AI features that survive production.
For teams moving AI from demo to production. LLM features and agents, engineered and evaluated.
- Timeline
- 4–12 wks
- Team
- 2–4
- From
- Scoped after a call
- Platforms
- Claude, OpenAI, open models
ProblemSound familiar?
- A demo that never shipped
- No way to know if the model got worse
- Costs that spike unpredictably
We engineerWhat we engineer
Retrieval, tools and agents with evaluation suites, cost controls and fallbacks — measured like any other system.
ArchitectureTypical architecture
- Product surface
- Orchestration + tools
- Retrieval store
- Evals + monitoring
CapabilitiesCapabilities
- RAG and search
- Agents and tool use
- Evaluation harnesses
- Cost and latency tuning
DeliverablesDeliverables
- Production feature
- Eval suite in CI
- Monitoring dashboard
ProcessProcess
Discover
We learn the business before the brief: the metric that matters, the constraints, the people involved.
- Kick-off workshop with your decision-makers
- Access to analytics, code and tools
- Agree the one metric this work must move
- Kick-off notes and a shared goal
- Access checklist
- Named pod lead
We learn the business before the brief: the metric that matters, the constraints, the people involved.
What happens- Kick-off workshop with your decision-makers
- Access to analytics, code and tools
- Agree the one metric this work must move
- Kick-off notes and a shared goal
- Access checklist
- Named pod lead
StackStack
WorkWork
Yes. We start with an architecture review, document what exists, and agree what to keep, refactor or replace before writing new code.
You do — repositories, cloud accounts and credentials live in your organisation from day one.
That is the default. A pod embeds with your team, pairs on the hardest parts, and leaves documentation and runbooks behind.
Start with ai systems.