October 2026
The biggest AEO visibility gap isn’t always the highest-priority fix. This practical framework shows how to connect AI-search gaps to ICP-fit buyer decisions, choose what to test, and learn from answer-engine and buyer outcomes separately.
September 2026 · AEO Series, Part 4: PERFORM
AI visibility, recommendation, evidence quality, referral and revenue are not interchangeable metrics. PERFORM connects these signals across the AI-mediated buying journey so GTM teams can locate where influence advances or stalls—and decide what to improve, continue, stop or test next.
September 2026 · AEO Series, Part 3: PROVE
The same brand won all 36 VAT-compliance comparisons. Yet some answer engines turned credible product facts into buyer-workload conclusions the evidence did not establish. Part 3 introduces PROVE—and shows AEO and GTM teams how to audit the evidence behind an AI recommendation.
The recommendation stayed stable. The evidence story did not.
August 2026 · AEO Series, Part 2: PREFER
In Part 2 of ValueTempo's AEO series, we examine what happens after credible brands make the shortlist. A repeated VAT-compliance experiment across ChatGPT, Claude, Gemini, and Perplexity shows how buyer context and public evidence shaped which brand became the #1 choice. One credible brand won the #1 spot 10 of 12 times; an equally credible alternative won zero.
August 2026 · AEO Series, Part 1: APPEAR
AI recommendations can look stable while the buying logic underneath keeps moving. Across 12 fresh-session runs on four AI answer engines (ChatGPT, Claude, Gemini, and Perplexity), the same brand made the final three 11 times and was selected as the #1 choice in 10. What that reveals about APPEAR, the first AI answer-engine decision state: whether your brand reliably enters the right consideration set for the right buying context.
August 2026 · Benchmark
AI search visibility platforms are moving beyond measurement into recommendation and action. But across 12 companies, 10 still meter primarily on observation-based units, while recommendation quality remains difficult to evaluate from public evidence. The August benchmark shows where the category is heading, and what GTM teams should watch next.
July 2026 · Benchmark
We scored 12 AI speech companies on what buyers can independently verify before they engage sales. Every company scored Exemplary — a first in the benchmark. Here's why that happened, and why the gaps that remain are more instructive than the ones that don't.
June 2026
Faster GTM workflows do not automatically create better GTM systems. Why buyability is a GTM system problem, and how a buyer-defined spec closes it.
June 2026
We scored 60 AI B2B SaaS companies across 5 categories and 8 evidence dimensions. The market is compressed in the middle, value unit precision is a major separator, and challengers often publish stronger buyer evidence than incumbents. The full benchmark is live — download the report.
May 2026 · Benchmark
We analyzed 60 AI products across 8 buyer-confidence dimensions. The market is splitting into two camps: gated and buyable. Here is what the data shows.
April 2026
Three layers of AI-native QA: how the AVS Rubric engineers for evidence integrity, and the verification layer most AI tools skip.
March 2026
What hardening AVS Rubric across Beautiful.ai, Hex.tech, and ZoomInfo taught me about evidence integrity, trust infrastructure, and building an AI-native diagnostic founders can trust.
March 2026
A Clay case study - AI pricing works when buyers can predict value, usage, and cost before committing. This article analyzes Clay's new pricing architecture using the AVS Trust Rubric and shows why Clay's pricing architecture improved, but AVS Trust score dropped from 81% to 75%.
March 2026
A build-in-public note on what broke, what worked, and what vibecoding an AI product taught me about reliability, production readiness, and trust infrastructure.
February 2026
A practical way to make value, usage, and cost feel predictable — why pricing drift becomes trust drift, and how AVS gives operators a shared map.