"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, telemetry data becomes the missing layer of truth. It tracks behavior, exposes failure patterns, and contextualizes this inside the models that compose your production apps. Without it, you're blind.

THE PROBLEM

Collecting telemetry data today generally means one of two options:

Closed observability platforms

Out-of-the-box solutions often offer limited options for customization for enterprise use cases.
And require you to relinquish control of your data assets.

99% open source approach

Prove platforms like OpenTelemetry are infinitely flexible and you retain ownership of your data assets.
But they require a months-long setup and a significant investment in developer hours.

This creates a dilemma for builders who want customizable solutions to meet their enterprise needs, but cannot afford the development resources or set up expensive observability infrastructure.

OUR MISSION

Prove AI has done the heavy lifting so AI engineers can focus on their most pressing issues —

production and performance.

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

When a system hits a failure point, monitoring isn't enough. You need a clear path that isolates issues and outlines the steps required to fix them. Prove AI observes the performance metrics, making the observability infrastructure simpler.

Prove AI is purpose-built for capturing clean telemetry data, finding the fastest path to resolution, and reporting this to relevant stakeholders.

WITH PROVE AI YOU CAN:

Own and fully control your AI data pipeline

Self-hosted and MIT licensed — your telemetry data never leaves your infrastructure.

Set and monitor custom metrics (KPIs)

Define the signals that matter to your team and track them alongside standard observability data.

Improve mean time to remediate (MTTR) AI performance issues

Surface root causes and blast radius before you touch a line of code.

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

Let's build together.

Download on GitHub