Built by the people who run it.
We came together 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. AI rarely fails on the model; it fails after the demo, in production. BeanSprout is the firm built for that — one team that designs the system, builds it, and runs it in production.
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 at the frontier of how these models reason — so the systems we build rest on what is actually 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.
No invented résumés here — these are the real institutions and markets our people have operated in, kept general by design.
A NASDAQ IPO (Alteon) and a strategic acquisition (AirWave) — built through the scrutiny that comes with a public ticker and a diligence table.
Provenance, not pretense. We name the institutions, not individuals — the record speaks before any name does.
The team that builds it runs it.
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.
Whoever designs a system runs it in production. One unbroken line from design to live operation is what makes it 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.
Talk to an expert and 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.