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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.

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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.

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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.

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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.