We heard from hundreds of AI engineers frustrated with the current state of AI troubleshooting.

The biggest failure mode in production AI isn’t intelligence, it’s a lack of transparency.

When GenAI frameworks fail, accuracy drifts, and performance degrades without an incident response, telemetry data becomes the missing layer of truth. It reveals behavior, exposes failure models and clearly translates production incidents into solvable engineering problems.

If you don’t own your telemetry, you can’t control the output of your AI models and agents.

Collecting telemetry data today generally means one of two options:

01

Closed observability platforms

Out-of-the-box solutions offer limited options for customizable metrics.

What’s more, they require you to relinquish control of your data assets.

02

DIY, open source approach

Platforms like OTel are far more flexible and allow self-hosting of your data.

But they require a months-long setup and heavy investment of developer hours.

Our mission is to enable builders to productionalize GenAI and prove its ROI, while maintaining continuous visibility into the performance metrics that matter most to them.

When systems hit a failure point, monitoring isn’t enough. You need a clear path that isolates issues, and outlines the steps required to fix them fast. Given GenAI’s infrastructure complexity, this hasn’t been possible with the current off-the-shelf solutions.

Prove AI is purpose-built for capturing GenAI telemetry data. Setting the foundation for faster, more effective mean time to remediation.

With Prove AI you can:

Own and fully control your AI data assets

Set and monitor custom metrics

Improve mean time to remediate (MTTR) AI performance issues

The biggest failure mode in production AI isn’t intelligence, it’s a lack of transparency.

Collecting telemetry data today generally means one of two options:

01

02

Prove AI has done the heavy lifting so AI engineers can focus on their most pressing issues - production and performance.

We’re on a journey to make AI troubleshooting a more intuitive process that integrates within existing workflows and improves human-machine collaboration.