Blog

August 2026 · Benchmark

AI Search Visibility Platforms Are Moving From Measurement to Action. Commercial Evidence Hasn't Caught Up Yet.

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

Why AI Speech Is the Most Buyer-Ready Category We've Benchmarked (And What That Reveals)

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

Building GTM Like a Product: Why Buyability Needs a Buyer-Defined Spec

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

Introducing the May 2026 AI SaaS Buyability Benchmark

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

The Buyability Gap: A Hidden Cost of Your AI Product Growth

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

Why a Trust Diagnostic Needs More Than Evals

Three layers of AI-native QA: how the AVS Rubric engineers for evidence integrity, and the verification layer most AI tools skip.

March 2026

A Stable Score Can Still Hide Unstable Evidence

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

Why AI Pricing Requires Three Layers: Architecture, Observability, and Trust

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

What I Learned Vibecoding an AI Startup Tool using Lovable + Claude Code

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

Trust is the new growth constraint in AI

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.