The operating problem
Commercial records often live across spreadsheets, accounting exports and conversations. The workflow problem is not merely calculation; it is preserving provenance, showing what changed and keeping the decision with the operator.
Mizanla turns those records into a structured operating view. AI can surface possible matches and next actions, while the business user confirms the actual transaction.
The system pattern
The reusable pattern is capture, normalise, validate, suggest and approve. Imported rows are mapped to a stable schema; reconciliation state is recorded; suggestions are generated from structured data; and an explicit approval step prevents an uncertain output from becoming an action.
The same principle is applied to adjacent operating workflows: machine preparation is separated from human authorisation, and each business surface receives only the access it needs.
What transfers to client systems
A useful AI workflow needs more than a model call. It needs a durable data model, traceable state, a clear owner and a safe exception path. Those lessons transfer directly to enquiry intake and CRM projects.
The case demonstrates product and workflow design under operating constraints; it does not substitute for independent customer outcome evidence.