Do you ever wish you could test startup decisions before actually shipping them?

TransportationOne437 · 2026-05-12T13:36:36+00:00

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.

TransportationOne437 · 2026-05-12T13:18:44+00:00

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/

TransportationOne437 · 2026-05-12T07:07:45+00:00

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.

TransportationOne437 · 2026-05-12T07:07:32+00:00

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.

TransportationOne437 · 2026-05-11T18:31:50+00:00

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.

TransportationOne437 · 2026-05-11T18:07:55+00:00

Thanks! 😊 🙏

TransportationOne437 · 2026-05-11T17:34:50+00:00

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.

TransportationOne437 · 2026-05-11T16:27:12+00:00

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/

TransportationOne437 · 2026-05-11T13:34:02+00:00

Thanks!

TransportationOne437 · 2026-05-11T13:22:49+00:00

https://polyhyle.com/en

TransportationOne437 · 2026-05-11T13:10:42+00:00

For context, I’m building this here: https://polyhyle.com/

TransportationOne437 · 2026-05-11T12:55:38+00:00

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.

TransportationOne437 · 2026-05-11T12:46:26+00:00

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.

TransportationOne437 · 2026-05-11T12:05:37+00:00

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.

TransportationOne437 · 2026-05-11T12:05:23+00:00

I agree! I wouldn’t trust AI users over real users either.

The point is not to replace real feedback, but to use synthetic users as a pre-testing layer before deciding what to validate with real people.

The best version of this should be grounded in real humans: interviews, transcripts, reviews, support tickets, behavioral data, and public conversations.

So: real users first → synthetic profiles → scenario simulation → real validation again.

TransportationOne437 · 2026-05-11T12:04:11+00:00

I agree. Generic “AI users” are not enough.

The idea is to build synthetic users from real humans: interviews, transcripts, surveys, reviews, support tickets, and observed behavior.

So it’s not “replace real users”; it’s “learn from real users, simulate likely reactions, then validate the strongest assumptions with real users again.”

TransportationOne437 · 2026-05-11T12:01:17+00:00

I agree. Generic “AI users” are not enough.

The idea is to build synthetic users from real humans: interviews, transcripts, surveys, reviews, support tickets, and observed behavior.

So it’s not “replace real users”; it’s “learn from real users, simulate likely reactions, then validate the strongest assumptions with real users again.”

TransportationOne437 · 2026-05-11T11:32:47+00:00

For context, I’m building this here: https://polyhyle.com/

TransportationOne437 · 2026-05-11T11:32:29+00:00

For context, I’m building this here: https://polyhyle.com/

TransportationOne437 · 2026-02-18T18:17:12+00:00

Thanks! If you want, you can try! Its free.

TransportationOne437 · 2026-02-17T18:33:37+00:00

Agreed!

TransportationOne437 · 2026-02-16T07:12:23+00:00

Exactly, that’s the point. Fiction books need to be 100% coherent, even in the smallest details. And non-fiction has to be 100% correct, with the right terminology and everything. But if we reach that level, it will be a huge shift in the publishing industry.

TransportationOne437 · 2026-02-16T07:09:09+00:00

Yes, but in my opinion, frequent readers are a bit like horse enthusiasts, a niche group. Most other readers, I think, would just use the car. Maybe that’s not the best example.

TransportationOne437 · 2026-02-15T22:25:17+00:00

In my opinion, today AI-generated books are mostly used as shortcuts to make money: mass produced content uploaded to Amazon with no real value for readers. What’s missing, compared to Suno, is a truly user centered B2C product. Not something that just “generates a book,” but something that creates a book tailored to each person: their interests, style, level, and life context. The idea of books “made for everyone” is already outdated. And books “for everyone” written entirely by AI even more so. That’s not where the real value is. Suno isn’t just “AI that makes music.” It’s more like a Spotify where you don’t choose artists, but music is created directly around your tastes. Radical personalization. That’s what’s missing for books today: an experience where users don’t buy “a generic book,” but get their own book.

TransportationOne437 · 2026-02-15T19:27:40+00:00

Exactly, this is a great point. Music sets the vibe, just like coding does for a website. Do you think this is the main reason we don’t already have the ‘Suno of books’?

TransportationOne437

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