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

# Merion Intelligence Factory

> Turn evidence from real work into reproducible capability tests and controlled improvement decisions.

# Start with real work

Merion helps applied AI and research-engineering teams learn which change will improve a real workflow—and preserve the evidence required to defend that decision.

The system begins with genuine work rather than a synthetic benchmark. It captures the starting state, execution context, outcome, human corrections, and economic value of a work episode. Reviewed episodes can then become reproducible evaluation tasks and controlled experiments.

## The learning loop

```text theme={null}
real work
  → reviewed outcome
  → related examples
  → reproducible evaluation
  → controlled experiment
  → release decision
  → production evidence
```

Merion is designed to sit above individual models and agent harnesses. A factory can observe work performed through Codex, OpenCode, a manual workflow, or another integrated harness without forcing a team to replace its execution environment.

## What you can do today

* Initialize a local Intelligence Factory in a new folder, an existing directory, or a Git repository.
* Detect local sources and available Codex or OpenCode harnesses.
* Capture a genuine work episode with content-addressed starting-state evidence.
* Close the episode with human outcome review and economic context.
* Turn governed, reviewed episodes into audited evaluation tasks.
* Exchange bounded evidence through ATIF and Harbor adapters.

## Start here

Follow the [quickstart](/quickstart) to link the private-alpha CLI, initialize a factory, and capture your first real-work episode.

> Merion is currently a private-alpha, local-first system. Public package installation, hosted execution, autonomous training, and production customer-data ingestion are not available yet.