AI-Powered Invoice Processing for a Logistics Group

A logistics group operating across Kenya, Uganda, and Tanzania processed over 3,000 invoices, delivery notes, and customs documents monthly — all entered manually into their accounting system. The data entry team of 8 people struggled to keep up, errors were common, and month-end reconciliation took days. The company needed to scale operations without proportionally scaling headcount.

Quick Tech built an intelligent document processing pipeline using OCR and machine learning models trained on the client's specific document formats. We partnered with the client's finance team to understand their validation rules and exception handling workflows, ensuring the AI system matched their existing processes.

• Analyzed 500+ sample documents across 12 document types
• Trained custom ML models on client-specific invoice and form layouts
• Built automated extraction pipeline with confidence scoring
• Designed human-in-the-loop review for low-confidence extractions
• Integrated directly with existing accounting and ERP systems
• Built monitoring dashboard tracking accuracy and processing volumes

Documents are scanned or uploaded and automatically classified by type. The AI extracts key fields — vendor, amounts, dates, line items, reference numbers — and populates the accounting system. Each extraction includes a confidence score; documents below the threshold are routed to a human reviewer with pre-filled fields. The system learns from corrections, continuously improving accuracy.