AI answers now shape category discovery, shortlist formation, and final brand preference. Markiqo helps teams measure and influence that narrative layer.
Every benchmark uses a controlled prompt environment across major AI systems, then converts outputs into decision-ready signals tied to growth actions.
What You Get From This Methodology
What it is: How often your brand appears when the prompt does not name you.
How to read it: Strong means AI discovers you organically. Weak means you rely on branded prompts to be seen.
Why it matters: If discovery is weak, you are missing demand before comparison even begins.
What it is: Where your brand appears when models list options.
How to read it: Early placement drives confidence and clicks. Lower placement gets skimmed or ignored.
Why it matters: Being present is not enough. Placement decides who gets considered first.
What it is: How AI frames your brand when it does mention you.
How to read it: Recommended framing supports conversion. Hedged framing introduces doubt and delays decisions.
Why it matters: This is the difference between being listed and being trusted.
What it is: How stable and verifiable AI claims are about your brand.
How to read it: Higher integrity means fewer risky time-sensitive, future, and pricing claims.
Why it matters: Integrity protects trust and reduces avoidable drop-off from wrong information.
| Common Approach | Markiqo Approach | Why It Matters |
|---|---|---|
| Count mentions as a single KPI | Measure discovery, placement, framing, and reliability together | You see whether visibility is commercially useful, not just present. |
| Treat all mentions as equal | Classify mentions as recommended, mentioned, hedged, or warned | You can diagnose persuasion quality and trust friction directly. |
| Surface generic recommendations | Tie every action to a measured gap with clear execution horizons | Teams can prioritize what to do now versus what to schedule next. |
Markiqo goes beyond positive-versus-negative labels. It tracks persuasion strength, identity alignment, and factual consistency so you can see why AI narratives accelerate or block conversion.
Recommended
Actively endorsed as a strong choice.
Mentioned
Listed neutrally without strong preference.
Hedged
Qualified with caveats or uncertainty.
Warned
Presented negatively or as not recommended.
Because models retrieve and reason differently, one-model analysis is misleading. Markiqo maps cross-model strengths and weaknesses so teams can prioritize with confidence.