Accurate intelligence,
engineered.
Enterprise AI models are more capable than their answers show. At runtime they drift off objective, repeat themselves, converge too early, and ship numbers nobody verified. That gap is where accuracy is lost. Gammatic closes it with control engineering: real-time measurement of agent behavior, deterministic verification of every critical claim, and runtime governance with the authority to stop.
▮ RESEARCH-ORIGIN ENGINEERING
Built as an instrument, tested like one.
A complete factory pre-design, conventionally scoped for a ten-engineer team over four months, delivered in a single working day in the thesis case study.
Case study →Of controlled runs ended on an external, auditable stopping event. Without the control layer, most runs never reached a decision at all.
Thesis experiment →Ships with a status: verified against its source, marked unverified, recomputed under deterministic rules, or rejected.
Claim discipline →▮ WHAT GAMMATIC DOES
A control loop around the model, not a bigger model.
Instrument the process
Tool calls, evidence, hypotheses, and claims become a measured process with control-relevant signals, the way an engineer instruments any critical system.
Verify every critical claim
Numbers reconcile against their sources. Units, time ranges, and aggregation grain are enforced deterministically. Unsupported claims are labeled, never smuggled into prose.
Hold the authority to stop
Completion is not the model's opinion. An external, auditable authority decides whether to continue, challenge, redirect, refuse, or stop.
▮ FIRST DEPLOYMENT DOMAIN: MANUFACTURING
Every critical numerical claim is either verified against its source, explicitly marked unverified, recomputed under deterministic rules, or rejected.
TECHNICAL DEEP-DIVES ARE SHARED UNDER NDA.