Find the repeated work
Mines real sessions for the problems AI keeps solving, and what they cost.
Tesryn Lab · AI evaluation
Capability Distillation
When an AI figures something out, decide where that know-how should live for good, and prove the change helped.
In use at Tesryn First built for teams paying for the same AI work twice · 2026
01 — What it is
Expensive AI keeps solving the same problems. This measures, one change at a time, whether a capability can move somewhere cheaper and more reliable, whether that's plain code, a check, a rule or a written procedure, or whether it should stay with the model. Each experiment is registered before it runs, and its verdict stands whichever way it falls.
The moment it earns its keepA question the AI answered 182 times becomes three seconds of ordinary code, with the evidence that it's just as right.
What it proves we can do
02 — What it can do
Four things it does.
One change, one verdict
Each change is tested against conditions written down before the run: keep or revert.
Cheaper, and just as right
A capability moves only when it gets cheaper without getting worse.
"Leave it with the model" is an answer
Sometimes the honest result is that nothing should move, and it says so.
03 — How it connects
What it shares with the rest of the Lab.
The same things we know how to do keep coming back in different industries. This is where this one sits: what it proves, and what else proves the same thing.
Knowing whether a change actually helped Conditions written before the run, a sealed test, and a verdict that stands either way. Also proved by Research Benchmark, Research Runtime.
Comparing only where evidence can tell things apart Differences smaller than what the method can measure are reported as ties, not wins. Also proved by Magnetic Digital Twin, Research Benchmark.
Watching without touching Systems that can detect and explain but hold no power to change what they watch. Also proved by Sentinel.
The whole map — six sections, and everything that proves them.
04 — What it's held to
- It measures and recommends. It never changes the system it measures.
- A held-back set is spent sparingly, so results stay trustworthy.
What it isn't. Not an automatic optimiser. A person decides what moves.
05 — The recipe stays in the lab
We'll tell you what it can do. Not how it's built.
Yours would be your own build, on your systems, and you'd own it outright: code, documentation, no lock-in. What we bring to it is the practice — we've solved this shape of problem before, so you're not paying for the first attempt. The method stays ours. And if what you need isn't here at all, that's the usual case — it's how every skill on this page started.