Free AI output trust check · no login · no upload

Can users trust what your AI app shows them?

A demo can look magical while the output boundary is still vague. Before launch, prove what the model may use, what it must cite, what humans review, and what the app does when the answer is uncertain.

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Failure mode 1

Confident wrong answer

The app sounds authoritative but cannot point to source, confidence, review, or fallback behavior.

Failure mode 2

Private context leak

A prompt, retrieved document, tenant row, or previous user's context can influence the wrong output.

Failure mode 3

No recovery path

When a user says “this answer is wrong,” support cannot inspect the input, model, sources, or state.

10 proof checks

Mark only what is actually proven.

This is not a legal, medical, financial, or security certification. It is a practical launch check for AI-built apps before real users depend on generated output.

Not every generated answer deserves the same level of trust or review.

If the model is uncertain, the product should not bluff its way through the user flow.

Users and support need evidence, not just a polished paragraph.

AI output is private data when it is built from private prompts, files, rows, or history.

The useful test is not a generic prompt. It is the prompt inside your retrieval, tool, and UI path.

Keep request id, model, version, source ids, and state. Do not log secrets or private user data carelessly.

Users remember the visible final state, not the internal reason a request half-failed.

Trust drops fast when the app hides what is AI-generated or oversells certainty.

When a real user asks “why did it say this?”, you need an answer quickly.

This is the smallest useful proof before letting customers rely on the output.

When output trust is the launch risk

Send one AI output flow. Get a practical next step.

Describe one flow where a generated answer, recommendation, summary, or decision matters. Keep it public and non-sensitive.

Do not send passwords, API keys, private customer data, private files, health/legal/financial data, or anything you do not have permission to share.

If it looks like a fit, the next step is the fixed-scope $99 one-flow preflight. No secrets, private data, private documents, or regulated-sensitive records.

Related check

Run the full launch simulator

Check payment, auth, data, AI output, support recovery, and launch confidence.

Open simulator

Need a second pass?

$99 one-flow preflight

One app, one critical flow, 10 checks, repro notes, and a 2-day action plan.

Book the preflight

Private data too?

Supabase exposure checker

If the output uses private rows, prompts, files, or tenant context, check the data boundary too.

Check Supabase exposure