AI for financial services:
explainable by design.

Agents ingest unstructured financial documents — PDF term sheets, SWIFT messages, freight invoices — and extract, normalise and match line items against your core ledger. Unlike template-based OCR, multimodal models read document semantics, so new formats and languages are typically handled without reconfiguration. Every match is logged with a replayable audit trail, and anything below a confidence threshold goes to a person rather than to the ledger.

By architecture rather than by assurance. On an on-premise or sovereign-cloud deployment, PII and financial records have no route to a public endpoint and are not sent to a provider that could train on them. Access is scoped by role, every material decision leaves a replayable trail, and data is encrypted in transit and at rest. Where a hosted model is used, we record the provider's retention and training terms in the architecture document, at the time of writing, and you should confirm them against the provider's current terms. What we do not bring is assurance: QQuantum.ai holds no security certifications, so the evidence we produce supports your audit rather than substituting for it.

Yes. Instead of a rewrite, we engineer AI-native interfaces that wrap existing COBOL backends and core banking databases. Agents read from, and write to, legacy systems through validated, reviewed interfaces with a schema check on every write, so workflows modernise while the underlying mainframe stays untouched.

We will not quote you a percentage, because any number we have not measured on your process is marketing. What we will do is measure the current cost of a specific workflow — reconciliation breaks, alert triage, document intake — build the case on your own figures, and tell you if it does not clear. The cost model page sets out the method, including the lines usually left out.