Outcome-Grounded Expert Encoding
The first computational methodology capable of encoding sublinguistic intelligence: learning from what experts were right about.
OGEE addresses the labeller's ceiling by substituting a fundamentally different training signal. Rather than learning from what experts articulate or demonstrate, it learns from what experts were right about.
Observe
Expert practitioners observed in naturalistic workflows without elicitation or interruption. We do not ask them to explain.
Match
Each decision paired with its verified long-run outcome: what actually resulted, not what was intended.
Recover
Maximum entropy inverse reinforcement learning identifies the implicit reward structure of expert decisions.
Deploy
Systems that predict with Bayes-optimal accuracy, exceeding any label-trained or demonstration-trained baseline.
As the dataset grows, OGEE-trained systems converge to the Bayes-optimal classifier, bounded only by the irreducible uncertainty in the outcome distribution itself.
AICIL: AI Compliance Intelligence Layer
AICIL predicts whether a cross-border payment will clear before you send it, trained on what compliance experts were right about, not what they could explain.
The invisible ceiling in cross-border payments
Twenty percent of all international wire transfers freeze mid-flight. The payment stops. An investigation begins. Documentation gets scrambled. Three to twenty-one days pass. This happens to $40 trillion in transactions every year. The root cause is not bad actors. It is that compliance systems were built on rules, rules that cannot capture what experienced compliance officers actually know. The best judgment in the world operates beneath those rules. AICIL encodes it.
How it works
Before the wire
Submit payment details before initiating transfer. AICIL receives origin, destination, amount, currency, counterparties, business purpose.
Screening
Maps the complete correspondent banking chain. Screens 32,453 global banks, 19,703 sanctions entries, 8 major payment systems. Not with rules, with the encoded judgment of expert compliance officers.
Clearance prediction
Returns prediction in seconds. If it will clear: generates jurisdiction-specific compliance dossier. If it will not: identifies what is missing, generates required documentation before submission.
What makes it different
Every other compliance system learns from what experts could articulate. That creates a ceiling at 92 to 95 percent accuracy. AICIL uses OGEE to learn from what experts were right about. It observes naturalistic workflows, pairs decisions with verified outcomes, recovers the implicit judgment. The accuracy ceiling is Bayes-optimal, not labeller-bounded.
Live production system
OGEE requires outcome-matched expert decision data, a resource that does not exist in any available dataset and cannot be synthesised. This creates a compounding structural advantage.