AI archaeology: the bottleneck behind failed automation

Most organizations do not fail with AI because the model is weak. They fail because system reality is undocumented.

The brownfield problem

Legacy environments contain years of exceptions, manual workarounds, and tacit knowledge. If this is not documented first, automation will produce inconsistent results. That is why Lights Out starts with AI archaeology: mapping how processes actually run, not how they are supposed to run.

The specification problem

AI agents cannot execute vague instructions. They require precise, executable specifications. We translate domain reality into clear Markdown rules, data requirements, and decision points. This is where Domain Translators matter.

Digital twin before production

Before an agent touches live data, workflows are executed in a digital twin of ERP/CRM environments. Scenario tests then verify outcomes: totals, tax logic, status flags, and dependencies must pass before production execution.

Niche focus is the path to true lights out

Generic factories get stuck in exceptions. Niche factories reach real autonomy. The narrower the domain, the better edge-case coverage and the higher operational reliability.