AI Development in Cambridge
Custom AI development for Cambridge businesses — production-grade LLM systems, RAG pipelines, fine-tuned models, AI-integrated software. Engineering depth, not just prompt tinkering.
Right here in Cambridge
Across Cambridge proper (CB1–CB4) — the Science Park, Trumpington, Cherry Hinton, Chesterton, and the city-centre core around Mill Road, plus the biotech and tech-startup hubs along Hills Road and the Madingley Road corridor.
The Cambridge workflow pains we keep hearing
- "Just bolt on ChatGPT" doesn't work. Generic AI integration produces generic results. Real value comes from grounding AI in your data and your domain.
- Hallucination kills enterprise trust. Without engineering layers around the LLM (validation, RAG, audit logs), no enterprise will let it touch real workflows.
- Vendor strategy unclear. 2026 reality: OpenAI vs Anthropic vs open-source isn't obvious. Wrong choice = expensive lock-in or technical debt.
- Talent shortage. Hiring an AI engineer in Cambridge is competitive and expensive. Contracting one for the specific build is often the better economics.
The Cambridge sectors we work with most
- Biotech & life sciences. Science Park and Genome Campus tenants — research data extraction, lab pipelines, supplier integration.
- Tech-enabled scale-ups. SaaS, fintech, deeptech around the Innovation Centre — AI integrations, workflow automation, customer ops.
- Professional services. Cambridge law firms, accounting practices, consultancies — KYC automation, billing pipelines, client portals.
- University-adjacent services. Tutoring, accommodation, conference-services — booking, lead-capture, review automation.
What we typically build
Retrieval-Augmented Generation (RAG) systems
Vector embeddings + semantic search + LLM = systems that answer accurately from your data. Used for support, internal Q&A, document analysis, sales enablement.
AI features in existing products
Add smart summarisation, classification, generation, or assistance to your existing app. Done with proper guardrails, audit, and confidence scoring.
Custom AI agents & workflows
Multi-step AI workflows that act on data — extract, analyse, decide, integrate — with clear failure modes and human checkpoints.
Model selection & cost optimisation
Right-sized models per task — cheap small models for simple work, GPT-4/Claude for complex reasoning, only-when-needed. Cuts AI costs 5–10× vs naive single-model approach.
How we deliver — 5 phases
Discovery
30-min call + 60–90 min structured scoping. Written scope + quote.
Design
Architecture doc, you sign off before any code.
Build
Git from day 1, weekly demos, no black-box phase.
Deploy
Shadow mode → restricted live → full live. Observability throughout.
Handover
Docs, runbook, training, 30 days free bug-fix support.
Full methodology at how we build.
Pricing guidance
Smaller AI feature integration into existing system £3,000–£7,000. Custom RAG / AI-search systems £7,000–£15,000. Larger AI products with custom training/fine-tuning £15,000–£25,000+. Optional retainer £300–£1,000/month.
Fixed-price quotes after the discovery call. No retainers required. No surprise invoices — scope changes get re-quoted before any work happens.
Where we're based
Wisbech-based, covering Cambridge and surrounds. View Jagatab.UK on Google Maps
Nearby areas we cover
Beyond Cambridge proper, we work with businesses across:
- Ely (CB7)
- St Neots (PE19)
- Newmarket (CB8)
- Royston (SG8)
- Saffron Walden (CB11)
- Huntingdon (PE29)
Real engagement walkthroughs
No fabricated testimonials here. Instead, see what real ai development engagements look like end-to-end:
→ Illustrative case study walkthrough
Detailed pattern documentation of the work we do — engineering choices, ROI math, gotchas, honest framing.
Frequently asked questions
GPT-4 / Claude / Open-source — which?
Depends on the workflow. We pick per task. Most projects use 2–3 models in different layers. See our comparison post.
Will my data train someone else's model?
No. We use API zero-retention modes where available. Your data stays yours. Full DPA, UK/EU hosting, audit trail.
Do you do fine-tuning?
When it pays off — usually not. Modern base models + good RAG outperforms most fine-tuning at fraction of the cost. We'll be honest about when fine-tuning is and isn't the right move.
Can you make it explainable?
Yes. We log every AI decision: input, retrieved context, prompts, output, confidence, action taken. You can answer "why did the system do that?" authoritatively for any past decision.
What if the AI gets it wrong?
5-layer protection: schema-validated output, RAG grounding, confidence routing, audit logs, reversible actions. Errors get caught and queued for human review, not auto-actioned. See our engineering post.
AI Overviews / ChatGPT Search / Perplexity — do we need to optimise for these?
Yes — and we build content + technical structure for AI citation alongside traditional SEO. They use the same fundamentals: clear factual content + semantic schema + structured data.
Start with a 30-minute call
Tell us about the workflow that's costing you the most time. We'll tell you whether ai development will pay back — honestly.