Where linear algebra meets the problems industry actually has
Every large language model is, underneath, linear algebra – vectors, matrices, transformations – at a scale nobody could compute twenty years ago. And every company has problems no model has ever seen, because they live in the heads of the people who solve them every day. eigenheads works where these two meet.
The name is older than the hype. I took it in 1999 from William Gibson’s Idoru and from eigenfaces, an early face recognition method. It took the technology two decades to catch up with the name.
Three Ways In
Most AI consulting today is wrestling: a lot of show, choreographed moves, and nobody really gets hit. We prefer sparring. That means real contact with your real problem, from the first hour.
The Workshop
One and a half days. You bring a briefing, we bring the LLMs, and we work on your problem live. We build prototypes and push until something breaks, to find out where AI actually creates leverage and where it doesn’t. No slides. We promise nothing we can’t show on the spot.
The Build
Production-grade AI applications, built with SOC 2 and ISO compliant processes. A prototype impresses in a meeting. A production system has to survive real operations, real data and the user who does exactly what nobody expected. That’s what we build for.
The Integration
Your domain experts know things no foundation model contains. It never will, because most of it was never written down. We connect that knowledge to LLMs for specifications, requirements engineering and data models. The result is grounded in how your industry really works, not in how a model guesses it works.
The Name
Eigen – the mathematical prefix running through modern AI.
Heads – the people who make it useful.
The math matters. But a model that is mathematically elegant and doesn’t solve your problem is worth nothing. So we need both, or better: we need the math to serve the problem. That’s why we start with your problem, not with the model.