01
Define
Freeze the buyer-intent questions, category rules and configured engines.
Independent AI recommendation benchmarks
See where AI ranks your product.
Back your favourite. See where the evidence leads.
Observed results · Transparent evidence · Never paid rankings
Climb on evidence, not promotion.
RankHigh measures how AI engines actually recommend products — independently of advertising, sponsorships or founder activity.
- Standardised buyer-intent questions
- Configured AI engines
- Inspectable evidence
- Never paid rankings
Launch benchmark
AI Meeting Assistants
The public category experience is ready. The benchmark itself remains unpublished until the evidence and methodology are approved.
Benchmark in preparation
Preparing the first public benchmark
No product ranks, scores or activity figures are public yet. That absence is intentional: RankHigh publishes dated evidence, not presentation-only placeholders.
No TEST data shown
No invented activity
No payment influence
From question to published result
Three stages. Full provenance.
02
Observe
Repeat the same questions and preserve model, timing and response provenance.
03
Review
Validate the evidence, score under the approved methodology, then publish a dated snapshot.
Evidence-first intelligence
We don’t just show a score. We show what produced it.
Every published result can carry the provider, exact model, tested surface, buyer-intent question, sample context, observed recommendation and methodology version.
Explore the evidence standardDated snapshots
No undated rank claims.
Commercial firewall
Payment never changes rank.
Reviewed evidence
Public metadata is deliberately curated.
Versioned methodology
Changes are labelled, not hidden.