The challenge
- The client provides inventory management services for industrial assets, and requires automated extraction of key information from a variety of structured and unstructured documents that is then stored in customer’s inventory system. Manual data entry slowed down operations and led to errors in inventory tracking.
- With 4,000 distinct tags, manually classifying each file was time-consuming, prone to errors, and slowed down workflow efficiency.
What we did
- We implemented an AI-driven document comprehension solution that uses OCR, NLP, and computer vision to extract and structure information from inventory documents and store them in customer databases.
- Our AI-driven document tagging system combining semantic similarity and two-stage classification. ModernBERT-based models quickly interpret document content and assign the correct tag.
Benefits for the client
- The solution reduced manual processing time, improved data accuracy, and improved asset tracking – and in result increased the inventory management efficiency.
- Tagging accuracy soared, while manual workload dropped significantly especially considering the large amount of available tags. The client can now manage high volumes of documents more reliably and at scale.