Catch what AI gets wrong about you — before a buyer believes it
Retired pricing. A feature you never shipped. A mix-up with a similarly named brand. AI states it with total confidence, and buyers act on it. Brand Climb finds these errors across engines and traces where each one comes from.
The screen this runs on
AI Visibility
How often the brand appears when a buyer asks an engine for a recommendation.
By AI platform
Four engines on every plan. Pro reads a fifth.
Sentiment
Tone of the answers that name you
Who the engines recommend
Share of tracked answers, this period
| Brand | Visibility | Share of voice | Sentiment | Avg. position | Change |
|---|---|---|---|---|---|
| 1Trailforge | 42.4% | 34.1% | Positive | #1.9 | +2.1 |
| 2Northstar BikesYou | 35.1% | 28.5% | Positive | #2.8 | +11.9 |
| 3VeloPeak | 27.8% | 21% | Neutral | #3.4 | -1.3 |
| 4Summit Cycles | 19.6% | 11.2% | Neutral | #4.1 | +0.4 |
| 5RideCore | 14.2% | 5.2% | Mixed | #5.0 | -2.6 |
Sample data — Northstar Bikes is a fictional brand
What Brand Climb catches
- Wrong product details
- Outdated pricing
- Missing or invented features
- Incorrect company facts
- False comparisons
- Wrong locations
- Unsupported reputation claims
- Confusion with a similar brand
- Stale policies or services
How it works
Set the truth
Add the verified facts about your company, products, and policies.
“For performance under $3,000 most riders shortlist the Trailforge Apex and the VeloPeak R2. Both publish full geometry…”
Compare answers
Brand Climb checks live AI responses against your source of truth.
Trace the source
Find the page or stale data feeding each mistake.
Fix it at the root
Get specific moves for your content, schema, and outreach.
The model judges only against facts a human recorded — never its own training knowledge of your brand. With no facts on file we refuse to run rather than let one model guess whether another lied.