Why we exist
Because the best time to figure out what "good" means is before you start building.
AI is not like other software
In traditional software, we have tools. We can enforce 80% test coverage. We add QA testers. We write integration tests. We can be reasonably confident that if the tests pass, the system works as expected.
AI systems built on natural language don't work that way.
The input is inherently unpredictable. 100% coverage is mathematically impossible. No matter how carefully you design your system, there's always a chance someone will ask for a pizza recipe when they're supposed to be querying your inventory database.
This is the paradox of natural language processing: the same flexibility that makes it powerful is what makes it impossible to fully control.
It lets us solve problems that conventional programming couldn't touch—or would take years to implement. But we can never be 100% certain every response will be correct.
The question isn't whether your AI will make mistakes. It's whether you'll know when it does.
The infinite fixing loop
Here's a scene I've witnessed more times than I can count:
A customer complains that responses lack detail. The team adds a prompt instruction: "Be more precise." Problem solved, right?
A week later, another customer says responses are too long. The team adds another instruction: "Be more concise."
Now the first customer's issue comes back.
Without defined metrics, teams oscillate forever between competing concerns. Every "fix" creates a new problem. Every sprint becomes about putting out fires instead of building something better.
The solution isn't to guess at the right balance. It's to define what "good" actually means—in measurable terms—before you write a single line of code.
What does "detailed enough" look like? Maybe it means: every suggested product includes its name, price, and category. That's specific. That's testable. That's something you can build towards with confidence.
You only get one chance
Shipping a bad AI experience isn't like shipping a buggy feature. If you fix a button that doesn't work, users will try again.
But if your AI gives someone a wrong answer —they may never trust it again.
Even if you completely rewrite the system. Even if you make it brilliant. That user has already decided: "AI doesn't work for this."
First impressions with AI are often last impressions.
What we're building
We want to make collaboration between stakeholders, managers, and developers efficient enough that business teams can focus on what actually matters—not endless prompt tweaking. And developers can spend their time designing and building solutions, not sitting in meetings about the latest "urgent fix" that was never in the requirements to begin with.
The world is changing fast. Product managers, business analysts, and product owners are now expected to understand AI architecture—often without proper training. That's a lot to ask.
We're here to bridge that gap. To help teams identify what needs to be proven, define how to measure success, and build the shared understanding that turns AI projects from science experiments into production systems.
Because the best time to figure out what "good" means is before you start building.
I've worked on dozens of AI projects. Some succeeded brilliantly. Others failed spectacularly. And after a while, I noticed something: the failures almost never had anything to do with the technology.
The pattern was always the same. Communication.
Everyone has used ChatGPT. Everyone thinks they understand AI. And in some ways, they're right—it's remarkably easy to build something that does something. You can spin up a prototype in one afternoon.
For low-risk applications—storytelling, brainstorming, creative exploration—that's often enough.
But for anything that actually matters? For systems your business depends on? That's where things get complicated.