Knowledge, operationalized.
Not “AI that reads documents”. The full loop: read the source, compile it into typed rules and formulas, execute them into results — and keep the proof. Law is the hardest case, so it’s home turf. The machine doesn’t care: tariffs, catalogues, safety codes, internal procedure.
Prose, PDFs, photographed pages, mixed Arabic/Latin scripts. Cheapest method first — familiar formats never reach a model.
Typed rules and formulas — versioned, cited back to their clause, tested against values hand-derived before the code exists.
Fixed-precision decimal, never floats. Deterministic: same checkpoint in, bit-identical result out. Or 4,412,809.00 — at ledger scale.
Every figure answers with its rule, its input and its run. Inputs are stored; conclusions can always be produced again.
IF THE KNOWLEDGE CHANGES, THE RULES RE-COMPILE. IF A RULE CAN’T FIRE SAFELY, THE MACHINE REFUSES. REFUSAL IS A FEATURE.
Bolting a model onto your stack is a morning’s work.
Knowing where it must never go — that’s the job.
The boundary isn’t a guideline. In my systems the computation engine has no network, no database, no import path to a model client.
Rules that live in people’s heads die under deadline pressure. Rules that live in the import graph don’t care about your deadline.
Yes, the work ships faster than it used to. It shows up in the estimate, not in the marketing.