N° 01
Context & Business Challenges
Order and quotation processing relied on heterogeneous documents with inconsistent structures and naming conventions. Matching these inputs with internal catalogs required heavy manual work and expert validation.
The challenge was to significantly reduce processing time while improving matching accuracy and securing decisions through business expertise
N° 02
What we built & delivered
Intelligent document processing platform
A system designed to aggregate, normalize, and structure data from multiple internal and external sources.
AI-powered matching engine
NLP-based models identify and match products across heterogeneous catalogs, reducing dependency on manual reconciliation.
Expert-in-the-loop validation system
Domain experts validate or adjust AI recommendations, with each correction continuously improving model performance over time.
N° 03
Operational outcomes
- Faster order and quotation processing
- Improved product matching accuracy
- Reduced manual workload through automation