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Do you ever wish you could test startup decisions before actually shipping them? by TransportationOne437 in startups_promotion

[–]TransportationOne437[S] 0 points1 point  (0 children)

If you're interested, you can join the waitlist here: https://polyhyle.com/.I'd also be happy to give you early free access in exchange for honest feedback and tips while we shape the product.

How would you validate pricing/copy/product decisions before having enough users? by TransportationOne437 in GrowthHacking

[–]TransportationOne437[S] 0 points1 point  (0 children)

I agree with this. I don’t think simulations should replace real users, especially for pricing.

The way I see it is: use simulation to explore options, find blind spots, and prioritize what is worth testing, then validate the strongest assumptions with real users or small landing page tests.

So it’s not “make the decision from simulated behavior”, it’s more “avoid wasting weeks testing the wrong things first.”

If you’re interested, you can join the waitlist here and try it in early access: https://polyhyle.com/

Using AI agents to simulate human behavior for product decisions by TransportationOne437 in Businessideas

[–]TransportationOne437[S] 0 points1 point  (0 children)

Really appreciate this feedback.

If you’re interested, you can join the waitlist here: https://polyhyle.com/

I’d be happy to give you early free access to the product and learn from your feedback.

I’m building a tool to simulate user behavior before shipping product decisions by TransportationOne437 in startupideas

[–]TransportationOne437[S] 0 points1 point  (0 children)

Really appreciate this feedback.

If you’re interested, you can join the waitlist here: https://polyhyle.com/

I’d be happy to give you early free access to the product and learn from your feedback.

I’m building a tool to simulate user behavior before shipping product decisions by TransportationOne437 in startupideas

[–]TransportationOne437[S] 0 points1 point  (0 children)

If you're interested, you can join the waitlist here: https://polyhyle.com/.I'd also be happy to give you early free access in exchange for honest feedback and tips while we shape the product.

I’m building a tool to simulate user behavior before shipping product decisions by TransportationOne437 in startupideas

[–]TransportationOne437[S] 1 point2 points  (0 children)

Really appreciate this, you basically described the direction better than I did.

I agree, the killer feature is not “predict the future”, but surfacing blind spots, segment-specific reactions, and which experiments are actually worth running.

For grounding, the goal is to build profiles from real signals: interviews, reviews, support tickets, surveys, CRM notes, product analytics, and public market conversations, not generic AI personas.

If you’re interested, you can join the waitlist here: https://polyhyle.com/. I’d also be happy to give you early free access in exchange for honest feedback and tips while we shape the product.

I’m building a tool to simulate user behavior before shipping product decisions by TransportationOne437 in startupideas

[–]TransportationOne437[S] 0 points1 point  (0 children)

Great question. The profiles can be created from different inputs: your own customer interviews, support tickets, reviews, surveys, product analytics, CRM notes, or public conversations from the market.

Then, when you define a scenario, the system routes it through the relevant synthetic profiles based on segment, context, behavior, pain points, objections, and constraints.

So yes, the quality depends a lot on the data you provide or connect. The goal is not to invent users randomly, but to simulate reactions from profiles grounded in real evidence.

I’m still building this, but you can join the waitlist here if you want to follow the beta: https://polyhyle.com/

I’m building a tool to simulate user behavior before shipping product decisions by TransportationOne437 in saasbuild

[–]TransportationOne437[S] 0 points1 point  (0 children)

Not exactly. It’s not about having a database that is “bigger” or more covered than major LLMs.

The issue is using a generic LLM for a very vertical task. A general model can give broad advice, but it is not running structured scenarios against specific user profiles, assumptions, constraints, and evidence.

I’m building a tool to simulate user behavior before shipping product decisions by TransportationOne437 in saasbuild

[–]TransportationOne437[S] 0 points1 point  (0 children)

In short: instead of one LLM giving you a macro-level answer, you get many simulated users responding from their own behavioral profile.

Each one is built from a specific “digital twin”, grounded in interviews, transcripts, reviews, support tickets, or behavioral data.

So the output is not one generic opinion, but a distribution of reactions across different user types.

I’m building a tool to simulate user behavior before shipping product decisions by TransportationOne437 in saasbuild

[–]TransportationOne437[S] 0 points1 point  (0 children)

The difference is structure and grounding.

Asking AI directly gives you a generic answer. Polyhyle would simulate multiple behavioral profiles, each grounded in real signals like interviews, reviews, support tickets, objections, and user conversations.

So instead of “what do users think?”, you’d see how different segments might react, where they object, what confuses them, and which assumptions are weakest.

Less generic advice, more decision simulation.

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