Jovan Kis, Co-Founder @ UkisAI (Building TrueStay), recently shared something that made me stop scrolling: "In a week we crossed 6,000+ users, and it all started with a simple idea: AI reads Booking reviews in 30 seconds and helps people choose a hotel faster."

That short statement carries a lot: a clear problem, a crisp promise, a concrete time bound (30 seconds), and a result that signals real pull (6,000+ users in a week). Jovan also mentioned that Jutarnji list (Croatia) reached out to talk about how TrueStay started, why they began from Belgrade, what it looks like to build an AI product people actually use, and what drove such fast growth.

I want to expand on what Jovan is pointing at, because it is bigger than one product launch. It is a blueprint for building useful AI in a trust-heavy category like travel, and for communicating that usefulness in a way that travels fast.

The real insight: speed is not the feature, trust is

Travel decisions are emotional and high stakes. A hotel is not just a transaction; it is sleep quality, safety, location stress, noise, cleanliness, and whether your trip feels smooth or chaotic. That is why we all end up doing the same thing: opening dozens of reviews and hunting for patterns.

The problem is that "read more reviews" does not scale. Booking platforms are built for browsing, but not for synthesis. Humans are good at intuition, but terrible at processing 200 scattered anecdotes under time pressure.

TrueStay's promise, as Jovan described it, is not "AI summaries." It is: give me confidence faster.

Key insight: In travel, the product is not the summary. The product is the decision clarity the summary creates.

Why review reading is broken (even for careful travelers)

Most travelers are trying to answer a small set of questions, but the content they must parse is huge. Typical questions include:

Is it clean, consistently?
Is it quiet at night?
Is the neighborhood safe and convenient?
Does the room look like the photos?
How is staff responsiveness when something goes wrong?
Are there recurring "gotchas" (thin walls, poor AC, construction noise)?

Reviews contain the answers, but they are noisy:

Recency bias: One bad week can dominate the latest comments.
Segment mismatch: A family of four and a solo business traveler value different things.
Extremes dominate: People who had very good or very bad experiences write more.
Language and cultural differences: "Small room" means different things to different people.

So the real job is pattern recognition. When Jovan says "AI in 30 seconds reads Booking reviews," the valuable part is not the reading. It is the clustering, weighting, and translating of messy human feedback into a decision-ready view.

What a 30-second AI review reader actually needs to do

If you want to earn trust quickly, an AI assistant in this space has to do a few things well. Not eventually, not in a roadmap, but right away.

1) Separate signal from anecdotes

A good experience story is still just one story. The AI has to answer: is this a pattern? For example, "noise" matters only if it shows up repeatedly and recently, or if it is tied to specific room types.

2) Preserve nuance instead of flattening it

A generic summary like "Guests like the location" is not decision-grade. The user wants to know: close to what? Quiet or busy? Safe at night? Walkable with luggage? Near public transit?

Key insight: The fastest way to lose user trust is to sound confident while being vague.

3) Let the user bring their own priorities

The best output is not one universal rating. It is a personalized lens: business trip vs family trip, light sleeper vs deep sleeper, car vs no car, accessible needs vs none. Even a simple set of filters or prompts can make the assistant feel surprisingly "human."

4) Show receipts

In trust-driven categories, explanations matter. Even if the final output is short, users feel safer when they can see why the assistant concluded something. That can be done through highlighted themes, sample quotes, or a count of how often an issue appears.

5) Stay within the 30-second promise

Jovan anchored on "30 seconds" for a reason. Time is part of the value proposition. If the experience takes two minutes, users fall back to old habits. Speed turns "maybe I will try it" into "I will use this every time."

What could drive 6,000 users in a week

Jovan did not list a detailed growth playbook in the post, but the ingredients are visible in the story itself. Fast early adoption usually comes from a mix of product clarity and distribution loops. Here are a few plausible mechanics that align with what he shared.

1) A single-sentence value proposition

"AI reads Booking reviews in 30 seconds" is memorable. It is also easy to repeat in a DM, a comment, or a group chat. Many AI products fail here because they describe the technology, not the outcome.

2) A high-frequency use case

People book hotels all the time: weekend trips, business travel, family vacations. A tool that helps with a repeated, annoying decision can grow quickly because every new user becomes a returning user.

"6,000+ users in a week" is not bragging if it is used as proof that the problem is real. The number makes the post credible, and it encourages curious readers to test the product themselves.

4) Shareable output

If TrueStay produces a clean list of pros, cons, and watch-outs, users can share it with a partner or friend to align on a decision. That is a natural organic loop: the output spreads the tool.

5) Press as an accelerator, not the source

Jovan mentioned Jutarnji list calling them. Press rarely creates product-market fit, but it can amplify something that already resonates. When a product hits a real pain point, coverage acts like a multiplier.

"Building from Belgrade" and what that signals

Jovan also highlighted "why we started from Belgrade." That line matters. It signals that meaningful AI products do not need to come from a narrow set of hubs, especially when distribution is global and problems are universal.

But it also carries a more practical lesson: when you build outside the loudest markets, you often compensate with clarity and execution. You cannot rely on hype. You have to win on usefulness. That is exactly the tone of the TrueStay story: simple idea, real users, and visible traction.

What founders and marketers can take from Jovan's post

The post itself is a mini case study in content strategy, not just product growth. If you want to write LinkedIn content that earns attention and drives action, there are a few patterns worth copying.

1) Lead with a concrete result

"6,000+ users in a week" is a hook, but it is also a promise that the rest will be practical.

2) Immediately explain the "why" in plain language

Jovan tied traction to a single idea. Not a broad mission statement. A specific job-to-be-done.

3) Add credible third-party validation

The Jutarnji list mention is not name dropping; it is confirmation that the story has momentum beyond LinkedIn.

4) Invite the next step

He ended with a clear call to action: read the interview if you want the story and how TrueStay changes accommodation choice.

Key insight: Viral posts often look effortless, but they usually follow a structure: result -> simple idea -> proof -> invitation.

Closing: the real opportunity in AI for travel

What I like most about Jovan Kis's framing is that it avoids the usual AI trap. It is not "look what our model can do." It is "here is a painful decision and a faster path to confidence."

If TrueStay keeps nailing that promise, the upside is obvious: travel is massive, reviews are overwhelming, and trust is hard to earn. An AI that helps people decide faster, while staying honest about nuance, can become a habit, not a novelty.

This blog post expands on a viral LinkedIn post by Jovan Kis, Co-Founder @ UkisAI (Building TrueStay). View the original LinkedIn post ->