AI & Automation
AI that does a specific job, measurably well
Most AI projects fail for an unglamorous reason: nobody defined what "working" meant. We start from the opposite end — pick a task with a measurable outcome, build an evaluation set from your real data, then build the smallest system that passes it.
Indicative
Assessment from £3,000
Fixed price agreed in writing before any build starts.
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The problem this solves
Companies buy AI expecting transformation and receive a demo. The demo impresses in a meeting and falls over on real inputs, because it was never tested against the messy cases. There is also rarely a cost model, so nobody notices the per-request spend until the invoice arrives.
What you get
Use-case selection and feasibility
A short assessment of which candidate tasks are actually suited to AI and which are better solved with ordinary software. Frequently the honest answer is that a rules engine will do the job more cheaply and reliably.
Evaluation harness
A test set built from your real inputs with expected outputs, so accuracy is a number that can be tracked across model and prompt changes instead of a subjective impression.
Model selection and benchmarking
We benchmark candidates — including Mistral, open-weight models you can self-host, and the frontier APIs — against your evaluation set, comparing accuracy, latency and cost per thousand requests.
Guardrails and fallbacks
Input validation, output schema enforcement, refusal handling and a deterministic fallback path, so a model failure degrades gracefully rather than producing confident nonsense.
Cost and usage controls
Per-tenant rate limits, caching of repeated requests and a spend dashboard, so unit economics stay predictable as volume grows.
Human review workflow
Where output carries risk, an approval queue with audit trail — because for many business processes "AI drafts, human approves" is the only defensible design.
How we work
Task definition workshop
We agree exactly what the system must do, what counts as correct, and what the acceptable error rate is.
Data and evaluation set
Assemble representative inputs including the awkward edge cases, and label expected outputs.
Prototype and benchmark
Build the thinnest working version, measure it, and report honestly — including when results say the project should not proceed.
Harden for production
Add guardrails, observability, retries, cost controls and the review workflow.
Integrate
Wire into the systems where the work actually happens, rather than leaving a separate tool nobody opens.
Monitor and iterate
Track accuracy and spend in production. Model behaviour drifts and providers deprecate versions; ongoing measurement is part of the design.
What you should expect
- A named accuracy figure on your own data, not a vendor benchmark
- Known cost per request before launch, with spend controls in place
- Graceful degradation instead of silent wrong answers
- Audit trail for every AI-assisted decision
Built with
- Mistral
- Claude
- OpenAI
- Llama
- Python
- TypeScript
- FastAPI
- LangChain
- pgvector
- Qdrant
- Docker
- AWS Bedrock
Mainstream, well-supported technology — chosen so you can hire for it and so another team could take the project over.
AI Development — your questions
Including the ones about cost, which most agencies leave off the page.
A feasibility assessment with an evaluation harness is typically £3,000 to £6,000 and tells you whether the project is worth building at all. Production systems generally start around £15,000. Running costs depend on model and volume — we give you a modelled figure per thousand requests during the assessment, which is usually the number that decides the business case.
Hosted APIs win on capability and time to market, and suit most workloads. Self-hosting an open-weight model becomes worthwhile at high, steady volume, or where data cannot leave your infrastructure for regulatory reasons. The break-even is a calculation, not a preference, and we run it with your actual projected volume.
Not if the contract is set up correctly. The major providers offer enterprise terms that exclude API inputs from training, and we configure accordingly. Where the data is too sensitive to send off-premises at all, self-hosting is the appropriate answer. We document the data flow so your DPO can review it.
You constrain it rather than trust it. Ground answers in retrieved source documents with citations, enforce a strict output schema so malformed responses are rejected, and route low-confidence cases to a human. Hallucination cannot be eliminated from a language model, so the system around it has to assume it will happen.
Sometimes, and the honest test is whether a specific repetitive task consumes real hours. Document extraction, enquiry triage and first-line support are common wins. Broad "add AI to the business" ambitions usually are not, because there is no measurable target. Start with one task and a number attached to it.
Related services
Most projects touch more than one of these.
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Support and sales assistants grounded in your documentation, with citations, human escalation and honest resolution reporting.
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AI features added to an existing product, with cost caps, streaming, fallbacks and an evaluation set — and no lock-in to one provider.
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Retrieval-augmented systems that turn scattered documents into answers with citations, for staff or customers.
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The repetitive tasks in your week identified, costed, and automated where the numbers justify it — with a human in the loop where they do not.
Read moreCustom Software
Bespoke systems that replace the spreadsheets, manual handoffs and off-the-shelf tools your business has outgrown.
Read moreTalk to someone who has built this before
A short call is usually enough to tell you whether this is the right service for your situation — including when it is not.