AI Receipt Validation
Validate Receipts at Scale with OCR + AI Ensemble
A two-model validation pipeline that reads every receipt - crumpled, faded, digital, or screenshot - extracts key fields with dedicated OCR, reconciles them with multimodal AI, checks them against your campaign rules, and returns a clear VALID, MAYBE, or INVALID verdict with the reason.
receipt_photo_001.jpg
Uploaded for validation
Store
BigMart
Total
$47.82
Date
Jul 12, 2026
Products
3 matched
The Challenge
Manual receipt verification does not scale. At campaign volumes, errors are expensive and slow human review is a bottleneck.
Volume Exceeds Capacity
Large consumer campaigns receive tens or hundreds of thousands of receipt submissions. A manual review team cannot process that volume within the campaign window.
Single-Engine OCR Fails
Standard OCR engines struggle with crumpled receipts, faded thermal prints, screenshots, and low-quality phone photos. Confidence drops and fields are misread.
Errors Are Expensive
Accepting an invalid receipt costs prize money. Rejecting a valid one loses a customer. Both outcomes are costly, and at scale, even a 1% error rate adds up.
Fraud Slips Through
Edited prices, duplicate submissions, and photoshopped receipts pass manual review because human reviewers miss subtle digital manipulation artifacts.
How It Works
From Upload to Verdict in Three Seconds
Receipt Uploaded
The consumer submits a receipt photo through the campaign portal - paper receipt, digital e-receipt, thermal print, screenshot, or PDF.
Primary OCR Extraction
AWS Textract reads every line of the receipt with per-field confidence scores, extracting store name, date, line items, prices, and total amount.
AI Reconciliation Pass
Google Gemini receives the extracted text and the original image, cross-checks the fields, catches what OCR missed, and applies campaign validation rules.
Verdict Returned
A clear VALID, MAYBE, or INVALID verdict is returned with the reason. Ambiguous cases are surfaced for human review rather than silently accepted or rejected.
300K+
Receipts Validated
In a single campaign for a global brand
<3s
Per Receipt
Average time from upload to verdict
99.2%
Accuracy
Against human-reviewed benchmark
<1%
Human Review
Only ambiguous MAYBE cases need review
Capabilities
What Gets Automated
Two-Model Ensemble
Dedicated OCR (AWS Textract) for extraction plus multimodal LLM (Google Gemini) for reconciliation - catches errors that either model alone would miss.
Any Receipt Condition
Reads crumpled, faded, folded, thermal, digital, screenshot, and low-quality phone photos. The ensemble design handles conditions that single-engine OCR cannot.
Configurable Rules
Validation rules are defined in plain English or structured filters - qualifying stores, date ranges, product categories, minimum spend amounts.
Three-Tier Verdicts
Every receipt gets VALID, MAYBE, or INVALID with a clear reason. MAYBE cases are routed for human review rather than auto-decided.
Tamper Detection
Detects edited prices, mismatched fonts, inconsistent pixel density, and cut-and-paste artifacts in receipt images.
Duplicate Detection
Catches the same receipt submitted multiple times, even with cropping, rotation, angle changes, or re-photographing from a screen.
Field-Level Extraction
Extracts store name, address, date, time, individual line items with prices, subtotal, tax, total, and payment method.
Fraud Flagging
Surfaces fraud signals - duplicate hashes, impossible date-store-product combinations, digital modification artifacts - for review without auto-rejecting.
What Gets Extracted
Store and Date
Store name, address, date, and time extracted and validated against campaign-qualifying criteria.
Line Items
Individual products with descriptions, quantities, and prices extracted from receipt text.
Totals and Payment
Subtotal, tax, total amount, and payment method identified and cross-checked for arithmetic consistency.
Any Format
Paper receipts, thermal prints, digital e-receipts, screenshots from retailer apps, and PDFs.
Any Quality
Low-light phone photos, crumpled receipts, faded thermal prints, and partially occluded images.
Fraud Signals
Duplicate hashes, edited pixels, impossible combinations, and digital manipulation artifacts.
FAQ
Frequently Asked Questions
Why use two models instead of one?
A single OCR engine reads text well from clean documents but struggles with crumpled, faded, or low-quality receipts. The second model (Google Gemini) receives both the extracted text and the original image, cross-checks the extraction, catches misreads, and applies campaign logic. The ensemble catches errors that either model alone would miss.
What receipt conditions can it handle?
Paper receipts, thermal prints, digital e-receipts, screenshots from retailer apps, PDFs, crumpled receipts, faded prints, low-light phone photos, and partially occluded images. The two-model design handles poor conditions much better than single-engine OCR.
How are validation rules configured?
Rules are defined in plain English ('qualifying stores: BigMart, SuperStore; date range: June 1 to August 31; minimum spend: $20') or as structured filters. Rules can be updated mid-campaign and apply to new submissions immediately.
What happens with ambiguous receipts?
Ambiguous cases receive a MAYBE verdict with a detailed explanation of what was unclear. They are routed for human review rather than auto-accepted or auto-rejected. This keeps false-positive and false-negative rates low.
Can it detect fraudulent receipts?
Yes. The pipeline flags duplicate submission hashes, inconsistent store-product-date combinations, digital image editing artifacts (mismatched fonts, pixel density changes), and screenshots of photos rather than original captures. Flagged entries go to review, not auto-rejection.
What scale has it been tested at?
The system validated over 300,000 receipt submissions in a single campaign for a global FMCG brand, with 99.2% accuracy against a human-reviewed benchmark. Under 1% of submissions required human review.
Request a Demo
See how the agent works with your data. Fill in your details and our team will set up a personalised walkthrough for you.