How We Built an AI Support Teammate in Under 30 Hours

AI isn’t the future, it’s the infrastructure of modern companies. Cleevio embraced that early, embedding AI agents into how it works at every level. That transformation led to the creation of Cleevio AI Automations, a new company built to help others unlock the same potential.

Cleevio AI Automations
To take things even further, Cleevio has spun off a new company: Cleevio AI Automations, led by David Zadražil, former CTO of Cleevio. His mission? Help other companies make the same leap. From isolated AI experiments to real-world, practical usage that actually makes teams faster and smarter.

Using what we build. Selling what we actually use.
The AI agents in Cleevio aren’t an experiment in a sandbox. They’re fully embedded in daily work: financial reports, project risk identification and daily stakeholders reports, projects estimations and boring administrative tasks. Every product team now works alongside AI counterparts. No deception, just integrated tech that augments human decision-making.

“Our belief is simple: AI should empower people, not replace them,” says Zadražil. “We’re not preaching some abstract future, we’ve implemented it ourselves. We don’t sell something we wouldn’t use. It’s about freeing people from routine so they can focus on high-impact work.”

Proof before pitch
In the era of overhyped AI promises, Cleevio’s approach is grounded in transparency and practice. The new company, Cleevio AI Automations, is a reflection of that philosophy-focused on building custom, human-centered AI systems that improve real workflows.

Clients will benefit from faster delivery, better decision-making, and more flexible roadmaps, all thanks to AI agents that handle the heavy lifting in routine tasks. Cleevio’s internal success is proof that the model works.

“It’s not about hype. It’s about mindset.”
At Cleevio, AI isn’t a flashy add-on. It’s a core capability built into the culture, not bolted on top. Teams don’t work around AI; they work with it. And with the launch of Cleevio AI Automations, the company is ready to help others do the same.

Your app is growing. That’s the good news.
The bad news? So are the reviews, tickets, and unexpected bugs no one saw coming.

We needed a fast, affordable way to handle it all, so our team could focus on what truly matters: high-impact work.

29.4 hours later, we had a solution.

We call it UpRate — and now, anyone can use it.

What is UpRate?

UpRate is your AI support teammate.

It handles repetitive tasks that kill your team’s flow — like replying to store reviews and searching for the right info in your knowledge base.

No fluff. Just actual time saved.

How we built it (and what it actually does)

Two people on our team were constantly switching context to reply to app store reviews, dig through sheets for answers, and forward support requests to the right teammate. It wasn’t a full-time job — but it was eating up way too much of their time.

So we gave ourselves a challenge:
Can we automate this in a way that actually helps our team — and ship it fast?

Here’s how we approached it:

• Code: Jumped between Cursor and Claude’s code interpreter
• UI: Used v0.dev to generate more complex components
• Landing page: Sketched in Lovable, polished in Cursor
• Testing: Wrote proper tests — over 80% coverage
• LLM credits used: $32
• Dev time: 29.4 hours total

And yes — it works.
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The tool now:

• Replies to all app store reviews instantly, using your tone and policies
• Pulls from a shared, limitless knowledge base — even across multiple apps
• Understands and translates reviews in foreign languages
• Routes support issues to the right person when needed

We didn't over-engineer it. We built what we needed — fast.
Now that it’s saving us hours each week, we’re opening it up to everyone else too.

Why we’re sharing it now

• Less time on support = more time building.
• Happier users = better ratings.
• No more burnout over repetitive tasks.

Real talk:

We didn’t build UpRate because AI is cool.
We built it because support was dragging down our team’s productivity — and our ability to build.

Now, it’s doing the heavy lifting.
Our users get faster replies.
Our PMs get useful insights.
And our team gets to focus on what actually matters: building great products.

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If your team is still manually handling app support — stop.
Let UpRate take care of it. You’ve got better things to do.

Want to see it in action? Get in touch or book a demo.

As software development complexity is increasing, efficient and reliable testing are critical. Enter Maestro, a user-friendly development testing tool that streamlines testing processes, speeds up release cycles and enhances software quality.

This article explores our journey with Maestro, highlighting challenges, solutions, and benefits as we strive towards enhanced testing and continuous improvement.

Our main goal? To use testing time efficiently, moving repetitive tasks to automation, and freeing up engineering time for activities such as critical and manual testing of a new feature before they are automated or preparing automation for a new feature.

How do we do it?

We use Maestro to automate testing scenarios and flows in our mobile applications - this helps us bring on-demand test results faster, gives us a quick retest option, and saves time.

Even though initial setup and writing tests require time investment, while automation tests run, QA engineers can focus on tasks like manual testing of new features or preparing automation for new features.

Tests are written in YAML files in a semi-codeless manner, using commands like "tapOn" and "scroll". Tests can run on hardware devices or emulators/simulators, making them ideal for end-to-end smoke tests or regression tests during fresh releases.

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Time Savings with Automation

Example Scenario:

A hypothetical mobile app with login, logout, dashboard, 2 main features, and a user profile.

Manual Testing:

• Complete smoke test: 1 hour per device
• Development period: 6 months (18 sprints, 4 releases per sprint)
• 3 devices (current and 2 older OS versions)

Approach 1: Manual Testing (First Month)

• 3 sprints, 4 releases/sprint, 1 hour/release, 3 devices/release
• Total: 36 hours/month on manual smoke testing
• Remaining: 134 hours for other testing activities

Approach 2: Automated Testing (First Month)

• Initial setup: 4 hours, fixing: 3 hours
• 3 sprints, 4 releases/sprint, 45 minutes/release, 3 devices/release
• Automated testing time utilized for other activities
• Total: 7 hours (setup + fixing)
• Remaining: 163 hours for other testing activities
• Time saved: 19 hours (29 hours without buffer)

Simulating Growth (Sixth Month, Coefficient: 1.2 per month)

• Approach 1: Manual Testing
  • Total: 269.2 hours/month on manual smoke testing
  • Negative balance: -89.2 hours (not feasible)
• Approach 2: Automated Testing
  • Initial setup: 28.8 hours, fixing: 21.6 hours, writing: 28.8 hours
  • Total: 194.4 hours (automated testing time utilized for other activities)
  • Remaining: 119.6 hours for other testing activities
  • Time saved: 208.8 hours

Summary:

• First month: 19 hours saved with Maestro
• Sixth month: 208.8 hours saved with Maestro
• Automation maintains 100% test coverage and allows QA engineers to focus on critical tasks, ensuring product quality.

Maestro represents a solution to the often complex issue of mobile application automation testing, offering organizations the tools and capabilities needed to elevate their testing practices to new heights and deliver exceptional software products that exceed customer expectations.

TL;DR

As applications grow more complex, automation helps maintain comprehensive test coverage. Automation frees QA engineers to focus on tasks requiring human insight, ensuring quality doesn't suffer even with tight deadlines.

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