What free resources does PilotProof offer?

PilotProof offers an eight-dimension Product and AI Readiness Assessment, an AI MVP decision tool, and decision-oriented guides for individuals, teams, and companies.

What is the Product and AI Readiness Assessment?

The assessment reviews problem evidence, workflow, AI role, data, rights, error impact, human oversight, and operational ownership. It recommends discovery, a bounded pilot, or a build-ready scope without inventing a score, price, or timeline.

What topics do the PilotProof guides cover?

The PilotProof guides cover four key topics: (1) AI Audit — how to evaluate your current systems for AI readiness; (2) AI MVP — a step-by-step guide to building your first AI product; (3) Agentic Enterprise — how to implement autonomous AI agents in your organization; (4) AI Marketing — using AI to optimize and scale marketing operations.

Are PilotProof resources available in German?

Yes. All PilotProof guides and resources are available in both English and German, designed to serve founders and enterprises across the DACH region (Germany, Austria, Switzerland).

Product & AI Decision Tools

Working tools and evidence-based guides for a responsible product decision.
Start with the user problem, then decide where AI actually helps.

Product & AI Readiness Assessment

Eight practical decisions turn an idea into a responsible next step—whether you are working alone, in a team, or in a company.

8 dimensionsNo invented scoreImmediate action plan

1. What evidence shows the problem matters?

Evidence keeps the first build tied to a real need.

2. How clear is the current user workflow?

Name where the product enters the user’s real day.

3. What should AI do in that workflow?

Separate assistance, decision, and execution.

4. Are representative data examples available?

A demo sample is not the same as operating data.

5. Are data rights and privacy boundaries clear?

Include personal, confidential, copyrighted, and third-party data.

6. What happens when the output is wrong?

Average quality must not hide rare, consequential failures.

7. Who reviews or approves the AI output?

Oversight must match the AI role and error impact.

8. Who owns quality after launch?

AI quality, cost, drift, incidents, and user feedback need ownership.

Method basis · reviewed 5 September 2026. This is structured decision support, not a legal opinion, conformity assessment, or promise of cost or delivery time. The questions reflect current primary guidance on risk, data, oversight, evaluation, transparency, and operational responsibility.