: private processing of clinical letters, genomic and patient data, and research records, with the data residency and audit trail that sensitive health data demands.
IP and professional services
For the IP, patent, and professional-services firms around the cluster: document review, prior-art and disclosure search, and report drafting, kept private because the documents are the value.
Engineering-first, private by design.
For a Cambridge team, the engineering is the decision. We build from first principles, run inference privately or on-device, and keep sensitive research and IP on infrastructure you control. On-device and heterogeneous compute is the core of our patent portfolio, not a marketing badge.
You own the code outright, integrated directly with your data and systems. No per-seat licences, no cloud subscription that sends your research data to a third party.
See the research behind it on our patents and innovations page, and the engineering case in WebGPU for enterprise.
For the private-data architecture, see private AI for UK regulated businesses.
Built to commitment, not to the hour.
A 12-month retainer with a dedicated engineering team, CTO strategy reviews, and monthly delivery sprints. Fixed written scope and fixed price within one week of the discovery call.
From £4,000 / mo
For teams running AI we have already built, or inheriting a system that needs a serious engineering partner.
From £6,000 / mo
A full 12-month delivery pipeline scoped and architected on day one, with capacity for your team’s requests each month.
12-month minimum. Prices exclude VAT. Exact pricing set on consultation. See our services page for the full retainer breakdown.
Relevant to Cambridge teams.
Arithmetic Intensity Explained: The Formula and Why It Predicts GPU Speedup
Arithmetic intensity is the ratio of compute operations to memory bytes moved. The formula, worked GEMM examples, and why it decides if GPU or CPU wins.
Best AI for Accountants (2026)
A sourced comparison of AI for accountants: Karbon, Just Ask Xero, Sage Copilot, Dext, MindBridge, and a private custom build for confidential client work.
Best AI for Healthcare Providers (2026)
A fair, sourced comparison of AI for healthcare admin: Dragon Copilot, Heidi, Tortus, Accurx, your EHR, and a private build for confidential patient data.
The Best AI for Law Firms: Tools Compared, and When to Build Your Own (2026)
A fair, sourced comparison of AI for law firms: Harvey, CoCounsel, Lexis+, Luminance, Spellbook, and a private custom build for confidential matters.
RAG vs Fine-Tuning: Which One Does Your Business Actually Need? (2026)
RAG vs fine-tuning explained: do you retrain the model on your data or let it look it up? When to use each, why you do both, and how data stays private.
What Is a Small Language Model (SLM)? And When It Beats a Big One (2026)
A plain-English guide to small language models: what they are, why a smaller model often beats a frontier one for a task, and how they run on your kit.
Common questions about AI automation in Cambridge.
Do you offer AI automation in Cambridge?
Yes. We build full code custom AI for Cambridge businesses and deliver it remotely from our offices in Newcastle and Dubai, with on-site visits as the engagement needs them. For a technical Cambridge team, what matters is the engineering and the data architecture rather than a local address. We are an engineering-first team with five pending UK patents on our compute architecture, and we build private and on-device systems where sensitive research and IP never leave your environment.
Which Cambridge sectors do you work with?
The Cambridge Cluster is deeptech and life sciences, so the natural fit is biotech and pharma research, semiconductors and hardware, AI and research-driven teams, healthcare and genomics, and the university spinout ecosystem. These are data-heavy, IP-sensitive functions where private, well-engineered AI returns the most and where sending data to a third-party service is rarely acceptable.
How do you protect sensitive research data and intellectual property?
We build private and on-premise systems where the model runs on infrastructure you control and data never leaves your environment. For research data, unpublished results, and proprietary IP, that is usually the only acceptable architecture, because a third-party AI API means your data leaves your perimeter. On-device and heterogeneous compute is the core of our patent portfolio, so the design that keeps your IP private is what we are built around rather than a compromise. We are ISO 27001:2022 and Cyber Essentials certified, and every pipeline is audit-logged.
What does AI automation cost for a Cambridge business?
We work on a 12-month retainer. Existing systems start at £4,000 per month; new-system builds start at £6,000 per month and scale with scope. The retainer buys senior engineering hours against a written scope, not per-seat licences. Exact pricing is set on consultation once the work is sized.
How is this different from ChatGPT or a no-code tool?
ChatGPT is a general assistant and no-code tools are connectors with ceilings. Full code AI automation is custom software, built from first principles, that integrates directly with your data and systems, runs privately on hardware you control, and is owned by you. For a deeptech or life-sciences team with sensitive data and demanding technical requirements, that is the difference between a useful demo and production infrastructure you can stand behind.
Do you have a Cambridge office?
Our offices are in Newcastle and Dubai, and we deliver to Cambridge remotely with on-site visits as needed. We are deliberately honest about this: what de-risks the decision for a Cambridge team is the engineering, the patents behind the architecture, and a private system built to a documented standard, not a serviced-office address.
Bring us your Cambridge problem.
Offices in Newcastle and Dubai. Delivered to Cambridge remotely, with on-site visits as needed.
Book a Discovery Call