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Published
May 2026

How One Eyewear Brand Lifted Checkout 10% to 15%

A DTC eyewear team piped prescription images through an OCR-and-Claude pipeline — lifting checkout from 10% to 15% and cutting $40K in contractor costs to unlock $200K in revenue.

$200K

Unlocked in projected revenue

< 4 weeks

Implementation Time

Not disclosed

Project Cost
the challenge
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.
what they built
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%.
best fit for
DTC e-commerce brands with regulated or medically sensitive product categories; custom AI dev shops targeting healthcare-adjacent retail.
Ai ROLE
Claude validates and interprets medical information extracted from prescription images by the OCR layer, determining whether each prescription meets the required clinical parameters for the ordered eyewear. LangGraph orchestrates the multi-step verification workflow, routing outputs to either automated approval in Shopify or exception queues for human review.
impact

$200K Projected Revenue Unlocked

Checkout conversion improvement from 10% to 15% translates to an estimated $200K in additional annual revenue.

$40K Contractor Costs Eliminated

Manual prescription verification team replaced entirely by automated pipeline, saving $40K annually in contractor costs.

4–6 Week Build-to-Deployment

Full pipeline — OCR extraction, AI validation, Shopify integration — shipped from project start to working deployment in 4–6 weeks.
implementation complexity
The implementation required building a custom multi-step pipeline combining OCR for image extraction, Claude for medical validation, LangGraph for orchestration, and AWS Lambda for serverless execution — all integrated directly with Shopify's checkout flow. The medically sensitive nature of prescription verification adds compliance and accuracy requirements that significantly increase the engineering complexity.

Nitesh Pant

Co-founder
Devdash Labs
Co-founder and COO of DevDash Labs, an applied AI research and development company that builds products to solve the hardest problems for SMBs looking to adopt AI and automate their processes.
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industry
Retail & E-Commerce
Healthcare & Life Sciences
business organization
Operations
Customer Service
AI TYpe
Document Processing & Extraction
Process Automation (RPA + AI)
value type
Revenue Growth
Cost Reduction
Headcount Avoidance
frequently asked questions
How did a mid-sized e-commerce brand lift checkout conversion from 10% to 15% with document-processing AI?

The experts built a prescription-verification pipeline in layers, since clinical accuracy left zero tolerance for error. OCR extracts data from uploaded prescription images, an AI model validates it against clinical criteria, and an orchestration layer routes verified prescriptions to automated approval while flagging exceptions for human review. Verification time fell from days to minutes and checkout conversion rose from 10% to 15%.

What AI tools and models were used for the verification pipeline?

The pipeline used an OCR model for extraction, Claude to validate and interpret the medical data, and LangGraph to orchestrate the multi-step workflow, running serverless on AWS Lambda and integrated directly with Shopify. The approach combined document processing and extraction with process automation.

What results did the e-commerce brand achieve?

Checkout conversion improved from 10% to 15%, an estimated $200K in additional annual revenue, and the contractor verification team was replaced entirely, eliminating about $40K a year in costs while giving the brand a compliance layer it owns outright.

How long did the build take?

The full pipeline was built and deployed in 4–6 weeks.

Who is this document-processing AI approach best for?

DTC e-commerce brands with regulated or medically sensitive product categories, and custom AI development shops targeting healthcare-adjacent retail.

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