TEMPO PULSE
AI Visibility
Understand the story AI tells about you.
See what AI systems say, how stable that answer is, and what shapes it.
Tempo Pulse AI Visibility studies how consumer AI systems describe a subject — including organizations, companies, institutions, public projects, issues, products, campaigns and people — and analyzes the themes, facts and narratives they emphasize, how stable those answers are, and how they move under skeptical, critical, open-ended and false-claim pressure.
Our methodology goes beyond isolated screenshots. We measure stability, prompt sensitivity, narrative resilience, factual risk, source influence, source shift and cross-platform differences to give you a clearer, more reliable view of the AI answer environment.
“A clearer view of what AI is
telling people — and how
dependable that picture is.”
KEY BENEFITS
Measure the AI answer environment — not just isolated responses.
AI Visibility helps you understand what AI says, how stable that answer is, and what can change it.
Understand the story AI tells about you.
See the facts, themes, attributes and associations that consistently define your AI representation.
Know whether that story is stable.
Test whether ordinary wording changes produce the same basic understanding — or a different one.
Find distortion beyond simple factual errors.
Identify how emphasis, omission, qualification, context and association can change meaning.
See how the narrative behaves under pressure.
Measure what remains stable — and what moves — under skeptical, critical, adversarial and false-claim prompting.
Know what is changing before it becomes obvious.
Track shifts in sources, prominence, factual treatment and cross-platform behavior over time.
EXAMPLE INSIGHTS FROM ACTUAL REPORTS
A richer, more reliable view of the AI answer environment.
AI Visibility reports combine stable baseline analysis, narrative stress testing, source review and cross-platform comparison so you can see not only what AI says, but how and why it changes.
Stable baseline helps separate fact from framing.
Neutral question sets show what remains consistent before directional pressure is applied.
False-claim testing reveals resilience differences.
Repeated false-premise variants show whether AI rejects, qualifies or begins to normalize false information.
Source differences can change the user experience.
Platforms may share a factual core while relying on different source neighborhoods and creating different impressions.
ADDITIONAL CAPABILITIES
A deeper view when you need it.
AI Visibility can be customized for your research question, with additional analysis to explore the answer environment in more detail.
Baseline Stability & Prompt Sensitivity
Measure whether neutral paraphrases produce a reproducible representation.
Narrative Stress Testing
See how the answer changes under skeptical, critical, positive and adversarial framing.
False-Claim Resistance
Test whether verified false propositions are rejected, qualified or partially normalized.
Source Influence & Source Shift
Track which source types recur, and which enter or leave as framing changes.
Cross-Engine Comparison
Compare how different AI systems describe the same subject.
Monitoring & Risk Register
Turn one-time findings into a framework for repeated monitoring over time.
A clear view of the AI answer environment
helps you make better decisions.
Tell us what you need to understand.