How we deliver
From use case to production — without the stall.
Most AI initiatives die between the demo and the rollout. Our delivery model is built to cross that gap: clear success measures up front, evaluation on every change, and accountability long after launch.
The method
Four phases. Scroll through them.
Illustrative artefacts
- 01
Align & scope
We clarify outcomes, constraints and success metrics before building — including the data available and the risks to manage.
- Stakeholder and process walkthroughs
- Data and integration review
- Risk, privacy and security considerations
- Agreed success measures and a go / no-go point
- 02
Design & architecture
We map the UX, data and stack so delivery stays predictable: model choice, retrieval design, integration points and evaluation criteria.
- Reference architecture for the solution
- Model shortlist and evaluation plan
- UX flows, including human review steps
- Security and access design
- 03
Build & iterate
Fast sprints, clear communication and QA checkpoints throughout — with AI output measured against agreed test sets, not impressions.
- Regular demos of working software
- Automated evaluation on every change
- Code review and automated testing
- Flagged cases reviewed with your experts
- 04
Launch & support
Release safely, monitor performance, then scale with confidence — tracking quality, cost and usage in production.
- Staged rollout with a rollback plan
- Monitoring for quality, drift, latency and spend
- Handover documentation and training
- Ongoing support and improvement
AI evaluation
Measured, not assumed.
An AI system is only as trustworthy as the tests behind it. Evaluation is part of the build, not a step at the end.
- Test sets built with your experts — real questions and documents, with agreed correct answers.
- Acceptance criteria up front — accuracy, tone, refusal behaviour and safety thresholds agreed before build.
- Regression on every change — prompts, models and retrieval are re-scored before release.
- Human review of edge cases — flagged outputs reviewed and fed back into the test set.
- Production monitoring — the same measures tracked after launch, with alerts on drift.
Working together
Clear communication, no surprises.
Regular check-ins
Weekly check-ins, with demos every fortnight. Decisions and risks are written down.
Documentation
Architecture, runbooks, evaluation suites and known limitations — so your team can own the system.
After launch
Monitoring, maintenance and improvement options to suit your team. Support levels and response targets are agreed per engagement.
Ready to get past the demo?
Book a consultation and we’ll map your use case to the first phase.