WebLab Technology

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WebLab Technology

Experienced Full-Cycle AI Dedicated Development Teams. Since 2013.

WebLab Technology is a dedicated development team specializing in designing and delivering digital products and strategic consultancy services for complex technological challenges. We architect and develop cloud-native applications, leveraging microservices, hybrid front-end, applied AI, and sophisticated automation to create cutting-edge digital products optimized for each client's needs and obje

ctives. Our story began in 2013 with just five people: developers, QA professionals, and data analysts. Over time, WebLab Technology has grown to nearly 50 professionals, including multiple engineers with PhDs in computer science. Our portfolio consists of many successful projects focused on complex logic and sophisticated workflows rather than visual designs or animations. We specialize in back-end systems and data-driven products that require rock-solid engineering skills. By combining a profound strategic perspective with expert product development, we assist companies in creating transformative solutions, leveraging new opportunities, and achieving digital leadership in their industries.

29/08/2025

Завжди раді долучатись до ваших освітніх ініціатив і ділитись досвідом, який допоможе і молоді, і вчителям будувати міцний IT-базис в нашій країні 🫶

29/08/2025

Time zones shouldn’t slow down your team – they should define your rhythm.

At WLTech.AI, asynchronous work means focus, ownership, and smart overlap windows where collaboration happens. Everyone contributes when it matters, and the team continues to make progress without waiting on anyone.

Here's why Async First is a good fit for us:
🔹 Structured Overlap. We do our best to make sure our team members have dedicated hours for working together in the same time zone, so they can talk in real-time when needed, while also respecting each person's work schedule.
🔹Ownership Culture. Everyone on the team is responsible for their own tasks, which leads to better accountability and quality outcomes.
🔹Productivity Boost. Research shows that remote workers can be 35% more productive than those working on-site. We can do this by cutting out meetings that aren't really necessary and setting times for people to focus on the task at hand.
🔹Reduced Burnout. The "always-on" culture can lead to burnout. A survey found that 47% of UK workers are setting aside time to focus better by turning off their phones and not answering any calls. Our asynchronous approach is great for people who need to have uninterrupted work time.

We use platforms like Jira and Slack to keep documentation clear, track progress, and make it easy to communicate when people aren't in the same place.

When we allow our team to work on their own schedule, they can manage their time better. This leads to more productivity, job satisfaction, and a better balance between work and personal life.

If this approach speaks to you, you could join a team that values ownership, syncs when it matters, and gives space to do your best work. Check out our open positions here: https://www.wltech.ai/jobs

29/08/2025

Most startups love to brag about their tech stack. We focus on something different – the people who make it actually work. Everything starts with the problem, not the code.
What matters most? Saving money, automating processes, improving user experience, not flashy one-off ML experiments that vanish in a week.

We move fast, but carefully. Prototypes go out quickly, tested constantly, and only expanded when their value is clear. By doing this, we avoid costly errors in infrastructure or staffing, keeping the work precise and purposeful.

People are at the heart of every decision. All of the solutions make it so they're on the same page. Clear paths to growth, ongoing feedback, and openness make technology a tool that helps people achieve more and work productively.

Data comes first. Almost half of AI project failures happen because of messy data or poor integration. Cleaning, organizing, and managing data is a part of the foundation for a project that actually works.

Our AI squads are different. They've got over ten years of experience, so they really get the business, the users, and the context. These teams mix technical skill, accountability, and deep insight to come up with solutions that help solve customers' needs on a day-to-day basis.

And it works. Research shows that teams owning their product deliver better code and happier users. Microsoft saw this in projects like Windows Vista, Eclipse, and Firefox. McKinsey points out that teams that communicate well and understand their goals build software faster and with higher quality.

We don’t put teams together for the sake of it. We bring together people who can take ownership, think ahead, and make the technology around them genuinely useful.

18/08/2025

A good TypeScript setup catches mistakes. A great one makes them impossible to write.
These tricks aren't from blog posts. They come from spending long weekends debugging systems in the field and figuring out how to prevent that from happening again.

Open it if you're ready to stop writing types and start designing systems 👇

12/08/2025

Радіємо, що доєднались до потужного комьюніті однодумців Diia.City Union 🫶

12/08/2025

A lot of businesses put a lot of pressure on themselves to get into AI by hiring engineers or starting pilots, and then they just get stuck. Projects get put on the back burner, tools go unused, and budgets get raided because no one ever asked: What problem are we actually solving?

