The challenge
The operator's back office processed a relentless volume of customer documents — orders, KYC files, and disputes — through a thirty-person manual team. Throughput was capped by headcount, error rates crept up under load, and every subscriber-growth milestone made the queue longer. Scaling the team further was the expensive answer to the wrong question.
Our approach
We started with the documents, not the models: two weeks profiling the real intake — scan quality, formats, edge cases — and a measured accuracy baseline for the existing manual process. The pilot targeted the single highest-volume document type, with classification, extraction, and validation tuned against production samples and every automated decision carrying a confidence score.
Low-confidence cases routed to a human review queue whose corrections fed back into the system, so accuracy improved with use. Only when the pilot held ninety-five percent accuracy on live traffic did processing expand to the remaining document types — reaching full production eight weeks after the pilot began.
The results
The engine now processes the full document intake around the clock at ninety-five percent accuracy, with humans handling only flagged exceptions. The thirty operators were redeployed from data entry to exception handling and customer-facing work, and processing time per document dropped from hours in a queue to minutes end to end.
Client name withheld. Engagement details shared with permission.
