Plavtora
Turn Business Decisions Into Better Outcomes.
After today's post reached #1 on the Build Board, I wanted to show a little more of what we're actually building with Plavtora.
The core of Plavtora is Decision Sphere.
The idea is simple:
You give it a situation, problem, or decision.
Instead of simply asking an AI to “analyze this,” Decision Sphere looks at the problem through different analytical perspectives and turns those perspectives into a structured judgment.
Inputs → perspectives → analysis → judgment → decision
The important distinction for us is that the output isn't supposed to be “here are 1,000 words about your problem.”
It's supposed to help answer:
“Given what we know, what should I actually do?”
We've already built the system and are now putting it in front of people to find out where it genuinely helps and where the concept falls apart.
The landing-page system is currently one of the first applications we're testing with it, but the underlying system isn't limited to landing pages.
I'd rather get this wrong in public than build in isolation.
So if you had a decision you were genuinely struggling with right now, what would you want a system like this to help you decide?
And if you've tried Plavtora already, I'd especially like to know where the recommendation felt useful vs. where it felt like generic AI analysis.
Hey everyone,
I’m building Plavtora, an AI-powered system designed to help businesses make better decisions without relying entirely on guesswork.
The idea is broader than just another AI tool. We’re building different systems that can analyze specific business problems and turn the analysis into actionable insights.
We’re starting with systems around things like user personas and website/landing-page analysis, with more decision systems planned.
The bigger vision is simple:
Understand → Analyze → Decide → Improve.
We’re still early, so I’d genuinely like feedback from other founders and builders:
What business decision do you currently find hardest to make?
That’s the kind of problem we want Plavtora to eventually solve.
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2When a product promises better decisions, the sharpest first wedge is usually one recurring decision with a clear before-and-after—not a broad analysis layer. I’d choose a decision that already costs a small team time every week, show the inputs, recommendation, and what changes after it. That makes the landing-page analysis less abstract and gives you a concrete activation event to measure. Which decision are users asking you to solve first?
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1Not limited to landing pages. Landing-page analysis is just the first system we’ve put into users’ hands while we validate the broader idea. The larger direction for Plavtora is a decision system helping someone take a messy set of inputs, evaluate them from the right perspectives, and arrive at a clearer decision/recommendation. Right now we’re using landing pages as the initial wedge because “what should I change here?” is a concrete decision with an observable before/after. We’re still testing what other recurring decisions are strong enough to become dedicated systems around the same underlying engine. So the question we’re trying to answer now is less “how do we make a better landing-page analyzer?” and more “which decisions are painful and recurring enough that people will actually come back to Plavtora to make them?”
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1That makes sense—the landing-page system is a good proving ground because the decision, evidence, and outcome can be bounded. To discover the next wedge, I’d log each request by frequency, urgency, and whether a user already has the inputs needed to act. The winners will likely be decisions people make weekly and currently take to a blank document or several tools. Once you see a repeatable first use, the broader platform story becomes much easier to earn.
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Share Your Problems so that we can help you .🙌🙌
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1Answering your actual question: the hardest decisions I make are not hard for lack of analysis. Killing a product line with real revenue, or moving a good person out of the wrong seat, are hard because the information is already clear and the cost of acting is personal, which is exactly why an analysis layer does not move them. The decisions AI genuinely helps with are the ones nobody has time to think about carefully, not the ones everyone is avoiding, and that distinction should decide which systems you build next.
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Sounds like an amazing product but how is it different from another LLM like chatgpt or Claude?
2 Comments
The useful-vs-generic distinction seems important, but I’d push one level further: have users actually made or changed a real decision because of a Decision Sphere recommendation, or are you mostly measuring whether the reasoning feels better than a normal AI response?