Engineering truth in the era of generative AI answers
We started AmpliRank because traditional SEO tools cannot see how AI answers are synthesized, and early AI monitors relied on fabricated scores and hype.
Why We Built AmpliRank
Over the past two years, the B2B software buyer journey underwent its biggest shift since the launch of Google search. Executives, engineers, and purchasing directors no longer click through ten blue links. Instead, they ask AI models (ChatGPT, Claude, Perplexity, and Google AI) to compare platforms, evaluate trade-offs, and recommend solutions.
When content teams opened existing rank trackers, they found nothing useful. Traditional tools tracked keyword positions on search engines, but had zero visibility into whether Claude recommended their product or what web pages Perplexity cited.
Worse, emerging AI tools began peddling black-box magic numbers: arbitrary scores, fabricated financial calculators, and simulated scraping with zero statistical validity.
Our Core Beliefs
1. Evidence Over Estimation
If an AI model recommends your competitor, there is a reason: it was grounded in specific web citations. We extract and categorize those exact sources so you know what evidence to build.
2. Statistical Honesty
AI generation is inherently probabilistic. We calculate Wilson score 95% confidence intervals on your mention rates based on sample size, rejecting overconfident single-point vanity scores.
3. Closed-Loop Action
Visibility data is useless without execution. We translate gaps into prioritized, assignable content actions and enable follow-up sampling to verify if your new publications moved the needle.
Join the forward-thinking content teams
Start measuring your presence across leading AI answer engines today.