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Applied AI
AI features are easy to demo and hard to trust. We treat them like any other system: define what correct looks like, measure it, then ship behind guardrails. That means retrieval you can inspect, prompts under version control, and an eval suite that fails the build when quality drops.
- LLM apps
- RAG
- Agents
- Data
Deliverables
What lands in your hands
Not a phase list. These are the artefacts you own at the end, and the ones we are prepared to be measured against.
- 01
Retrieval pipeline with chunking, embeddings, and a vector store you can query directly
- 02
Eval suite with golden datasets, regression scoring, and results in CI
- 03
Guardrails: input validation, output schemas, refusal handling, and cost ceilings
- 04
Tracing and logging for every model call, so failures are debuggable
- 05
Model and provider abstraction, so switching costs you a config change
- 06
A written accuracy baseline and the honest list of what the system cannot do
How we work
The same four stages, every time
No discovery phase that never ends, and no black box between kickoff and launch.
- 01Week 1
Scope
We map the problem, agree what version one must do, and cut everything it does not. You leave the week with a written scope, a fixed price or rate, and a delivery date we are willing to be held to.
- 02Week 2-3
Design
Flows, screens, and data model in parallel. You review clickable work, not static mockups, so the arguments happen while changes are still cheap.
- 03Week 4+
Build
Two-week sprints with a demo at the end of each one. Staging updates continuously, so you can use the product long before it launches.
- 04Launch +30d
Ship
We deploy, watch the graphs, and fix what real traffic exposes. Thirty days of included support, then handover docs — or we keep going as your team.
The other lines
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Let's scope your applied ai work.
Bring the problem, the constraints, and the date you need it by. We'll come back with a scope, a price, and the engineers who would do it.