Check your AI feature for trust elements
Mark whether your interface explains how output was produced, confirms before consequential actions, allows editing, offers a human-override option, and shows a confidence indicator.
AI TRUST-COVERAGE SCORE
Trust Layer
Enter which AI-trust elements your interface has and get a trust-coverage score across explanation, confirmation, editability, override, and confidence.
- Uses your inputs in a transparent calculation
- Instant result with practical next steps
- No signup required
Planning guidance only. Validate important decisions with customer evidence and your delivery team.
YOUR INPUTS
Build your result
Complete every field. The result updates only when you choose Calculate.
How it works
We calculate a trust-coverage score
Your score is the share of the five standard AI-trust elements present, out of 100.
Get the specific elements to add
Missing elements are listed by name, with confirmation and human-override prioritized first for any AI action carrying real consequences.
Continue learning: AI tools in the MVP development workflow · Can AI replace a developer for your MVP?
Frequently asked questions
Does Trust Layer add these trust elements to my product automatically?
No. Trust Layer computes a coverage score from the yes/no answers you give about your existing interface — it does not implement any of these elements and does not run a real AI/LLM generation step.
Why does a confirmation step matter for AI features specifically?
AI output can be confidently wrong in ways users don't always catch before an action is taken. A confirmation step before anything consequential (sending, deleting, paying) gives the user one last chance to verify before the AI's output has a real effect.
What counts as a "human-override option"?
Any way for the user to bypass or replace the AI's decision entirely — a manual entry mode, a "write it myself" fallback, or an escalation path to a person, rather than being locked into whatever the AI produced.
Do I need a confidence indicator even for low-stakes AI features?
It matters less for low-stakes suggestions (an autocomplete hint) than for consequential outputs (a generated contract clause, a financial estimate). Weight the missing elements against how much harm a wrong or overconfident output could cause.
How is this different from AIStateKit?
AIStateKit scores whether your interface handles the technical interaction states of an AI response (generating, retrying, failed, etc.). Trust Layer scores a separate concern: whether the interface gives users reason to trust and safely act on the AI's output once it arrives.
How We Compare
| Feature | MVPHub | Figma | UXPressia |
|---|---|---|---|
| AI-trust-coverage score from your own audit | Included | Not included | Not included |
| Named list of missing trust elements | Included | Not included | Not included |
| Design the actual trust-building UI | Not included | Included | Not included |
| Journey mapping for AI feature trust points | Not included | Not included | Included |
Figma is where the actual trust-building UI (confirmations, confidence indicators, override controls) gets designed, and UXPressia can map where trust matters most across a broader user journey. Trust Layer runs earlier — a fast checklist confirming which trust elements are missing before either gets involved.
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