Built by the people who run it.
AI rarely fails on the model. It fails after the demo — in integration, in cost, and in the controls a board requires before a system runs unattended. This team was assembled for that stretch — from the places where this technology is made and run — senior roles at Microsoft, Google, and AWS, an advisory role at Anthropic, and the boardrooms of a NASDAQ IPO and a strategic acquisition.
Three disciplines. One team.
Most firms hold one of these. Production AI needs all three in the same room.
AI research scientists
People who work on how these models actually reason — so the design rests on what is true today, not on last year's assumptions about what AI can be trusted to do.
AI engineers
Builders who ship production-grade systems and keep them reliable under real load — evaluation, observability, and the unglamorous discipline that separates a demo from something a business can depend on.
Operators
Senior executives who have answered to a board, owned a P&L, and lived with the consequences of a number. They translate between what the technology can do and what the business needs it to be worth.
Where the experience comes from.
The institutions, the alliances, and the public-market scrutiny these careers were built in.
A NASDAQ IPO (Alteon) and a strategic acquisition (AirWave) — built through the scrutiny that comes with a public ticker and a diligence table.
We name the institutions, not individuals — the record speaks before any name does.
Principals deliver the engagement.
The senior people in the room are the senior people on it. No handoff to a junior bench once the contract is signed.
One unbroken line runs from design to live operation. That continuity is what makes a system hold up long after the demo — measured under real load, over time, against the outcome that was promised.
Talk to the people who would run your AI.
Meet the team itself — or begin with a Charter, a fixed-fee diagnostic that turns "we should use AI" into a costed, governed plan to operate it in production.