A leading Indian diagnostics and stem cell company, serving over 5,000 partner labs nationwide, was stuck manually processing handwritten Test Requisition Forms (TRFs)\u00a0 a bottleneck that capped how fast it could grow. DCM Infotech built an Agentic AI solution using Google Vertex AI that reads and interprets handwritten TRFs with over 90% accuracy, letting the client onboard more partners without hiring more staff.<\/p>\n
The client is a well-established diagnostics and stem cell company in India, serving more than 5,000 labs and collection centers across the country. Every partner sends physical Test Requisition Forms the documents that specify which tests to run and carry the clinical context behind them. These forms are handwritten, dense, and often full of tick marks and shorthand notes that make them genuinely difficult to digitize.<\/p>\n
Before automation, each TRF passed through two people: one for data entry, one for verification. That two-person model is slow by design, and it doesn’t scale, growing the partner network meant growing the data entry team at roughly the same rate.<\/p>\n
TRF forms are a harder automation problem than they look. Handwriting styles vary across thousands of referring doctors and technicians, layouts are dense, and tick marks are frequently ambiguous. The client had already tried automating this process before working with DCM\u00a0 previous vendors couldn’t get past roughly 60% accuracy, which left the team skeptical that automation could work here at all.<\/p>\n
The client came to DCM Infotech after seeing DCM’s automation work on invoice processing for another enterprise client, and decided to test whether the same approach could hold up against a much harder document type.<\/p>\n