I built a verification system for my own AI product. It caught me over-crediting my business.🐝
Weeks ago, I shipped a major update to MoltyBeeAI™, an AI-powered business audit system.
The goal was simple:
Make the audit more useful, more actionable, and harder for the system — or the customer — to fool.
Then I did something I probably should have done earlier:
I ran the system on my own business.
It caught me.
What changed
One of the biggest changes was rebuilding the scoring system.
Instead of allowing an AI model to simply judge a business based on what it was told, I wanted the score to increasingly depend on things that could actually be checked.
So the AI Leverage Score™ became a formal rubric:
24 anchored criteria → 4 dimensions → versioned scoring → evidence-backed criteria.
That sounds simple.
It wasn't.
Then we built the verification layer.
The audit can now examine things like:
•pricing pages
•case studies
•repositories
•checkout pages
•FAQs
•analytics-related evidence
•other publicly accessible business information
The system doesn't simply ask:
"Does this business claim X?"
It increasingly asks
"Can we verify X?
That distinction turned out to be much more important than I expected.
And then the funny part happened.
I ran the audit on my own company.
I had effectively over-credited myself in several areas.
I had a homepage with multiple claims, pricing information, FAQs, and metrics.
So I thought:
"Great. The system should be able to verify all of this."
It couldn't.
One URL couldn't substantiate four separate claims.
So the system flagged my own evidence and lowered my score.
😂
And honestly?
I loved it.
Because that was the moment I knew the verification mechanism was doing something useful.
It wasn't protecting the author.
It was evaluating the evidence.
And that's a product you can trust. Verdicts, not vibes.™
That changed how I think about AI products.
One of the biggest problems with AI systems is that they're extremely good at working with information that sounds plausible.
But businesses don't operate on plausibility.
They operate on:
•Evidence.
•Data.
•Constraints.
•Actions
•Results.
That's why we're increasingly designing 🐝MoltyBeeAI™ around a simple principle:
AI should make the judgment.
The system should make the evidence checkable & verifiable/VERIFIED.
We also shipped several other pieces:
🐝 Shareable Verdict Pages
Every audit can generate a public-facing verdict page and Score Badge.
Instead of keeping an audit buried inside a private PDF, the result can become something a founder can actually share with their team, investors, advisors, or board.
🧰 The Agent Prompt Kit™
When an audit identifies a gap, the system doesn't just say:
"You have a problem."
It can generate a copy-paste implementation prompt based on the business context.
The goal is to move from:
Diagnosis → Action
rather than stopping at diagnosis.
📅 The Progress Protocol
We're also moving toward measuring whether the business actually changed after the audit.
Instead of:
Audit → PDF → Done.
The system can track initiatives across:
Day 3 → Day 14 → Day 30 → 60/90-day checkpoints
The interesting part is that future audits can then check whether the recommended changes were actually implemented.
🏠 Hive Member™
We're also building a persistent account layer where businesses can see:
•their current score, that grows/declines or stays flat as the business does
•verified standing that moves as the business moves or grows
•evidence findings
•identified gaps
•historical audits
•progress over time
So the audit becomes less like a one-time report and more like an evolving business intelligence record. Moving with your business.
Always giving you CLARITY & verdicts on what's working or not.
The part I'm most interested in.
We're experimenting with verification standing.
Essentially:
What percentage of a business's score is actually supported by independently verifiable evidence versus self-reported information?
That could become an interesting metric in its own right.
Because imagine comparing businesses not only by:
"What is your score?"
but also:
"How much of your score can actually be verified?"
That's a very different way of thinking about business intelligence.
One lesson I'd share with all other Hackers.
If you're building an AI product, don't only ask:
"How intelligent can we make the model?"
Also ask:
"How much of the model's judgment can we move into deterministic, checkable & verifiable system infrastructure?"
Things like:
•server-side validation
•deterministic rules
•database constraints
•versioned scoring
•evidence requirements
•reproducible calculations
The more important the decision, the more valuable that layer becomes.
The AI can interpret.
But the system should constrain what the AI is allowed to claim.
And that's probably my biggest takeaway from this release:
Build the integrity layer before you desperately need it.
We found weaknesses in our own system while we're still small enough to fix them.
I'd rather discover that now than after thousands of businesses were depending on our system.
We're continuing to build 🐝MoltyBeeAI™ in public.
I'd genuinely love feedback from other Hackers, Founders, BusinessOperators, and AI builders here:
Where do you think AI systems should draw the line between model judgment and independently verifiable evidence?
And if you've built something too, I'd especially love to hear:
What mechanisms have you found useful for preventing your own system from trusting claims too easily?
Introducing 🐝 MoltyBeeAI™ v3.2 🚀
Live now at www.moltybeeai.com and the Founding Hive Member beta price is rising soon. So lock yours in now.
Let me know what you discovered, I'd love to hear back from you. And all your feedback.
Thank you,
— Sihle Dimaza
Founder & Operator
Building in public.
