The hard questions, answered plainly.

It trains a small AI model to do one job as well as a much larger model would.

A large "teacher" model writes expert analyses of your data. Software checks every number in those analyses against your source records and throws out any that fail. A small "student" model then trains on the answers that remain. "Distillation" is the name for this teacher-to-student process.

The student runs on an ordinary computer, answers in seconds or less, and costs a tiny fraction of what the teacher would. Every number in its answers still points back to your records.

The questions are fixed, but the set can be broad, and the answers change with the data.

"What changed materially this week?" produces different findings, causes, and recommendations every week, as the data changes.

Breadth is cheap, because a question nobody asks costs almost nothing once the model is running. A leading AI model can help look for gaps. Whether the set covers everything someone will want to know remains a judgment call, and HeartCut does not claim to settle it.

It makes every fact behind the reasoning checkable, and invented facts are the most common failure. The reasoning itself is left to a reviewer.

If an answer says a metric rose from 6.1 to 8.4, HeartCut automatically checks that both values exist in the source data. It cannot prove that the explanation for the rise is right.

That narrows what a reviewer has to take on trust. Instead of confident prose that may rest on invented numbers, the reviewer sees an interpretation built on traceable evidence and can judge the reasoning on its merits.

Not of the mistakes the checks catch. HeartCut checks the large model's work before the small model trains on it.

HeartCut rejects teacher output that fails its evidence and logic checks, and several teacher analyses can be compared before one becomes training material.

There is a limit: if the teacher consistently makes the same plausible mistake, the student learns it too. HeartCut improves the training signal. It does not make an imperfect teacher perfect.

Simulated scenarios supply what the history lacks.

A model trained only on normal operation would learn to report that nothing is wrong, so the missing conditions are generated instead of waited for. The motorsport work HeartCut grew from already trains on real and generated driving data together.

Generated data brings assumptions of its own. HeartCut keeps every record labeled as real or generated through the whole pipeline, and tests the final model on real records only.

Only if cost is the reason. Speed, running where the data already lives, and checkable evidence can each justify HeartCut at lower volume.

On cost alone, HeartCut wins on constant workloads: thousands of metrics, markets, machines, or customers analyzed again and again. If you need a few thousand answers a month, calling a leading AI model is easier and cheaper than building a specialized one.

The model is rebuilt.

The teacher analyzes the new data, the results join the training examples, and an updated model learns the changed conditions.

You can rebuild on a fixed schedule, when the incoming data drifts, or when the small model's answers start to disagree with a leading model's. HeartCut gives model maintenance a structure; it does not remove the need for it.

The pipeline works today as a prototype, and the savings are modeled from it.

Every stage runs end to end in the motorsport work that HeartCut grew out of: teacher analysis, citation checks, logic checks, generated training data, and a working specialized model. The cost figures are modeled from that pipeline and scaled to enterprise volumes.

A capable engineering team can build one pipeline. HeartCut spares them from building the next one.

HeartCut's value is a reusable framework, so a team does not build a new pipeline for a broad range of analysis problems. It does not claim to be a new kind of AI model.

The questions, domain knowledge, data format, and rules change from one application to the next. The checking, record-keeping, training steps, citation lookup, testing, and reporting do not. The aim is that a new application needs little more than configuration and a connection to your data, so the savings show up on the second, tenth, and hundredth application.

A framework that turns expensive general-purpose AI reasoning into cheap, fast, specialized answers, with the facts underneath them checked automatically.

It provides a repeatable way to take valuable reasoning from a leading AI model, check it, shrink it into a small model, and run it at a scale and cost that would otherwise be impractical.

It does not claim that AI reasoning becomes infallible, that it knows every question users will ask, or that historical data contains every situation a model will meet.