Question 1

What is AI Search optimization?

Accepted Answer

AI Search optimization is the work of making an organization easier for search engines and AI answer systems to find, understand, verify, cite, and recommend.

Question 2

What is answer engine optimization?

Accepted Answer

Answer engine optimization focuses on improving how an organization appears in systems that synthesize answers instead of only listing links. These systems include ChatGPT, Claude, Gemini, Copilot, Perplexity, and Google AI Overviews.

Question 3

What is generative engine optimization?

Accepted Answer

Generative engine optimization is another term for improving visibility in generative AI systems. Kumu treats it as part of AI Search optimization.

Question 4

What is LLM visibility?

Accepted Answer

LLM visibility is the degree to which large language models can recognize, retrieve, summarize, and accurately describe an organization.

Question 5

Can Kumu guarantee that an AI system will cite a company?

Accepted Answer

No. No agency can guarantee inclusion in a specific AI answer. Kumu improves the evidence, structure, clarity, and third-party signals that make inclusion more likely.

Question 6

What does Kumu test?

Accepted Answer

Kumu tests owned website signals, search visibility, AI-answer behavior, third-party verification, structured data, page-level evidence, crawlability, channel presence, and user action pathways.

Question 7

What is the Kumu Brand & Signal Benchmark?

Accepted Answer

The Kumu Brand & Signal Benchmark is an evidence-led assessment that measures Brand Integrity and Signal Architecture side by side.

Question 8

What is the difference between SEO and AI Search optimization?

Accepted Answer

SEO helps pages appear in search results. AI Search optimization also helps answer systems understand what an organization is, why it is credible, what it offers, and whether it should be cited or recommended.

Question 9

What is llms.txt?

Accepted Answer

llms.txt is an emerging way to give AI systems a concise map of important pages and context. Kumu treats it as an emerging signal, not a pass/fail requirement.

Category	Identified Operational Issue
UX Research	Over-indexed on late-stage validation rather than exploratory discovery.
Navigation	Site structure failed to match actual customer lifecycle tasks.
Competitive Gaps	Lagged behind Verizon and AT&T in plan comparisons and trade-in flows.
Governance	Priorities were set by internal consensus rather than customer data.
VOC Data	Fragmented feedback loops without a unified taxonomy or mapping framework.

T-Mobile: Defining the Digital North Star

Executive Summary

Key Findings & Internal Audit

Audit Breakdown: What We Heard

The Digital North Star Principles

Strategic Execution Roadmap

Independent Brand & Signal Benchmark