Service
AI Setup & Integration
From copilot to production agent — AI that ships, not just demos.
Most AI projects fail not because the model is wrong, but because the evaluation, data pipeline and human-in-the-loop layers are missing. We build the entire stack: retrieval, prompt engineering, evals, observability and the boring infra that keeps it running at 3am.
What's included
- Retrieval-augmented generation with vector + keyword hybrid search
- Fine-tuning on your proprietary data (LoRA, QLoRA, full SFT)
- Multi-agent systems with tool use, planning and human approval
- Evaluation harness with golden datasets and regression alerts
- Cost, latency and quality dashboards — so you can ship with confidence
Stack we usually pick
Deliverables
- 01Production AI feature integrated into your product
- 02Eval suite with continuous monitoring
- 03Data pipeline and embedding refresh jobs
- 04Internal AI playbook for your engineering team
How we work
How we work
Use-case framing
We pick the right problem — one that AI uniquely solves and that has measurable value.
Prototype
A 2-week spike to prove the model can do the job on your real data.
Productionise
Caching, retries, evals, observability, cost dashboards and rate limits.
Iterate
Monthly model refresh and prompt-tuning informed by real user telemetry.
Frequently asked
Frequently asked
Which model should I use?+
We benchmark Claude, GPT-4-class and open-source models against your actual data and report the cost-quality trade-off — you make the call with real numbers.
Will my data train someone else's model?+
No. We default to zero-data-retention API endpoints and can deploy fully self-hosted models if required by regulation.
How do you measure AI quality?+
Golden eval sets, LLM-as-judge with regression alerts, plus human spot-checks. We refuse to ship a model without an eval harness.