How to spot an AI automation worth building
Volume, variability, return, control and a concrete way to decide whether to build.
Read resource↗OPEN KNOWLEDGE / 001—010
Guides and lab notes you can use before hiring me. Decisions, tests and architectures explained from practical work.
Subscribe via RSS↗Take it into a real session and document scope, experience, security, AI, games, evidence and the release decision.
Download in English↓A shareable summary of consulting, training, Beta Lab, the laboratory and public evidence. No rates, inflated promises or sensitive names.
Download brief↓Before automating, do the maths.
Estimated current workload. Deduct review, maintenance and exceptions when calculating savings.
Sample data
Volume, variability, return, control and a concrete way to decide whether to build.
Read resource↗Quality, attacks, tools, fallback, cost and regression before launch.
Read resource↗What each piece solves and how to design autonomy that produces evidence.
Read resource↗Managers, handoffs, work contracts, evaluation and four verifiable exit states.
Read resource↗Code, configuration, hashes, verified mechanics and honest agent-evidence limits.
Read resource↗Turn outcome, process, data, risk and success criteria into a copyable document.
Read resource↗An explicit baseline for time, volume, people and rework, without promising future savings.
Read resource↗Separate the 12-testers-for-14-days threshold from the evidence, builds and corrections that explain the test.
Read resource↗Limited or open Playtest, keys, internal access or a beta branch according to the game, audience and decision.
Read resource↗Internal groups, external testers and public links separated by review, identity, capacity and evidence.
Read resource↗