What does it take to build one of the most downloaded app bundles on the App Store? At Guru Apps, it’s about quick decisions, lean teams, and a sharp focus on what people actually need.

We recently had a great chat with Olga Breslavska, Chief Product Officer at Guru Apps (Universe Group) — a product leader who crafts strategy, builds at speed, and genuinely enjoys testing bold ideas.

Guru Apps is a bundle of iOS tools — including Cleaner Guru and Translator Guru — designed to solve everyday tasks quickly and simply. In 2025, Cleaner Guru topped the App Store charts in the US, Canada, and Australia, beating out TikTok, Instagram, and even ChatGPT.

In our conversation, we explored building focused product teams, implementing AI without overcomplicating things, and staying relevant in a crowded market.

How to Grow Fast Without Reinventing the Wheel

Start with what your team already knows. That internal expertise — the skills and instincts built over time — often sets successful product teams apart from the rest.

Fast iteration and solid execution usually deliver results. But rather than diving into completely new categories, it often works better to leverage your strengths. If your team excels at building utility apps, there’s probably more value in exploring fresh angles within that space than attempting to break into, say, health & fitness from scratch. This might mean watching niche markets closely, spotting gaps, and testing ideas quickly, sometimes going from sketch to live product in just weeks.

And when deciding what to build next, it’s not just about market size. You must also factor in how the business model works, market growth patterns, and who’s dominating currently. 

In subscription-based apps especially, things can look healthy on the surface thanks to long-tail revenue from older users, even as traffic and new demand start to dip.

Why Static Markets Aren’t Worth the Climb

If no new players enter a market and things feel slow — even with big current players — that’s usually a warning sign. For product teams aiming to grow fast, slow-moving markets often spell trouble.

It’s easier (and smarter) to build in spaces with momentum — where new products can take off and hitting $1M/month in revenue is actually achievable. A market might look big on paper, but if no one’s launching anything new or growing quickly, the opportunity may have long passed.

Take hiking or trekking apps. They generate solid revenue and have loyal users, but they’re seasonal and dominated by a handful of established players. Users in these apps have strong emotional ties — their saved routes, their history, their communities. 

Even if your app is technically better, breaking in means overcoming some powerful user loyalty. You’ll need more than just a great product. It’ll take serious effort (and budget) to convince people to switch.

What Makes a Product Team Actually Deliver

Before launching anything, the team addresses fundamental questions:

• Who are we building this for?
• How will they find us?
• What’s the real value for them?
• What problem are we solving, and why does it matter?
• How do we make money from it?
• How will we run things internally?

Once there’s a clear alignment across the board, they move fast. But even with all that prep, not every idea makes it. Less than 10% of experiments actually turn into real products, though that’s not for lack of trying. 

What helps is doing the homework: studying the market deeply, modeling revenue properly, and securing buy-in across the team. It’s not about throwing something out there and hoping it sticks. It’s about committing to make it work, even if early metrics like CAC or LTV aren’t perfect.

Ownership is a big part of success. The same small team (usually just 4–6 people) that builds the product also sees it through. They wear multiple hats initially and only start growing the team once they see things clicking.

Talent Fuels Everything

Hiring the right people is never easy, but it’s also the secret sauce behind everything that works. The team’s always on the lookout for smart, motivated individuals who want to take on real challenges and grow fast.

Currently, Guru Apps is hiring across a bunch of key roles. They’re especially focused on bringing in product managers, and there’s plenty of internal support to help them level up. On top of that, they’re building their product analytics team and looking to add more iOS developers and QA engineers to the mix. Marketing’s growing, too. Guru Apps are also after creative user acquisition professionals who can bring fresh energy and sharp ideas.

The vibe is simple: if you’re curious, ambitious, and up for solving real problems, they’d love to talk.

AI Doesn’t Replace the Work — It Shifts It

Despite the noise about AI taking over human jobs, it’s mostly changing what those jobs look like. If anything, AI is creating more to build, not less. From writing messages and emails to speeding up testing, it’s already integrated into everyday workflows.

Teams use AI to move faster, especially when managing multiple products. It’s also great for research, helping generate user insights based on historical data, without the time and cost of full-fledged UX studies. In some cases, AI-generated user models match real interview insights remarkably well.

Still, AI can’t replace speed, culture, or talent. But when those things are already in place, it amplifies their impact.

Better Hypotheses, Smarter Workflow (With a Bit of AI Help)

Strong product hypotheses is what really moves the needle — key metrics like win rate, ARPU, and LTV all depend on them. But building those ideas takes time most teams don’t have. That’s where AI steps in.

It helps cut through the busywork: scanning markets, analyzing A/B tests, monitoring competitors — all the tasks that took hours now take minutes. And it’s not just about direct competitors. Sometimes the best ideas emerge from unexpected places — like adapting Duolingo’s cheeky, passive-aggressive tone to rethink user engagement.

Even as some companies are publicly cooling off on AI, behind the scenes it’s reshaping how products are built and researched. It’s not replacing good product thinking — it’s just making it faster and a whole lot sharper. 

AI as a Multiplier, Not a Replacement

The fastest-moving teams today embrace change, test new ideas, and smartly integrate AI into their day-to-day. Whether it’s speeding up coding, testing smarter, or pressure-testing product ideas, AI helps teams work better. Some even use tools like ChatGPT to challenge their own thinking — like a sparring partner that doesn’t just generate stuff but asks, “Are you sure about that?”

Of course, good output requires good input. For AI to deliver real value — like predicting A/B test outcomes — it needs structured, historical product data. Without context, predictions remain shallow. But with the right data, AI can already hit around 60–65% accuracy in test result predictions — more than enough to cut out weak ideas early and save time and money.

