This page shares some of the thinking and tools behind how I help early-stage AI companies land their first enterprise customers: discovery templates, ROI models, close plans, and account planning tools I've built, plus a few resources I trust. Remix freely.
📋 Table of Contents
🗞️ Published August 11, 2026
For all the power of frontier AI models, and their increasingly adept open-source counterparts, they still struggle with a fundamental lack of consistency, owing in large part to their probabilistic architecture. Much like biological systems, these models are “grown” rather than “built” making it hard to understand why a model capable of crushing the LSAT insists the country of Austria ends with the letter B.
For low-risk use cases where humans are closely in the loop – say, reviewing the draft of an email – these errors are generally no big deal. As we move up the adoption curve into higher-stakes workflows with less human review, however, the equation flips: a once-in-a-hundred hallucination becomes a real liability. Think of an agent issuing an erroneous refund, citing a hallucinated legal case, referring the wrong patient to a clinical trial, or serving as a vehicle for data exfiltration – errors that can end a customer relationship or earn a company a day in court.
Hence the large and growing ecosystem of tools built to “wrangle” these models into consistency: wiring telemetry into agents and observing how they behave, running large-scale evals across their outputs, and/or enlisting other LLMs as judges. All of which sits atop more research-driven, less immediately productizable work to peer inside the “mind” of these models, through techniques like sparse autoencoders (SAEs) and mechanistic interpretability (hat tip, Neel Nanda).
So how do these AI reliability platforms fare? How close can we get AI systems to behavior consistent and reliable enough for meaningful enterprise - and scientific - use cases? And, from a go-to-market perspective, how does a startup in this space capture enough value to sustain an engineering team and build enterprise-specific features?
In this post, I’ll walk you through my perspective on the (broadly defined) AI reliability space, as developed through in-person interviews and secondary research. We’ll cover:
I cut my teeth in enterprise design software, first on the GTM team at InVision, later as an Enterprise AE at digital collaboration platform Mural, carrying $1.3M+ annual quotas and watching our startup grow from 150 to 1,000 employees. I watched this product category take hold and grow, and I'll draw on those lessons throughout this post. I look forward to your comments, edits, and pushback, especially from sellers and operators closer to today's sales motions. Let's learn together and make AI safer and more reliable.
Building an agentic workflow has never been easier. Open a Claude Cowork session, ask it to build, say, a database of news articles on recent renewable-energy policy, and it will spin up agents that get to work. Fancy a more hands-on approach? Visual builders like Lindy, Gumloop, and n8n have you covered, with varying levels of interoperability with the systems you already work with.