From AI ambition to systems in production.
Most AI efforts get stuck between an impressive demo and something a business can actually rely on. We close that gap — defining where AI is worth it, building it end to end, and operating it at scale.
Who this is for
- Engineering leaders under pressure to “do something with AI” who want production value, not another demo that never ships.
- Teams with a promising proof-of-concept that stalled on the path to something real users can depend on.
- Organizations looking to reshape a critical function — or stand up a new line of business — on top of AI, and want operators who have run systems at scale.
What we deliver
- An AI strategy grounded in your actual systems, data, and constraints — not a generic transformation narrative.
- Working solutions built end to end: proof-of-concept to MVP, with the evals and guardrails to trust them.
- Agent and LLM systems engineered for production — observability, failure handling, and cost controls from day one.
- An operating model and trained team to run, evaluate, and extend the systems after we leave.
Engagement
Scoped to your goals. Three phases.
Strategy & use-case selection
We separate the use cases worth building from the ones that only sound good in a board deck — judged against your data, your risk tolerance, and where AI actually moves a number.
Build (POC → MVP)
We build the solution alongside your team: agents and LLM workflows wired into your systems, with an evaluation harness so quality is measured, not asserted.
Productionize & operate
Hardening, observability, guardrails, and the operating cadence to run it at scale. Documentation and training so the system stays your team’s, not ours.
Outcomes
What changes by the end.
Categories of value, not promised percentages. Real numbers belong on a case study, after we've done the work.
- AI systems in production that realize measurable value — not pilots that quietly stall.
- An evaluation discipline your team uses to ship changes with confidence instead of vibes.
- Guardrails, observability, and cost controls built in, so the system holds up under real traffic.
- A team that can operate, evaluate, and extend what we built after the engagement ends.
Work with us
Ready to make AI real?
The qualifying form asks where you are with AI, what you're trying to build, and what's gotten in the way. It takes about two minutes.