Craxas AI Lab
Private technical demos of agentic AI safety for financial institutions.
Explore synthetic demonstrations of safety-shielded policy learning, constrained offline reinforcement learning, contextual bandits, and shadow-mode evaluation for customer-facing FI workflows.
Synthetic data onlyNot a production systemBy appointment
01What the lab demonstrates
Agentic safety layer
Multi-agent flows bounded to bank-approved actions before anything reaches a customer or core system.
Safety-shielded policy learning
Next-best-action policies evaluated offline, with hard constraints applied before a recommendation surfaces.
Synthetic FI environments
Disputes, servicing, suitability, and escalation cases across US/Canada, India, and Global packs.
Shadow-mode benchmark path
Run on anonymised historical cases — no customer exposure — and return a quality benchmark.
02Technical methods
The control layer is model-agnostic. The learning is constrained.
Supervised policy learningLearn next-best-action mappings from labelled institutional outcomes.
Contextual banditsChoose among approved actions under uncertainty, inside guardrails.
Constrained offline reinforcement learningOptimise sequential policies from logged data — never live on customers.
Offline policy evaluationEstimate a policy's effect before it is ever surfaced.
Safety shieldHard constraints for evidence, consent, suitability, and escalation.
Human-review thresholdsRoute to people when confidence or risk crosses a defined line.
Audit-replayable decisionsEvery recommendation reconstructable from input to outcome.
Private access
Technical walkthroughs by appointment.
The full engine is not exposed publicly. Request a private session with synthetic FI environments and shadow-mode evaluation.