A DTC eyewear brand was losing customers at prescription verification — a manual, contractor-dependent step that introduced delays and errors, holding checkout conversion at 10%. Every failed verification was a lost sale. Scaling the business meant either hiring more contractors or finding a way to automate a medically sensitive process with no tolerance for mistakes.
Devdash Labs built a custom prescription verification pipeline using OCR to extract data from uploaded prescription images, Claude to validate and interpret the medical information, and LangGraph to orchestrate the multi-step verification workflow. The system integrated directly with Shopify to trigger automated approvals or exception routing. Built in 4–6 weeks, the pipeline eliminated the contractor bottleneck, reduced verification time from days to minutes, and created a fully automated compliance layer the brand now owns outright.
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%.
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