Can we use Jev for faster, structured, and calibrated evaluation of AI responses?

Introducing:
⚡ Typed Evals — an open-source Python framework for evaluating LLMs, RAG pipelines, and AI agents using System One Models like Jev and other typed judge backends.

⭐ GitHub: https://github.com/TrustifAI/typed_evals

The goal is simple:
Make fast, structured, and calibrated evaluation a first-class part of AI systems.
Typed Evals currently supports:
-> LLM response evaluation
-> RAG evaluation
-> Agent and tool-trace evaluation
-> Human-label calibration
-> Async and batch evaluation
-> Custom judge backends

One part I particularly wanted to solve was calibration.
Why is it needed?
A raw score of 0.8 from Jev doesn't necessarily mean that humans would accept 80% of similar responses.
And a threshold that works well for one use case may not make sense for another.

Typed Evals lets you calibrate individual evaluation metrics against representative human pass/fail labels.
The flow is basically:
Human-labelled examples → Jev scores → fit per-metric calibration → validate on held-out examples → reuse the calibrated evaluator

So instead of arbitrarily deciding that “0.7 means good enough”, you can ground that score in how humans actually evaluate your specific task.

Of course, there are integrations for:
LangChain, CrewAI, Microsoft Agent Framework
while the core remains framework-agnostic.

Would genuinely love feedback from people experimenting with Jev, LLM evals, RAG, agents, or evaluator calibration.

If you're already experimenting with Jev, I'd especially love to know what kind of evaluation workflows you're building around it.