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

# Why EigenPal

> Automate repetitive work with AI, with datasets and evaluators for measuring output quality.

EigenPal is for repetitive tasks that sit between manual work and brittle
scripts. Document processing is the most common case, but the same model works
for any task you can describe as a sequence of steps with structured inputs and
outputs.

It addresses two practical problems.

## The same work gets rebuilt and redone

Teams often rebuild the same extraction, classification, or routing logic in
several places. One app has a script, another has a prompt, another has a manual
review step. They drift, cost more to run, and return slightly different output.

EigenPal makes that work a reusable **artifact**. You define it once as a
workflow, version it, and call it from each product or internal tool. For
documents, processed results can be cached and reused so expensive parsing and
extraction do not repeat unnecessarily. See
[Artifacts as files](/concepts/artifacts).

## You cannot assume the output is correct

An automation that works on one input is not ready. Models change, vendors
change, edge cases appear, and a workflow can start returning wrong data without
throwing an error. Spot-checks do not give downstream teams a stable quality
signal.

EigenPal treats output quality as something you measure. You attach a dataset
and evaluators that define the expected result, tune until the workflow clears a
threshold, and keep running those checks when prompts, models, or code change.
See [Evaluations](/concepts/evals).

## The development model

Build AI automation like software: keep definitions in files, review changes,
run tests, compare versions, deploy, and add real failures back to the test set.

Start with [How it works](/concepts/how-it-works).