No items found.
cas client
Scaling sustainability data extraction with Generative AI for a Global Plastic Waste Reduction Initiative
A global non-profit working to eliminate plastic waste launched a platform to help map waste flows and support both researchers and member companies in improving waste management practices. Yet data acquisition remained a major bottleneck. Extracting figures from lengthy sustainability reports was manual, time-consuming, and prone to inconsistencies. To scale its impact, the organization needed an GenAI-powered solution to automate data extraction, reduce manual workload, and improve both accuracy and efficiency across the board.
30,000+
PDF pages processed automatically in under 24 hours
50,000
KPIs extracted and validated within two weeks
98%
time savings achieved in data collection and validation workflows
découvrez
Ce que nous avons mis en place
01
Enjeux
- Manual data extraction delayed access to insights, slowing down decision-making and waste management efforts
- Time-consuming, repetitive processes placed a heavy burden on analysts and operational teams
- Inconsistent definitions and formats across reports led to discrepancies in interpretation and validation
02
Notre approche
- Build a fully automated extraction workflow powered by Generative AI to minimize manual effort while ensuring high data accuracy
- Streamline processing and scaling using Databricks Workflows, improving efficiency and transparency across the pipeline
- Develop a self-service interface that allows users to review and validate extracted data—making it easy to contribute to the sustainability database without technical know-how
03
Résultats
- Scalable, collaborative data workflow enabled multiple users to extract data efficiently across large document sets
- Improved transparency and real-time monitoring through Databricks Workflows, allowing teams to track progress and optimize performance in real time
- Standardized data definitions and scope eliminated inconsistencies from manual extraction, ensuring consistency and trust in the collected data
Challenge
Our approach
Outcome
Contact