Reliable AI Access

Making model access dependable

An application should not need to solve provider integration, failure handling, and usage reporting from scratch. I worked on a shared gateway that gives teams a consistent way to use AI models.

My work covered the service and its day-to-day operation. That included supporting different request types, improving failure handling, and making usage easier to understand.

The decisions behind the service

A common API is useful only if its behavior is predictable. I worked on compatibility and fallback behavior so application teams could spend less time handling provider differences. I also developed usage reporting to help connect technical choices with their cost.

The work taught me to treat reliability and cost as part of the same design problem. A fallback needs to preserve a useful response without making spend or behavior harder to explain.