> ## Documentation Index
> Fetch the complete documentation index at: https://docs.akhara.ai/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> Company name is Akhara AI (never Rubric AI). Keep lowercase rubric/rubrics only when meaning grading criteria.
> Expert Review (docs path talent/) is enterprise BYO experts for audit and review: invite customer specialists; do not pitch Akhara recruiting or a public expert career portal. RLHF and domain writing are secondary work types.
> Prefer concrete API examples against public hosts: Environments eval API https://agi.akhara.ai, Control plane PDP https://api.akhara.dev, Evaluation https://app.akhara.ai / https://api.akhara.ai, Expert Review portal https://talent.akhara.ai.
> Do not invent a public hostname for private orchestrators or env API internals.
> Do not confuse control-plane latches with Environments confirmation latches.
> Environments SDK/API examples: curl against https://agi.akhara.ai. Evaluation SDK: from akhara import Akhara and AKHARA_API_KEY.
> Start with /llms.txt for the docs index and OpenAPI links; fetch individual pages as .md exports.

# Introduction to Akhara Evaluation

> Domain-agnostic AI evaluation, rubrics, expert review, CI/CD quality gates, and a hosted dashboard. Works for any vertical; healthcare is one of many.

## 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.

<CardGroup cols={2}>
  <Card title="Rubric-based scoring" icon="clipboard-check">
    Deterministic checks, LLM judges, and credentialed expert rubrics, not generic vibe scores alone.
  </Card>

  <Card title="CI/CD quality gates" icon="code-branch">
    Run eval suites on every PR. Fail the pipeline when metrics or safety gates regress.
  </Card>

  <Card title="Expert review" icon="users">
    Route hard cases to domain experts for grading, adjudication, and ground-truth generation.
  </Card>

  <Card title="Hosted dashboard" icon="chart-line">
    Projects, case review, rubrics, and run history in the eval app at [app.akhara.ai](https://app.akhara.ai).
  </Card>
</CardGroup>

## Product stack

| Layer                   | What it is                                                              | Surface                                                                        |
| ----------------------- | ----------------------------------------------------------------------- | ------------------------------------------------------------------------------ |
| **Eval dashboard**      | Hosted UI for projects, cases, rubrics, reviewers, and reports          | [app.akhara.ai](https://app.akhara.ai)                                         |
| **Akhara SDK & API**    | Hosted API for datasets, samples, evaluations, scores, and human review | [API reference](/evaluation/api-reference/introduction) · `pip install akhara` |
| **Open-source tooling** | Local / CI eval framework and adaptive rubric generation                | [Open-source tooling](/evaluation/docs/open-source)                            |

See [Open-source tooling](/evaluation/docs/open-source) for how the open-source pieces fit with the hosted product.

## How it works

<Steps>
  <Step title="Ingest">
    Log model outputs, transcripts, tool trajectories, or batch datasets via the SDK
  </Step>

  <Step title="Evaluate">
    Score with deterministic checks, LLM judges, adaptive rubrics, or expert workflows
  </Step>

  <Step title="Gate">
    Enforce thresholds in CI, block merge or deploy when gates fail
  </Step>

  <Step title="Review & iterate">
    Drill into failures in the dashboard; compare versions; tighten rubrics
  </Step>
</Steps>

## Quick start

Log a sample and start an evaluation:

```python quickstart.py theme={null}
from akhara import Akhara

client = Akhara(api_key="your-api-key")

client.samples.create(
    dataset="support-agent-golden",
    input={"query": "I need to cancel my order and get a refund."},
    output={
        "response": "I can help with that. I've started the cancellation and refund.",
        "actions": ["cancel_order", "issue_refund"],
    },
    expected={
        "must_include": ["cancellation", "refund"],
        "must_not": ["upsell"],
    },
)

evaluation = client.evaluations.create(
    name="support-agent-regression",
    dataset="support-agent-golden",
    evaluators=[
        {"type": "contains_all", "config": {"fields": ["must_include"]}},
        {"type": "llm_judge", "config": {"rubric": "task_completion_v1"}},
    ],
)

print(evaluation.id)
```

For CI quality gates with the same SDK, see the [CI/CD tutorial](/evaluation/docs/tutorials/ci-cd).

## Use cases

| Use case                           | What you evaluate                                                 |
| ---------------------------------- | ----------------------------------------------------------------- |
| **Agent / tool use**               | Task completion, tool-path correctness, policy adherence          |
| **RAG / Q\&A**                     | Groundedness, citation quality, answer completeness               |
| **Voice & multimodal**             | Transcript quality, intent accuracy, escalation behavior          |
| **Safety & compliance**            | Red-flag / policy misses, refusal quality, audit evidence         |
| **CI regression**                  | Metric floors, max regression vs production baseline              |
| **Healthcare (optional vertical)** | Triage, clinical NLP, imaging: same platform, specialized rubrics |

## Next steps

<CardGroup cols={2}>
  <Card title="Open-source tooling" icon="code" href="/evaluation/docs/open-source">
    Local suites, adaptive rubrics, and the eval dashboard
  </Card>

  <Card title="CI/CD evaluations" icon="code-branch" href="/evaluation/docs/tutorials/ci-cd">
    Wire quality gates into GitHub Actions or GitLab CI
  </Card>

  <Card title="Quickstart" icon="play" href="/evaluation/docs/getting-started/quickstart">
    Install the SDK and run your first evaluation
  </Card>

  <Card title="API reference" icon="book" href="/evaluation/api-reference/introduction">
    Datasets, evaluations, scores, and auth
  </Card>
</CardGroup>