FieldRules operationalizes the methods that knowledge engineers developed over five decades: contrastive elicitation, anti-habituation, process reward models, and consequence-naming as a guard rail. The reasoning layer isn't aspirational. It's provably structured.
Post-hoc rationalization is the default. A domain expert can confidently explain why they made a decision, but that explanation is often constructed after the fact, not the actual reason they made it. Traditional documentation captures confabulation, not reasoning.
FieldRules uses contrastive elicitation: instead of asking “why did you do this?” it asks “what would go wrong if you didn't?” That shift forces the expert to articulate consequences, not intentions. Consequences are harder to confabulate about.
FieldRules is pre-pilot. We don't yet have our own published efficacy data — and we won't claim we do. What we have is a growing body of recent external work, from researchers with no commercial interest in FieldRules, arguing that the reasoning trace — not more model capability — is the bottleneck. The BECAUSE field is our name for the artifact those researchers are describing.
A note on our own data: we'll publish pilot results once we have them. Until then, we're careful to keep the external validation section about the problem space, not about FieldRules's efficacy. If you want to see our measurement methodology — Reasoning Health Score, anti-pattern detection, divergence alarms — we can walk you through it on a call.
We're onboarding a small number of research partners and production teams. If your domain carries judgment that scales with AI, let's talk.
No deck. No demo-ware. We start with a conversation.