Automated Purchase-Proof Processing
Sole architect and builder
Stack
- Laravel
- Claude Code
- multimodal extraction
Numbers
- ~$2,000 saved per study
- Submission-to-validated-data: days → minutes
- Same-week reporting on purchase behavior now feasible
Problem
Longitudinal purchase research requires validating that respondents actually bought what they say they bought. The traditional workflow involved a hybrid of human review and brittle OCR pipelines across receipts, online order confirmations, and product-screenshot submissions — slow, expensive, and inconsistent across receipt formats and retailers.
Approach
Replaced the hybrid review with a full AI-driven extraction pipeline. Multimodal models handle the visual variability across receipt types; a structured-output layer normalizes the extraction into the schema the analytics layer expects; an exception queue routes only true edge cases to human review.
Outcome
Roughly $2,000 saved per study. More importantly, the time from submission to validated data dropped from days to minutes, which changes what’s possible methodologically — same-week reporting on purchase behavior rather than weeks-out.