AI & LLM development
Summarisation, drafting, extraction, classification, conversational interfaces, designed around your workflow, evaluated on your data, and shipped inside your product rather than beside it.
All AI services
Language-model features built into the products people already use.
What we build with it.
The shapes this work usually takes. Yours will differ; the approach won't.
Copilots inside your product
Drafting, rewriting and answering in the context of the record the user already has open.
Document extraction
Turn contracts, invoices, forms and reports into structured, validated data.
Classification and routing
Tickets, leads, claims and messages sorted and sent to the right queue with a confidence score.
Conversational interfaces
Chat and voice front-ends that stay on topic, cite sources and escalate cleanly.
Deliverables, not decks.
Everything is handed over as code, data and documentation you own. Nothing depends on us staying.
- Evaluation set built from real inputs, with client sign-off
- Prompt, model and retrieval design with documented trade-offs
- Production integration in your web or mobile product
- Guardrails: input validation, output checks, PII handling
- Tracing, cost dashboards and nightly regression evals
- Handover playbook and model-swap plan
The engagement, step by step.
Define and measure
Two weeks to agree the job to be done, assemble the eval set and set the bar the feature must clear.
Prototype on live data
A working slice inside your product, scored nightly, iterated with the people who will use it.
Harden
Guardrails, fallbacks, cost limits and observability. The boring part that makes it shippable.
Launch and tune
Staged rollout with human review, then a monthly cycle of eval review and model updates.
Chosen per project, by score and cost.
Which model will you use?
Whichever scores best on your evaluation set at a cost you can run. We build so the model is swappable, and we re-run the evals when a better one appears.
Can it run on our own infrastructure?
Yes. We deploy hosted models through AWS Bedrock, Azure AI or Vertex AI, and open-weight models on your own cluster when data residency requires it.
How do you stop it making things up?
Grounding on your content with citations, output validation against schemas, confidence thresholds, and a human escalation path for anything below them.
Need ai & llm development?
Tell us the problem. We'll come back within one business day with how we'd approach it.
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brighter path.
Tell us what you’re building. We’ll be in touch within one business day.