Scaling AI training & production data
This case study explores a successful data implementation project undertaken by a leading artificial intelligence company which operates a proprietary LLM for F500 clientele. The project involved selectively fetching data on a query basis from multiple customer-specific data sources and delivering into a live production system used by end clients, ensuring full data integrity and security throughout the process.
Challenges in scaling
In the fast-paced world of scaling AI technology, configurable access to proprietary and third party data is paramount to differentiation. Scaling selective access to data while managing OPEX/CAPEX targets is common challenge many exec teams are grappling with.
Problem
The Client needed to onboard data rapidly, but to do so in a specific cost-effective way that optimized the movement of data within their own environment, and to do so for a range of use cases that contemplated different ways in which data would be selectively drawn upon to feed iterations of the core large language model.
Client Results
The EASL Platform deployed against a proof-of-concept that showed how easily internal teams could customize each implementation, drawing data based on specific queries, while optimizing when data hits S3 buckets to mitigate cloud servicing costs. The Client immediately wanted to roll that platform out across all existing and future data source implementations. Further, over the first six months, the data error rate plummeted, and for the few errors that did occur, the average reconciliation time was 9 minutes or less given the platform's automated alerts.