We de-risk AI the same way every time: agree on what “working” means, build the riskiest part first, and prove quality with evaluation — not optimism. No black boxes, no surprise bills.
Every engagement moves through the same shape. Evaluation runs through all of it, so quality is measured from the first sprint to ongoing operation.
Define success, map data and integrations, choose the architecture.
Ship working software every sprint; iterate against real data.
Prove quality, control cost, then roll out and run it safely.
You don’t have to commit to a platform on day one. Start small, prove value on real data, and scale only when the evidence supports it.
1–3 weeks · fixed scope
You have an AI idea and a deadline, but no shared definition of what “working” means.
It turns an uncertain, open-ended bet into a lower-risk first step — and tells you honestly whether to build at all.
Deliverables
4–8 weeks
You need to prove one workflow or model path works on real data before you commit to scale.
It de-risks the build by validating the hardest path first — on your data, against a real evaluation harness.
Deliverables
8–16 weeks
You’re ready to ship AI into a real product and it has to hold up with real users.
Most AI dies between demo and deployment. This is the engineering that gets it across — integrated, observable, and measured.
Deliverables
Ongoing · monthly
Your AI is live and now has to stay reliable, accurate, and affordable as it evolves.
LLM systems drift. Models change, data shifts, costs creep. This keeps quality and unit economics under control over time.
Deliverables
Acceptance criteria are written in discovery, not argued after launch. Before we build, we define the quality targets, the cost envelope, and the conditions a release has to meet to ship.
Those criteria become an evaluation harness: gold-standard datasets, automated scoring, and regression checks that run as prompts, models, and logic change. When a change makes quality worse, we see it before your users do.
The result is a release decision based on evidence — a defensible answer to “is this good enough to ship?” that everyone can see.
Acceptance criteria
IllustrativeReal criteria are defined with you in discovery and tuned to your workflow, data, and risk profile.
These aren’t add-ons. They’re how we work by default — the reason our systems make it to production and stay there.
We define acceptance criteria and build an evaluation harness early, so quality is measurable from the first sprint.
We validate the riskiest integration or model path before investing in everything around it.
Unit economics are estimated in discovery and instrumented in delivery — no surprise bills at scale.
Discovery and proof-of-value engagements are time-boxed and low-commitment, so you can stop early if the evidence says so.
Data handling, model risk, and contract boundaries are addressed early — not retrofitted under audit.
If we can’t see it, we don’t ship it. Every system is traceable and monitored before it launches.
Start with a discovery sprint. In a few weeks you’ll have a reference architecture, an evaluation plan, and an honest go / no-go.