03 · Engagement

AI Product Build

Production AI systems with evaluation and observability — built only where AI-native architecture is actually justified.

Who it is for

Teams past the demo, short of production.

Ventures with a working prototype that falls over under real traffic, real data or real money. Teams who shipped an agent and cannot tell you whether it got better or worse last week.

And founders who need a build partner that will say no to the AI-native version when a boring system would win.

Scope

The build layer.

01Discovery & feasibility checkWhether AI belongs here at all, decided before anyone commits.
02AI-native architecture & system designData, retrieval, orchestration and failure modes on one page.
03Prototype and validationA thin path through the whole system before breadth.
04Production-grade buildThe real thing: auth, rate limits, migrations, the unglamorous parts.
05Agentic orchestration & toolingTools, guardrails and the human checkpoints that keep it accountable.
06Deployment, evals & observabilityYou can see what the system did and whether it is improving.
How it runs

Thin path first, breadth after.

01
Feasibility
The honest read on whether the AI-native architecture earns its complexity.
Weeks 1–2
02
Architecture
System design, data model, eval plan and the failure modes written down first.
Weeks 3–4
03
Build
Vertical slices shipped to a live environment, weekly, with evals attached.
Weeks 5–12
04
Harden & hand over
Security, compliance, observability, runbooks and the team walk-through.
Weeks 13–14
What you get

A system your team can run.

01Production applicationDeployed, instrumented and documented.
02Evaluation suiteRegression sets and scoring, so quality is a number.
03ObservabilityTraces, cost and latency visible per request.
04Agentic guardrailsPolicy, escalation and audit trail.
05Security & compliance passTesting and the paperwork that follows it.
06Runbooks & handoverWritten for the engineer who joins next month.
What it is not

Not a demo, not staff augmentation.

We do not ship prototypes labelled as products, and we do not park engineers inside your sprint board. Every engagement carries a fixed scope and a fixed end date.

If the honest answer is that a deterministic system beats the agentic one, that is the recommendation — and it has happened more than once.

Model-agnostic by design, with an evaluation harness that makes switching a measured decision rather than a rewrite.

Yes. Most builds are not greenfield; we start with a read of what exists and what it costs to keep.

The team runs it. We stay available for a scoped support window, not an open-ended retainer.

See it in practice

What this engagement produces, shown.

What the AI Product Build engagement produces
Production systems with evaluation and observability, shown on our own ventures.
Scoped to requirement

We operate, we do not observe.

We build alongside the founding team and hand over something they can run without us.

Talk to the studio