Automoat
local-first moat discovery, evaluation, and build
Automoat
Automoat helps you discover what is actually proprietary in your business, turn that data into something testable, and prove whether it creates an advantage through research, benchmarks, retrieval, adaptation, and local-first workflows.
The interesting part is not “AI” in the abstract. It is the workflow data, decisions, edge cases, and outcomes a business already has and usually underestimates.
Core loop
Research the domain, map likely moat ideas, and decide what is worth testing.
One entry point is business-first: understand the workflows, identify likely proprietary advantage, and produce a plan for what data to collect or structure next.
Analyze a dataset directly, generate evals, and benchmark approaches.
The other entry point is dataset-first: inspect the records, generate moat hypotheses, compare baselines, and find out whether the data actually creates lift or not.
Dallas electrical permits and inspections.
That is the narrow real-world case for showing how local workflow records can become a durable advantage instead of just another pile of business data.