The numbers tell a story:
🔹 Between 70% and 85% (https://rheodata.com/ai-failures-stats/?utm_source=chatgpt.com) of AI projects fail before reaching production due to poor matching with business needs.
🔹 Only 11% (https://www.cfodive.com/news/AI-project-fail-data-SPGlobal/742784/?utm_source=chatgpt.com) of companies say they've seen measurable results from their AI investments, and 44% say it's fallen short of their expectations, according to TechRadar.
🔹 45% (https://www.techradar.com/pro/nearly-half-of-all-code-generated-by-ai-found-to-contain-security-flaws-even-big-llms-affected?utm_source=chatgpt.com) of AI-generated code contains security issues, even from leading large language models.
🔹 84% (https://www.itpro.com/software/development/developers-arent-quite-ready-to-place-their-trust-in-ai-nearly-half-say-they-dont-trust-the-accuracy-of-outputs-and-end-up-wasting-time-debugging-code?utm_source=chatgpt.com) of developers use AI tools, yet 46% don’t trust the outputs, citing accuracy concerns and wasted time debugging.

Organizations like Booking.com and Shopify offer a better path. Booking.com has been working on its machine learning (ML) platform, and they've got over 200 engineers (https://deptofux.com/how-booking-com-modernized-its-ml-experimentation-framework-with-amazon-sagemaker/?utm_source=chatgpt.com) and scientists on board. They're infusing intelligence into the user experience and personalization, all thanks to a well-established infrastructure with constant experimentation and feature pipelines. Meanwhile, Shopify now requires teams to justify new hires by showing why AI can't perform the task instead as part of its new internal memo and performance criteria.
We take a similar approach and adapt it for teams that want to move fast without the unnecessary extra steps.

Here’s how we work:
🔹 Start with a business problem, not hiring.
We help our clients come up with use cases that count, like saving money, automating things, and making improvements to the customer experience – not one-off ML experiments.
🔹 Keep it simple and test fast.
We prototype quickly and only scale when the value is concrete. This helps prevent expensive mistakes with infrastructure and staff.
🔹 Put people first.
We always include human involvement in our solutions. We set clear ways to override things, create feedback cycles, and make things clear—we're not replacing humans, but giving them more power.
🔹 Prioritize data readiness.
Failures in AI projects are often due to poor data or integration issues, with up to 43% of them resulting from these problems. We handle data cleanup, pipelines, and governance as part of our service.

Code and models are tools, but they're not the goal. The best part is when AI starts figuring out ways to solve specific business headaches and becomes a part of the process.
That's why we focus on building AI teams for the problems that actually need solving. When it’s time to move from trying things out to getting results, we are always available to share our cases and visions of proper functioning of AI teams.

07/08/2025

Remote work that actually works: What we’ve learned after 10 years of doing it daily 💻

We don’t do remote work. We live it. Since 2014, WLTech.AI has been fully remote - not temporarily, not hybrid, not in theory. It’s not a benefit we mention at the end of job descriptions. It’s the way we work, think, and build. And over the years, we’ve figured out a few things that keep it working, not just functionally, but well.

Here’s what we’ve learned that doesn’t always show up in articles:
1. Teams need rhythm, not micromanagement
We don’t run around with calendars full of meetings. But we don’t go silent, either. There’s a rhythm - daily check-ins, weekly syncs, and regular updates that give people enough context to move fast without getting lost. No one’s left wondering what’s going on.
2. You can’t fake presence
When something’s unclear, we jump on a call. Not in an hour. Now. Just five minutes can save five hours of guessing. Quick calls > long threads.
3. Recognition doesn’t need approval
If someone helped you, you thank them publicly. That’s it. You don’t wait for a review cycle or a team lead. We’ve built a culture where giving credit is part of working together, not a scheduled event.
4. Culture lives in the small things
No one here is trying to replicate an office online. We don’t have forced Google Meet hangouts. But we do post memes, share photos in merch, send AI updates, and joke in threads that have nothing to do with tasks. Those small things are the culture. And they work because they’re real.
5. Trust isn't a policy
You don’t need to ask for time off. You don’t need to explain a slow morning. You don’t need to prove your hours. You just need to do your work, be kind, and let people know what to expect. That’s how trust looks when it’s practiced, not just promised.
6. Remote leadership = noticing what’s not said
When you don’t see people in person, you have to listen differently. Energy drops. Tone shifts. Delays that aren't typical. Our leads stay close, even without being visible. Sometimes support is a message that just says: “How are you doing, really?”

For our team, remote is the way we’ve worked since day one and what keeps working now, because we keep shaping it together.

We’re not guessing anymore. We’ve had a decade to get it right. And we’re still hiring.

21/07/2025

What does it really feel like to be part of WebLab Technology?

In our first post, QA engineer Yaroslav shared how trust, flexibility, and metal playlists shape his workday. Now we’re continuing the series, honest answers from inside the team, with a new voice.
Meet one of our Software Developers, Andriy. Read on for his thoughts on first months at WLTech.AI and what he found most notable.

🔹 1. How would you describe the team at WLTech.AI in a few words?
A smart, hardworking, and strong team of specialists you genuinely enjoy working with.

