Encounter ambiguity
A realistic scenario includes an AI-assisted recommendation, incomplete evidence, and a decision that still has to be made.
Founder & method
Built to examine the judgment around the answer.
ThinkProof was created by M. Fawad Butt around a practical concern: AI adoption can accelerate output faster than organizations can see how people are evaluating it.
Experience the method →Why ThinkProof exists
The workforce is not the last mile of AI adoption. It is part of the design intelligence.
ThinkProof focuses on the human decisions that sit between an AI-generated answer and a real-world action: what evidence deserves weight, what assumptions remain hidden, how confidence should be calibrated, and when a person should verify, escalate, or stop.
The work is founder-led. The current product is deliberately developmental so its limits stay visible while the scenarios, rubric, feedback language, and delivery model are improved.
Method in plain language
Scenario, decision, reason, reflection.
The method is designed to create evidence for a developmental conversation—not a permanent label.
Choose an action
The participant decides whether to trust, verify, challenge, escalate, pause, or reject.
Defend the choice
The reasoning is examined against a defined developmental rubric, with uncertainty kept visible.
Practise a better habit
Feedback points to a concrete next experiment rather than presenting a score as a verdict.
Evidence before claims
Trust should be earned in the open.
ThinkProof does not publish invented customer counts, anonymous testimonials, artificial scarcity, or outcome claims that have not been measured. Pilot evidence will be added only when it can be represented accurately and with permission.
Current limitations, identity separation, scoring support, and human-review boundaries are documented in the trust model.
Review the trust model →Two ways to begin