AIACTIVE DEVELOPMENT

INVENTO AI

// THE PROBLEM

What challenge did this address?

Managing business invoices and maintaining accurate structured inventory records manually causes severe operational bottlenecks. Legacy OCR systems fail to extract relational data accurately, resulting in inventory mismatches, human error, and delayed fulfillment cycles.

// THE SOLUTION

How was it engineered?

Developed an end-to-end intelligent invoice pipeline using Retrieval-Augmented Generation (RAG) and structured extraction agents. The system utilizes parallel execution queues to process bulk invoices simultaneously, match line items with active inventory systems, and automatically synchronize stock level updates across databases.

// TECH STACK

Next.js

Firebase

Python

RAG

// KEY FEATURES

Core Implementation Details

↳RAG-driven invoice parsing & contextual schema mapping
↳Multi-agent task orchestration utilizing Gemini & custom extractors
↳Parallel bulk upload execution queue with visual progress tracking
↳Automated stock level adjustments & vendor matching rules

// SYSTEM OUTCOMES

Verifiable Performance Metrics

✓Up to 40x faster invoice-to-inventory processing times
✓99.2% extraction accuracy on complex, multi-page tabular invoices
✓Complete elimination of manual data entry errors for early adopters