Frank A.

One challenge I've been thinking about is how to measure whether an AI agent is genuinely reliable.

It's easy to know when a traditional application works correctly because the expected output is usually well defined. AI agents are different they reason, call tools, make decisions, and sometimes produce different outputs for the same task.

Before deploying an agent, how do you decide it's ready for real users?

Do you use benchmark tasks, automated evaluations, human reviewers, production metrics, or something entirely different?

Went through today's launches properly this morning. Generate this, automate that, no code needed. Every page is a capability list. Not one of them tells you what the product does on the day it gets something wrong.

That's backwards for anything a person puts their name on. The failure that actually costs you isn't the crash, because a crash announces itself. It's the confident wrong output that ships looking fine. A video that publishes a false claim to someone's channel. A generated model that's subtly the wrong shape. An agent filling in its own verification field.

Hey everyone - I am Frank, the solo founder behind CompliBase.

I built this because I kept seeing how easily compliance documents get lost across multi-location businesses. Permits expire without warning. Certificates go missing. And when an auditor shows up, nobody has everything ready.

CompliBase gives businesses one place to track every document, get AI compliance assessments for each location, and generate a full audit pack PDF in one click.

Happy to answer any questions about the product, the problem we are solving, or what it was like building this as a solo founder.