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
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