Discover
We audit workflows, data and systems, then rank AI opportunities by ROI and feasibility.
What happens
- Stakeholder interviews
- Data audit
- ROI model
You get
- Opportunity map
- Technical brief
- Fixed-scope proposal
Process
Evidence first. Then scale.
Five phases, each with clear deliverables and a decision point — so you never pay for a build before the numbers justify it.
Timeline
The five phases.
Prototype
A working prototype on your real data, with evals that prove quality before we commit to a build.
What happens
- Model benchmarking
- UX prototype
- Eval harness
You get
- Clickable prototype
- Eval report
- Architecture plan
Build
Weekly sprints shipping production code to your repos, with demos every Friday.
What happens
- Sprint demos
- CI/CD
- Security review
You get
- Production app / agent
- Test & eval suites
- Documentation
Deploy
Staged rollout with monitoring, guardrails, cost controls and team training.
What happens
- Canary rollout
- Observability
- Training
You get
- Live system
- Dashboards & alerts
- Runbooks
Optimize
We measure outcomes, tune prompts and models, cut cost and expand to the next use case.
What happens
- Monthly reviews
- Cost tuning
- New features
You get
- KPI reports
- Model upgrades
- Roadmap
Principles
How we work.
Security first
Least-privilege access, private deployments and zero-retention endpoints by default.
Evals before opinions
Every model and prompt change is measured against a test suite on your data.
Humans in the loop
Clear approval points wherever an AI action is costly or irreversible.
Tied to ROI
We agree the success metric before writing code — and report on it after launch.
[ Next step ]
Let's build AI that pays for itself.
Book a 30-minute strategy call. We'll map your highest-ROI AI opportunities and tell you honestly what's worth building.