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Why Akhara Evaluation?

You need to know whether your model or agent is getting better, before that change ships. Akhara Evaluation gives you versioned datasets, deterministic and LLM judges, expert rubric workflows, and CI quality gates that block regressions in pull requests. The platform is domain-agnostic: the same objects (datasets, samples, rubrics, evaluations, reviewers) apply whether you score support agents, RAG systems, voice bots, or clinical workflows. Domain-specific pages under Voice, Notes, and Imaging are optional vertical depth, not the product definition.

Rubric-based scoring

Deterministic checks, LLM judges, and credentialed expert rubrics, not generic vibe scores alone.

CI/CD quality gates

Run eval suites on every PR. Fail the pipeline when metrics or safety gates regress.

Expert review

Route hard cases to domain experts for grading, adjudication, and ground-truth generation.

Hosted dashboard

Projects, case review, rubrics, and run history in the eval app at app.akhara.ai.

Product stack

See Open-source tooling for how the open-source pieces fit with the hosted product.

How it works

1

Ingest

Log model outputs, transcripts, tool trajectories, or batch datasets via the SDK
2

Evaluate

Score with deterministic checks, LLM judges, adaptive rubrics, or expert workflows
3

Gate

Enforce thresholds in CI, block merge or deploy when gates fail
4

Review & iterate

Drill into failures in the dashboard; compare versions; tighten rubrics

Quick start

Log a sample and start an evaluation:
quickstart.py
For CI quality gates with the same SDK, see the CI/CD tutorial.

Use cases

Next steps

Open-source tooling

Local suites, adaptive rubrics, and the eval dashboard

CI/CD evaluations

Wire quality gates into GitHub Actions or GitLab CI

Quickstart

Install the SDK and run your first evaluation

API reference

Datasets, evaluations, scores, and auth