The Challenge

ChiveLab AI is designed to provide comprehensive information about plants, fruits, common foods, recipes, and branded foods. They trained their models using data extracted from 20+ top-tier supermarkets and retailers internationally. They built their data pipelines using a Cloud ELT solution. However, the data they receive is often inconsistent and not standardized, making it challenging to feed their models. To address this problem, ChiveLab had to constantly update their 10+ data pipelines every five days and dedicate one data engineer for over 10+ hours weekly to monitor the data pipeline. This issue slowed the training process and increased the project's overall cost.

DaBot’s Solution

ChiveLab successfully implemented DaBot's cutting-edge solution for their data onboarding use case. With just one bot in place of 10 existing data pipelines, they were able to automate their DataOps process and cut down effort from over 10 hours to just 2 hours. The implementation took less than a couple of hours, and the savings were significant. DaBot's powerful automation capabilities have freed up valuable time for ChiveLabs' data engineers, enabling them to focus on extracting meaningful insights for their organization. With DaBot, onboarding new vendor data is just 5 clicks away, making it the perfect solution for streamlining your data pipeline and increasing productivity.

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