How We Measure Results Without Replacing Evidence with Promises.

The Situation

The product has existing demand and content, but AI comparative answers favor competitors. We record baseline citation share and map it to organic sessions, demo requests, and sales pipeline growth.

Execution Framework

• 01Map a benchmark prompt set across buyer journey stages and establish a pre-launch baseline
• 02Audit priority pages for structured facts: pricing tiers, constraints, implementation criteria
• 03Identify external source mentions that directly influence AI model citations and lead quality
• 04Re-scan periodically against the baseline map and attribute shifts to qualified inbound leads

The Situation

For e-commerce brands, it is critical not to conflate domain rating, organic demand, and AI search visibility. We isolate category-level prompts, source authority, and inbound order quality.

Execution Framework

• 01Isolate category prompt impressions, external citations, and branded vs. non-branded sessions
• 02Evaluate external source quality and mention context instead of vanity backlink counts
• 03Structure comparison matrices, buying guides, and product landing pages with clear entity data
• 04Correlate AI response shifts with Search Console, web analytics, and order volume

The Situation

When defining a new category, a zero baseline doesn't automatically grant top AI recommendations. We first analyze buyer phrasing and identify sources AI engines already consider authoritative.

Execution Framework

• 01Perform a GEO audit documenting current AI responses, terminology, and adjacent solutions
• 02Build category-defining hub pages with verifiable facts, constraints, and evaluation criteria
• 03Curate a list of independent industry publications and expert channels to cultivate off-site
• 04Re-scan prompt sets periodically to measure early signals, retention, and business impact