Most AI forgets your business between one job and the next.
We think that’s the most important unsolved problem in applied AI — and the one we’ve staked the company on.
Your business doesn’t run on clever answers. It runs on what your people know — how the work really flows, what went wrong last time, the reasons nobody’s written down. It’s the most valuable thing you own, and today’s AI throws it away after every conversation. MIT found 95% of enterprise AI projects deliver no measurable return — not because the models are weak, but because they never learn the business they’re dropped into. That’s not a bug you patch. It’s an unsolved research problem.
You’re paying for this now, even if it’s never shown up on a report. When an experienced person leaves, what they knew leaves too — replacing a skilled worker costs between half and twice their salary(SHRM). Across field operations, about a third of working time goes to manual admin, not the job itself(Autodesk/FMI). And the AI you bought to help learns none of it.
Knowledge that stays.
Understanding that holds up accurately over years — doesn’t drift, doesn’t quietly forget, still right the hundredth time you rely on it.
Why, not just what.
Not spotting that jobs keep running over — understanding why it happens in your business, so it still helps when the situation changes.
Shared across your business, sealed off from everyone else’s.
What one part of your operation learns makes the rest sharper — while staying completely isolated from every other business. Not “we protect your data.” Isolated.
No textbook covers this. No model off the shelf does it.
Plenty of companies will sell you AI. Almost none are doing the research to make it learn your business — and the few who are aren’t pointing it at operations like yours. The big platforms build one intelligence for everyone, tuned to no one. The consultancies advise; they don’t build. We’ve run operations like yours, at national scale — we didn’t find this problem in a lab, we hit it ourselves and started solving it because we had to.
We don’t research in a vacuum. Every idea is tested against real operations — the messy, high-stakes places where AI either earns its keep or gets found out. It’s why we deploy inside businesses today and why we built Raku Sight: the proving ground for the science is real work, not a lab. The same MIT study found the AI that works succeeds about twice as often when built with an embedded partner inside the operation, not handed over as software. That’s how we work: in the operation, on the real problem, until it holds.
We don’t know the final shape of what this research becomes. A framework, a platform, something we haven’t named yet — we’re working toward it, and we won’t pretend to be certain. What we are certain of: the goal is intelligence that learns your business and compounds the longer it runs, so what you build becomes something only you have. We’re researching our way there in the open, with the businesses we work with today. That’s the honest version — not a product to buy, a direction we’re committed to.
We think that’s the most important unsolved problem in applied AI — and the one we’ve staked the company on.
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