AllocateDecisions your regulator can read,
at the speed your customers expect.
Dispute, onboarding, and reconciliation agents trained on your rulings and running inside your boundary, with audit trails and hard spend caps by default.
Dispute and chargeback review
Clear the legitimate disputes overnight and flag the rest with the evidence already assembled.
KYC and onboarding
Read documents, verify against your rules, and file exceptions to a human with full context.
Reconciliation and reporting
Pull the week from your warehouse and deliver the summary before anyone asks for it.
A dispute agent clearing 14,860 cases a week at 96.1% precision, every ruling feeding the next model version.
Models teams route here.
Start on frontier, fine-tune the open ones on your own data.
Common questions
Can our regulator review what the AI did?
Yes. Every action, tool grant, and model change is logged; decisions carry the evidence they were made on. Auditability is built into the platform rather than reconstructed after the fact.
Does customer data train anyone else’s models?
No. Your data trains your model inside your boundary, and nothing crosses it. That is an architectural property, not a policy promise.
How do we control spend on high-volume workloads?
Hard caps per key and per workspace, with live per-route metering. Dispute and reconciliation volumes are forecastable because the unit economics are visible per token.
Can we run different models for different workflows?
That is the default: each workflow is a route with its own model, so KYC extraction can run on a cheap fast model while dispute rulings run on a reasoner. Swapping either is configuration, not a deploy.
Where can workloads be pinned?
To a region: South Africa today, with Gulf infrastructure on the regions page. Pinned workspaces keep data, weights, and inference inside the region.