Zomma

Financial services teams run operations, compliance and risk work that depends on procedure, evidence and review, such as investigating compliance alerts and screening hits, tracing reconciliation breaks across portals and spreadsheets, and conducting periodic risk reviews against policy. This work is done manually on screen across bank and custodian portals, company registries, case managers, ticketing tools, PDFs, statements and internal systems. Public AI benchmarks do not measure whether an agent can actually perform this specific work correctly, making it hard for enterprises to trust automation with tasks that cannot fail.

Zomma builds computer-use agents that work on screen the way a person does, signing into existing portals, case managers and internal systems with their own credentials, so no API integration or migration is needed. Before going live, and after every model, agent or procedure change, each agent is run against a private benchmark built from the customer's own past cases and their reviewers' decisions, including edge cases and actions the agent must refuse. In production, a fleet of agents works the live queue with one agent per case, while a separate agent scopes, assigns and tracks the work as the fleet grows with the queue. Every step is recorded and replayable, agents only recommend rather than decide, and cases are routed to a named human reviewer based on low confidence, a policy threshold or a possible match, with routine cases signed off in batches.