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
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.