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BlogEngineeringJune 10, 2026
The Vanta AI Quality Eval Maturity Model<br>Written by
Andy Almonte<br>Sr. Manager, Engineering
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This blog is part of our Trustcraft series, in which we dig into Vanta’s approach to building with AI. Read the first blog in this series to learn more about how we define Trustcraft.<br>You've seen what ChatGPT and Claude can do. You've heard about MCPs, CLIs, and APIs that let you wire a foundation model into just about anything. So when you look at Vanta's AI features, a fair question might come up: Why not just connect a general-purpose LLM to your own company’s data sources and call it a day?<br>It's a reasonable instinct. But after building AI systems that serve thousands of customers across compliance, trust, and security workflows, we've learned something that isn't obvious from the outside: The gap between a raw LLM integration and a production-quality AI product is enormous, and it widens as the stakes go up.<br>This post is about what lives in that gap.<br>The ‘just connect an LLM’ illusion<br>Foundation models are extraordinarily capable. But capability and reliability are not the same thing. In compliance and security, “good” means correctly interpreting control requirements, evaluating evidence accurately, handling regulatory edge cases, and never hallucinating details that could put your audit at risk. A raw LLM integration gives you a coin flip on each of these. Vanta's AI is engineered to get them right, repeatedly, at scale.<br>The difference isn't the model, but everything around the model—the prompts, the context retrieval, the memory, the domain-specific scaffolding, and the system design that turns a general-purpose model into a compliance-ready tool. When you use Vanta's AI, you benefit from deep, focused work put into every one of those layers.<br>When you use Vanta, you don't need to be a prompt engineer, you don't need to design your own retrieval system, and you don't need to stitch together context about your controls, evidence, and frameworks. It just works. When you connect ChatGPT or Claude to an API yourself, you're responsible for building all of that, or accepting worse answers.<br>Meet the framework that holds every Vanta AI feature to the same rigorous quality bar<br>To ensure we’re producing production-quality AI products, we don’t just ship anything. However, as our AI portfolio grew, we noticed a problem: Our AI teams didn't have a shared understanding of what "good enough" looked like for quality and evaluation. Teams were building great features but evaluating them inconsistently—different standards, different tools, different levels of rigor.<br>So we built a rigorous, multi-dimensional quality system we call our AI Quality Eval Maturity Model, which now governs how every AI-powered capability at Vanta is developed, measured, and improved over time.<br>The model evaluates our AI systems across five critical dimensions, each representing the ideal state we hold our teams to:<br>Observability : Full trace coverage across every AI interaction—inputs, outputs, reasoning steps, and metadata—with real-time monitoring and automated alerting when behavior drifts.<br>Curated evaluation datasets : Versioned, actively maintained datasets curated by our GRC subject matter experts. Not ad-hoc test sets, but evolving collections that reflect real-world complexity.<br>Calibrated evaluators : Formal evaluation pipelines, including validated LLM-as-a-judge systems, calibrated against the expertise of our subject matter experts and Vanta's deep trust and compliance domain knowledge. This ensures our quality assessments are consistent and aligned with what actually matters in production, not just what a model thinks is correct.<br>Systematic experimentation : A structured, repeatable experiment-and-analysis cycle for every AI change, with clear criteria for measuring impact and determining next steps.<br>Integrated feedback loops : Explicit and implicit user feedback are automatically captured, tagged, and linked back to evaluation datasets, creating a continuous cycle where real customer experiences drive improvement.<br>How our work has changed with the Eval Maturity Model<br>We score every AI team across each of the five dimensions using a simple rating system: Red (Foundational/Reactive), Yellow (Systematic/Developing), and Green (Advanced/Proactive).<br>When we first ran this assessment, the picture was humbling. Most teams were deep...