Coming soon
Change your AI models without breaking your application.
Aver compares a current and candidate model configuration on real workload examples, finds meaningful regressions, and records the evidence behind a release decision.
The decision
Is this change safe enough to ship?
Model swaps can lower cost or improve quality, but aggregate scores often hide the exact cases that changed. Aver is designed around the release decision rather than a generic benchmark.
Use your workload
Evaluate current and candidate configurations on prompts or cases that represent the behavior your application actually depends on.
Find the differences
Surface changed outputs, likely regressions, and the exact examples that deserve review—not only a single average score.
Apply a reliability policy
Choose the simplest, lowest-cost tested configuration that meets the standard you defined for the release.
What Aver will do
Focused by design.
Aver is a model-change assurance tool—not a full LLM operations platform or certification system.
Designed to
- Compare a current and candidate model or configuration
- Run a defined set of real or representative cases
- Show where behavior changed
- Support a simple pass or review policy
- Keep a record of inputs, settings, and results
Not designed to
- Certify an entire AI application end to end
- Guarantee that a model never fails in production
- Replace engineering or domain-expert review
Aver is coming soon.
If you have a model-change problem you’d like to discuss, get in touch.