The existing pricing tool was built on outdated technologies that made it increasingly difficult and costly to maintain. Every change request required significant development effort, and the architecture couldn't accommodate the growing complexity of the company's pricing models.
Data ingestion was limited to daily batch processing, which meant pricing decisions were always based on yesterday's data. In an energy market where spot prices shift constantly, this lag put the company at a competitive disadvantage when quoting large C&I customers.
The platform also lacked scalability. As data volumes grew and pricing models became more sophisticated, performance degraded. There was no environment for testing new pricing models or running simulations, forcing the team to validate changes in production. A significant operational risk for a revenue-critical system.
