ReceiptsAI

ReceiptsAI is built on a proprietary multimodal deep-learning stack that marries computer vision, natural-language processing, and adaptive categorization. The vision module uses convolutional neural networks fine-tuned on millions of receipt images to detect text even under glare, crumples, or low-resolution conditions. Simultaneously, a transformer-based language model parses every extracted string, understanding context such as merchant names, line-item descriptions, and tax codes. The final layer is an auto-categorization engine that continuously retrains itself on anonymized user feedback, ensuring that the system grows smarter with every upload. All of this is delivered through a lightweight REST API that averages sub-second response times, making real-time expense classification a reality.

Intelligent Image Capture
The mobile capture flow automatically detects edges, corrects perspective, and flags blurry frames before upload, eliminating the need for retakes. Users simply point, shoot, and watch the AI do the rest.

Multi-Format Support
Unlike niche OCR tools, ReceiptsAI ingests PDF bank statements, JPG/JPEG/PNG receipts, and even multi-page invoices in one seamless upload queue. Future releases will add Excel and CSV ingestion for corporate card feeds.

Granular Data Extraction
Beyond total amounts and dates, the engine surfaces merchant addresses, tax breakdowns, tip allocations, and even loyalty point redemptions. Finance teams gain forensic-level detail without opening a single spreadsheet.

Auto-Categorization & Custom Rules
Out-of-the-box categories such as Travel, Meals, Software, and Office Supplies cover 90% of use cases. Power users can layer on conditional rules—e.g., “Mark Uber rides over $50 as ‘Travel – Executive’”—to enforce policy compliance.