The routine batch cleared automatically, exceptions routed to the right person with the evidence attached and a threshold you control.
Extraction and validation for onboarding and lending packets, with per-document-type accuracy measured rather than assumed.
Every automated action traced, replayable and exportable, so a review is a query rather than an archaeology project.
Tenancy, access control and cloud structure that hold up to a security questionnaire from a bank.
Short cycles, visible progress, and a scope you can change. You see working software every week rather than a status report.
The repository, the infrastructure code, the evaluation set and the documentation. In your accounts, under your licence, from the first commit.
Sit with the reconciliation or onboarding team, collect real cases including the ugly ones, and write the evaluation set from them.
A working agent on a copy of production data, running in shadow mode against the same batch your team is clearing.
Guardrails, review queue and approval flow built with your risk owners, then live volume increased in steps.
Monthly evaluation reviews, model upgrades tested before they ship, and the audit pack kept current.
Above a value you set, nothing posts without a named human approving it. That limit is a business decision, not a model setting.
Inference runs in your cloud or under enterprise terms with training disabled, and the data path is documented before we build.
When a partner API or statement feed breaks, the system raises it. A quiet gap in a ledger is worse than an outage.
Outputs cite the source record. If an agent matched two lines, you can see exactly which two.