u/TransportationOne437

One thing I keep seeing in early-stage startups is that many decisions are still made with a weird mix of intuition, small sample feedback, and hope.

Pricing changes, landing page copy, onboarding flows, positioning, new features.

Most teams either ask a few users, run a slow A/B test, ship and wait, or ask a generic AI model for feedback.

None of these are ideal when you’re still early.

You often don’t have enough traffic for proper experiments. User interviews are useful, but slow. Surveys can be shallow. And a single LLM answer is usually too generic to be trusted for a specific market or audience.

So I’m building Polyhyle.

The idea is to let founders simulate user behavior before shipping product decisions.

You define the context, the audience, the assumptions, and the scenario. Then different synthetic users react based on their profile, needs, constraints, and behavior.

Use cases I’m focusing on:

• testing pricing changes
• comparing landing page copy
• stress-testing positioning
• validating onboarding flows
• testing product ideas before building
• understanding how different segments might react

To be clear, this is not meant to replace real users.

The goal is to catch weak assumptions before spending weeks on interviews, ads, A/B tests, or building in the wrong direction.

I’m opening early access for free to a small group of founders and builders in exchange for feedback.

One thing I keep seeing in early-stage startups is that pricing, copy, onboarding, and feature decisions are often made with very little data.

You can talk to users, run interviews, look at analytics, or run A/B tests, but early on you often don’t have enough traffic or enough signal.

I’m building Polyhyle to explore a different workflow:

Define a scenario, simulate how different user segments might react, then use that to decide what is actually worth testing with real users.

Not trying to replace real validation. More like a way to surface objections, compare segments, and avoid wasting time on obviously weak directions.

Would this be useful before making product or pricing decisions, or would you not trust behavioral simulation at all?

One thing I keep seeing in early-stage startups is that pricing, copy, onboarding, and feature decisions are often made with very little data.

You can talk to users, run interviews, look at analytics, or run A/B tests, but early on you often don’t have enough traffic or enough signal.

I’m building Polyhyle to explore a different workflow:

Define a scenario, simulate how different user segments might react, then use that to decide what is actually worth testing with real users.

Not trying to replace real validation. More like a way to surface objections, compare segments, and avoid wasting time on obviously weak directions.

Would this be useful before making product or pricing decisions, or would you not trust behavioral simulation at all?

One thing I keep seeing in early-stage startups is that pricing, copy, onboarding, and feature decisions are often made with very little data.

You can talk to users, run interviews, look at analytics, or run A/B tests, but early on you often don’t have enough traffic or enough signal.

I’m building Polyhyle to explore a different workflow:

Define a scenario, simulate how different user segments might react, then use that to decide what is actually worth testing with real users.

Not trying to replace real validation. More like a way to surface objections, compare segments, and avoid wasting time on obviously weak directions.

Would this be useful before making product or pricing decisions, or would you not trust behavioral simulation at all?

One thing I keep seeing in early-stage startups is that pricing, copy, onboarding, and feature decisions are often made with very little data.

You can talk to users, run interviews, look at analytics, or run A/B tests, but early on you often don’t have enough traffic or enough signal.

I’m building Polyhyle to explore a different workflow:

Define a scenario, simulate how different user segments might react, then use that to decide what is actually worth testing with real users.

Not trying to replace real validation. More like a way to surface objections, compare segments, and avoid wasting time on obviously weak directions.

Would this be useful before making product or pricing decisions, or would you not trust behavioral simulation at all?

I’m trying to understand how founders handle decision-making before they have enough traffic or users for proper experiments.

For example:

• changing pricing
• changing landing page copy
• launching a feature
• changing onboarding
• testing a new ICP
• repositioning the product

At that stage, A/B tests are often useless, interviews are slow, and analytics are too sparse.

I’m exploring whether simulated behavioral segments could help founders generate better hypotheses before doing real-world validation.

Not as a replacement for users, but as a way to ask:

“What are the likely objections?”
“Which segment would react badly?”
“What should I test first?”
“What decision has the highest risk?”

Curious how others think about this.

Would this be useful, or would you not trust simulation for this kind of decision?

I’m trying to understand how founders handle decision-making before they have enough traffic or users for proper experiments.

For example:

• changing pricing
• changing landing page copy
• launching a feature
• changing onboarding
• testing a new ICP
• repositioning the product

At that stage, A/B tests are often useless, interviews are slow, and analytics are too sparse.

I’m exploring whether simulated behavioral segments could help founders generate better hypotheses before doing real-world validation.

Not as a replacement for users, but as a way to ask:

“What are the likely objections?”
“Which segment would react badly?”
“What should I test first?”
“What decision has the highest risk?”

Curious how others think about this.

Would this be useful, or would you not trust simulation for this kind of decision?

I’m exploring an idea at the intersection of AI agents, behavioral simulation, and startup decision-making.

The product is called Polyhylo.

The premise: instead of asking a single AI model “is this idea good?”, you define a market context and simulate reactions from different synthetic behavioral profiles.

For example:

• price-sensitive users
• skeptical buyers
• early adopters
• enterprise decision-makers
• churn-risk users
• power users
• casual users

Then you can run scenarios like:

• pricing changes
• landing page copy changes
• feature launches
• onboarding changes
• competitor moves
• market shocks
• A/B tests before going live

The output is not meant to be “truth”.

It’s meant to be a decision-support layer: surface objections, compare likely reactions, generate hypotheses, and help teams decide what to test with real users.

I’m curious how people here think about this.

Where do you see the line between useful behavioral simulation and fake confidence?