# Overview

LayerLens Stratix is the AI evaluation platform for teams who need to know — before customers do — whether a model, a prompt, or an agent is good enough to ship. Three customer-facing experiences cover the full evaluation lifecycle: **Stratix Public** for anonymous research, **Stratix Premium** for team workflows, and the **Python SDK** for programmatic access.

{% hint style="info" %}
**Want to see Stratix in action?** [Schedule a 30-minute demo →](https://github.com/LayerLens/gitbook-full/blob/main/01-introduction/schedule-a-demo.md) — or jump straight into [Stratix Public](/2.-get-started/public.md) (no signup) and the [Free tier of Premium](/2.-get-started/sign-up.md).
{% endhint %}

<table data-view="cards"><thead><tr><th></th><th></th><th></th></tr></thead><tbody><tr><td><strong>What is it?</strong></td><td>High Level Overview and the "two products" framing.</td><td><a href="/pages/7AP10HeZHFQJNsSPJSdp">Read</a></td></tr><tr><td><strong>Three experiences</strong></td><td>How Public, Premium, and the SDK fit together.</td><td><a href="/pages/eUgq64V04y1wAtdYcXCu">Read</a></td></tr><tr><td><strong>Agentic evaluations</strong></td><td>Pre- and post-deployment quality gates for multi-step agents.</td><td><a href="https://github.com/LayerLens/gitbook-full/blob/main/01-introduction/agentic-evals.md">Read</a></td></tr><tr><td><strong>Platform architecture</strong></td><td>The components: catalog, evaluation engine, judges, traces, learning.</td><td><a href="https://github.com/LayerLens/gitbook-full/blob/main/01-introduction/platform-architecture.md">Read</a></td></tr><tr><td><strong>Personas and paths</strong></td><td>Builder, Operator, Researcher, Admin, Buyer — pick yours.</td><td><a href="/pages/OmgFTWVrbwoyktjVVQLT">Read</a></td></tr><tr><td><strong>Quarterly reports</strong></td><td>Q1-Q4 research reports on model performance and benchmark trends.</td><td><a href="https://github.com/LayerLens/gitbook-full/blob/main/01-introduction/quarterly-reports.md">Read</a></td></tr><tr><td><strong>Pricing tiers</strong></td><td>Free, Premium, Enterprise — what's included and what's not.</td><td><a href="https://github.com/LayerLens/gitbook-full/blob/main/01-introduction/pricing-tiers.md">Read</a></td></tr><tr><td><strong>How Stratix compares</strong></td><td>Stratix vs Braintrust, LangSmith, Galileo, Promptfoo, Arize.</td><td><a href="/pages/PRM2ILOCxx2EitM0RMtD">Read</a></td></tr><tr><td><strong>Trust and security</strong></td><td>Encryption, compliance, tenant isolation, audit, BYOK.</td><td><a href="https://github.com/LayerLens/gitbook-full/blob/main/01-introduction/trust.md">Read</a></td></tr><tr><td><strong>Schedule a demo</strong></td><td>Book a 30-minute walk-through — or start hands-on today.</td><td><a href="https://github.com/LayerLens/gitbook-full/blob/main/01-introduction/schedule-a-demo.md">Read</a></td></tr><tr><td><strong>Brand assets</strong></td><td>Product names, logos, voice, and boilerplate for partners.</td><td><a href="https://github.com/LayerLens/gitbook-full/blob/main/01-introduction/brand-assets.md">Read</a></td></tr></tbody></table>

## The Stratix Workflow

Six stages from picking a model to governing it in production:

1. **Select** — review leaderboards and benchmarks; optionally compare candidates side-by-side.
2. **Build** — wire your code or agent to emit traces.
3. **Observe** — see real production behavior across surfaces.
4. **Evaluate** — score outputs with LLM judges, LLM-backed scorers, and code graders.
5. **Improve** — tune prompts and judges (GEPA); re-run.
6. **Govern** — enforce gates in CI/CD and across your org.

Every concept, how-to, tutorial, and recipe in these docs maps to one of these stages. [Learn the workflow →](/1.-introduction/the-stratix-workflow.md)


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.layerlens.ai/1.-introduction/01-introduction.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.