The big goal now? Build datasets that enable personalized predictions — not just generic insights, but tailored support for each product. Even as AI gets sharper at generating and ranking ideas, it’s still just a sidekick. Real ownership stays with the product manager.

Engineering the Future: From Prompt Writers to Context Architects

AI isn’t just reshaping workflows; it also makes roles evolve. Industries move beyond hiring prompt engineers to something deeper — context engineering. 

These professionals don’t just write clever prompts; they determine how to feed AI the right information, structure setups, and create workflows that make the most of the model’s capabilities. It’s a mix of systems thinking, research, and a lot of trial and error. Getting decent AI output isn’t that hard. However, what about improving it from 90% to 99%? That requires time, patience, and real domain expertise.

The best context engineers typically have strong theoretical and practical AI or machine learning backgrounds. They understand how to build evaluation datasets, structure inputs with precision, and interpret model behavior. Yet, with all that, critical thinking remains the (pardon for repetition) critical.

Despite catchy headlines in the media, AI isn’t replacing engineers or product teams — it’s just raising the bar. The teams that really thrive understand how algorithms work and can think like the systems they’re building.

Starting Smarter in the AI Age 

The way people break into tech is changing. Junior roles traditionally involved repetitive tasks — simple, mechanical work that helped learn the ropes. But with AI handling the basics, the entry point looks quite different now. Newcomers are expected to tackle more complex challenges right out of the gate. That’s not a bad thing — it’s just a different kind of learning curve.

Today’s 14-year-olds build projects in days that previously took experienced developers weeks to figure out. It’s not because they’re magically smarter, but because they’ve got better tools. GPT, Cursor, visual IDEs — these flatten the learning curve dramatically. You don’t need to memorize every command blindly when you can ask the right question and receive instant feedback. That said, deep understanding still matters. The difference is we’re getting to the good stuff — strategic thinking — a lot sooner.

It’s all part of a bigger shift: as tools become smarter, we must become smarter with them. The real edge now isn’t knowing syntax, but rather framing problems, asking better questions, and building cool things with what we’ve got. If AI eliminates busywork, we can finally focus on thinking that actually moves the needle.

AGI, AI Agents, and the Quiet Transformation

While everyone’s buzzing about Artificial General Intelligence, we might not notice when it actually shows up. It’ll quietly sneak into our tools and workflows, handling specific tasks better than humans and gradually branching into broader areas. Some models already excel at coding or advanced math while struggling with tasks humans find simple, like spatial reasoning. That mix doesn’t mean they’re weak. It just shows where progress is needed.

Still, the real shift is already here. AI is transforming how products are built, used, and shipped — fast. Soon, conversational interaction with apps will feel natural. You’ll just say, “Clean up my photos” or “Run the campaign,” and it’ll happen. AI agents are trained to handle complete workflows — from generating copy to testing and optimizing it. Going beyond just a feature, it’s becoming an entire operational layer behind the scenes.

We already see it in photo and video enhancement. One poor result can lose a user forever, so quality is mandatory. People expect AI not just to help, but to deliver results they can trust, especially with personal content.

None of this makes engineers or product teams obsolete. If anything, it raises the standards. It demands better context, sharper judgment, and real ownership. Entry-level jobs won’t be about following steps anymore; they’ll be about solving real problems from day one.

Ever-advancing tools mean using our intelligence for what matters: asking better questions, building real solutions, and staying one step ahead. After all, teaming up with machines is better than competing with them.

You Have Three Sips of Coffee to Prove Your Product’s Worth

These days, people discover apps while half-watching reels and sipping coffee. And when they hit install, you have seconds to present value. To this end, remember: they’re not exploring features or reading tutorials. They’re asking, “Is this actually useful for me?” If the answer isn’t immediately clear, they bounce. No second chances.

That’s why communication right after install must hit the mark. Grabbing attention is not enough; you must show value instantly. Since people download apps for various reasons, that first message must match their intent. Personalization at this stage is everything.

Of course, with IDFA limitations and attribution gaps, figuring out user intent isn’t straightforward. Without clear data signals, teams have to get creative, using behavior patterns and smart onboarding flows to guide each person to the right experience. It’s too early to talk about boosting retention at this stage, as the main goal is to earn the second tap.

Perplexity: How Models Measure Surprise

Perplexity sounds complicated, but it’s actually a measure of how well a language model predicts what comes next. The lower the number, the better the prediction accuracy. When the text is super generic or follows common patterns, the model nails it. But toss in something quirky or totally unexpected? That’s when perplexity spikes — the model gets “confused.”

This metric is particularly interesting when tested on stuff the model hasn’t seen before — like fresh code or brand-new documents. At OpenAI, they’ll test how well a model handles its own source code (which it wasn’t trained on) to see how well it can generalize to similar but unfamiliar material.

In plain terms, perplexity shows how “surprised” the model is by what it’s reading. Less surprise — even with complex, new material — indicates greater intelligence.

The Bottom Line

Scaling product teams isn’t about chasing the next big thing — it’s about executing what already works with precision. Guru Apps demonstrates that fast iteration, focused execution, and strong internal talent outperform trying to do everything. Their approach — small teams, clear ownership, and data-backed decisions — transforms experiments into chart-topping products.

As for AI? It’s not here to take anyone’s job, at least not yet. Right now, it speeds things up, questions old habits, and helps product teams build smarter. Sure, AI is learning fast — but until it can make proper espresso or explain your app’s churn rate coherently, we’re safe.

The real opportunity? Using AI to ask better questions, build better products, and maybe, just maybe, teach it why the ‘skip intro’ button is sacred.