Score candidate patterns against your inputs
For your decision type, 2-3 candidate patterns (e.g. tabs vs. sidebar nav, table vs. cards, single-page vs. multi-step form) are each scored against your item count and frequency.
Free UX pattern-fit tool
PatternFit AI
Answer a few structural questions about your requirement and get scored recommendations between common UX pattern options, with explicit tradeoffs — computed from your own answers, not an AI model.
- Calculated instantly from your inputs
- No signup or data submission
- A focused next step for MVP planning
Your entries remain in this browser session and are not sent to MVPHub.
Your inputs
Describe your requirement
Answer these questions about the specific UX decision you're making.
Your calculated result
Your result
0/100
Planning score
Recommended next actions
How it works
Weight by scale and repeat use
Higher item/step counts favor more structured patterns (sidebar over tabs, multi-step over single-page); frequent repeat use favors patterns optimized for speed over ones optimized for first-time clarity.
Explain the tradeoff, not just the winner
The recommendation includes what you give up by choosing it, so the decision is transparent rather than a black-box verdict.
Frequently asked questions
Is this a real AI recommendation engine?
No — this is a structured planning tool. It scores fixed candidate patterns against your entered item count and frequency using a documented method; no AI model evaluates your specific product.
What if my item count is right on the boundary?
Near a boundary, either pattern is a reasonable choice — treat the recommendation as a starting point and weigh your own product's specific needs, not an absolute rule.
Does frequency really change the right pattern?
Yes — a pattern that's clearer for a one-time setup task can feel slow for something done daily; the tool explicitly weighs repeat-use speed once frequency is "frequently."
How is this different from PatternScout?
PatternFit AI compares 2-3 specific candidate patterns for one already-identified decision; PatternScout matches a broader, less-defined problem description against a wider pattern library first.
How we compare
| Capability | MVPHub | Picking a pattern based on personal preference | A general-purpose AI assistant |
|---|---|---|---|
| Scores patterns against your actual scale/frequency | ✓ | × | — |
| Explains the tradeoff, not just a winner | ✓ | × | — |
| Consistent, repeatable scoring method | ✓ | × | × |
| Instant, no design engagement required | ✓ | ✓ | ✓ |
MVPHub scores your specific scale and frequency against documented pattern tradeoffs. Personal preference varies by designer; a general AI assistant lacks a fixed, repeatable comparison method.