AI SYSTEMS & AUTOMATION / CAMBRIDGESHIRE
WHAT IS INCLUDED
WHY CAMBRIDGE
Cambridge audiences are, fairly, sceptical about AI claims, and many local teams know the technology better than most. Our role is usually practical integration rather than research. A company might want its scientists to ask questions across years of internal reports and lab notebooks, with answers citing sources. A grant-funded team might want first drafts of progress reports assembled from existing records. Each needs careful retrieval, access control and honest evaluation.
AI SYSTEMS & AUTOMATION / CAMBRIDGE
We treat accuracy as something to measure, not assume. Before an AI feature goes live we build a test set from your real material, check how often answers are right, and set rules for when the system must say it doesn't know. For sensitive data, we can use models deployed in your own cloud environment. We're equally happy to recommend against AI when a simpler rules-based automation would be more reliable.
IN PRACTICE / CAMBRIDGE
A materials science company could let staff query internal reports, experiment summaries and meeting notes in plain English, with each answer citing the documents it draws on and respecting existing access permissions.
A research-led start-up with several grants might generate first drafts of progress reports from milestone trackers, timesheets and experiment records, leaving scientists to correct and add insight rather than start from scratch.
A biotech team could receive a weekly digest of new papers and preprints relevant to its targets, summarised and ranked by relevance, with links to originals so scientists can judge importance themselves.
AI SYSTEMS & AUTOMATION ACROSS CAMBRIDGE
DELIVERY / CAMBRIDGE
Agree the opportunity and evidence of success.
Make direction tangible before delivery expands.
Build in visible, testable releases.
Use real performance to shape what follows.
QUESTION / CAMBRIDGE
We build an evaluation set from your real questions and documents, agree what a correct answer looks like, and measure performance before launch and after changes. Results are shared openly, including failures. That lets your team decide on thresholds and review processes based on evidence rather than a demo that happened to go well.
NEARBY SERVICE COVERAGE