The Problem
Modern vision AI models don't just misread receipts—they confidently fabricate them from scratch. A model might invent a store name, items, and prices that look completely plausible but never appeared on the paper. Expense management platforms, accountants, and invoice automation tools trust these outputs directly, leading to corrupted accounting records, false reimbursements, and audit failures that are nearly impossible to catch until it's too late.
What We're Building
ReceiptGuard is a validation API that detects vision model hallucinations in real-time. It cross-checks extracted receipt data (store, items, total) against multiple vision models simultaneously, runs reconciliation heuristics (do line items sum to the stated total? Is the store name plausible?), and returns a confidence score with risk flags—all before the data hits your accounting system. Drop it in between your receipt scanner and your expense platform in minutes.
Who It's For
Finance ops leads and accounting software vendors at mid-market companies (50–500 employees) using Ramp, Divvy, Expensify, or building internal expense automation. Also: accounting practices and invoice processing vendors handling 100+ receipts daily.
Key Features (Planned)
- Multi-model cross-checking: query multiple vision models simultaneously to detect disagreements and confidence anomalies
- Reconciliation heuristics: automatically verify that line items sum to stated totals, store names are plausible, and prices fall in expected ranges
- Risk scoring: confidence scores (0–100) with specific flags for hallucination patterns, not just pass/fail
- Sub-second validation: drop-in API between your receipt scanner and accounting system with <200ms latency
- Works with any vision model: open-source (Qwen, MiniCPM, LLaVA) or proprietary (Claude Vision, GPT-4V)—detect hallucinations regardless of source
We're validating this idea before writing a single line of code. If this resonates with you, we'd love your feedback:
When was the last time a receipt scanning error slipped into your accounting records undetected? How much did it cost to find and fix?
Check out the concept page and let us know what you think.
Built by Jenavus — AI-powered business intelligence
Top comments (0)