Hey Show HN! I built Acceptify to solve a problem I ran into while building my other app. I’m the founder of a SaaS tool called Fortress. Early on, I had a classic issue: not enough real users yet, but I still needed feedback on whether features actually worked from a user perspective. QA and unit tests told me if the code worked — not if the experience made sense. So I built an internal tool that creates AI personas and agents to simulate different kinds of users interacting with the product. How it works: 1. You define a user story and acceptance criteria (similar to how product teams already write them) 2. You create AI personas (e.g. technical power user, first-time non-technical user, impatient user, etc.) 3. You give an AI agent a goal like “complete signup” or “create a project” 4. The agent explores the product autonomously, navigating, clicking, and making decisions 5. Acceptify evaluates what passed, what’s uncertain, and where behavior didn’t match expectations It’s essentially an attempt at automated user acceptance testing that focuses on behavior and expectations, not just DOM checks or scripted flows. Some things it surfaces well: • Flows that technically work but are confusing • Missing validation or feedback states • Places where navigation or UX assumptions break • Gaps between acceptance criteria and actual behavior This started as an internal tool for Fortress, but I realized other product teams probably face the same “too few users, too much guesswork” problem, so I turned it into a standalone product. It’s still early, and I’m very interested in feedback from HN folks especially around: • How this compares to existing QA/UAT workflows you use • Where you’d expect this to fail or hallucinate • What would make this genuinely useful vs. just a demo Happy to answer questions and share more technical details about how the agents and evaluation work.