Devdash Labs began with a clear constraint: the verification process had zero tolerance for error — clinical accuracy and compliance requirements meant the solution had to match or exceed human accuracy before replacing the contractor team. The pipeline was built in layers. An OCR model handled the first step, extracting prescription data from uploaded images across varying formats and quality levels. Claude processed the extracted data against clinical validation criteria, determining whether each prescription met the parameters required for the ordered eyewear. LangGraph orchestrated the full multi-step verification sequence, routing verified prescriptions to automated Shopify approval and flagging exceptions for a human review queue. The serverless architecture on AWS Lambda was chosen to handle variable order volume without infrastructure overhead — critical for a DTC brand with seasonal spikes and limited engineering resources for ongoing system management. The full pipeline was built and deployed in 4–6 weeks. The outcome was a system the client owns outright rather than a contractor dependency that would need to scale with order volume. Verification time fell from days to minutes, and checkout conversion improved from 10% to 15%.