🔹 2. What felt like the biggest challenge when you joined?
Probably figuring out the codebase - what works where and how. Every company has its own style, and I had to get used to that. But over time, it all started to fall into place. Now it doesn’t feel hard at all, just interesting.

🔹 3. What brings you the most comfort in WLTech.AI’s fully remote format?
The fact that I can work remotely at all. No morning commute to an office an hour away. More focus, fewer distractions - it’s a win.

🔹 4. What makes WLTech.AI’s culture feel different to you?
Trust and results. That’s what stands out most. You’re trusted to do your work well and on time. Everyone works toward a shared result, but with full freedom, your own style, and your own approach.

🔹 5. What’s one thing you’d bring back or improve?
The Coffee Meetings. They were a great excuse to take a break from code and just chat about life with colleagues, cup in hand instead of mouse.

🔹 6. Which of our values really resonate with you?
Autonomy and trust. And the focus on building something that matters, not just doing things because “that’s the process.”

🔹 7. Do you have a favorite remote work ritual or setup?
Yes,I often start with a song from my favorite band, Twenty One Pilots. After that, it’s like a turbo button gets pressed. Focus kicks in, and I’m in the flow.

These answers give you a little taste of how people really work at WLTech.AI - with trust, support, ownership, and the space to stay human.

We’ll keep sharing more from the team soon! And if this sounds like a place where you’d feel at home, you can always check our open roles: https://www.wltech.ai/jobs

14/07/2025

You can hire the most brilliant CV in the room and still break your team.

Google did the research (https://rework.withgoogle.com/en/guides/understanding-team-effectiveness ). After years of chasing the smartest people, they ran a deep internal study to understand what actually makes teams successful. It wasn’t IQ or technical skill. It was psychological safety - the ability to speak up, ask questions, admit mistakes, and still feel respected.

Project Aristotle (https://www.leadingsapiens.com/project-aristotle/?utm_source=chatgpt.com) proved it. And we’ve seen the same at WLTech.AI.
We’ve been building fully remote since 2014 and we’ve learned that resumes don’t build teams, but shared values do!

When we talk about “the right candidate,” we don’t mean someone who ticks off tech stacks. We mean someone who works in a way that makes the whole team better.
The kind of person who:
– speaks up early, not just when it’s safe;
– knows how to code clearly and communicate even clearer;
– admits when they’re stuck and helps when others are;
– owns what they build like it’s theirs, because it is;

Our values are integrated into everything we do, especially the hiring process.

So when we talk about the “right candidate,” here’s what we really mean:
– open-minded: they learn fast, adapt faster;
– humble: they don’t need to win every debate;
– respectful: they listen with intent, not ego;
– honest: they say when something’s not working;
– self-driven: they don’t wait for permission to care;
– owners: they treat the work like it matters and take responsibility for the results;

According to HBR (https://hbr.org/2016/01/the-top-complaint-from-talent-40-of-employees-are-considering-leaving-their-jobs), 80% of employee turnover is caused by bad hiring decisions. That’s a big price to pay for ignoring team fit. That’s why we look for the people who fit how we work. We don't mean “culture fit” in the superficial sense. We mean the ability to build in a way that is asynchronous, ownership-driven, and long-term focused.

If this sounds like the kind of team you want to join, take a look at our open positions - we might be the right fit for each other: https://www.wltech.ai/jobs

30/06/2025

In AI development, speed is just one piece of the puzzle - consistency, ownership, and team focus are what truly drive success.
That’s why we work in the dedicated team model: not task-based, not short-term, but continuous product partnerships where engineers grow alongside the solution they build.

This approach isn’t new, but it's becoming more popular for good reason. According to Global Outsourcing Survey 2024 from Deloitte, 80% of execs plan to either keep investing in external delivery or increase their spending in this area over the next year. This shows that businesses are looking for teams that are fully in sync with their own product objectives.

Here’s how a dedicated team works at WLTech.AI:
🔹 You don’t just get engineers - you get continuity
Each client has a long-term, integrated team that works within your stack, understands your roadmap, and contributes as if they were in-house. This consistency builds deep technical context and saves time on onboarding or re-explaining goals.
🔹 Our CTO personally interviews every engineer
We make sure that technical requirements are met. We carefully select talent with the right experience in LLMs, modular content, RAG, AI Agents.
🔹 We scale as you scale
If you need to grow your team or integrate a new AI layer, we'll bring on people who already understand your architecture and product context. This way, scaling up doesn't mean starting over — it means teaming up with people who are already familiar with the process.
🔹 You know exactly who you’re working with
No middlemen. No rotation of anonymous resources. Just the same team showing up every day with full ownership of their work. Poor internal communication can really mess with productivity, and getting it better can increase team output by as much as 25% (https://lnkd.in/eSgEFFAj). Our engineers will join your Slack, attend standups, and stay close to your roadmap.

We’ve worked this way since the beginning. With one of our clients, Shaman, for example, our dedicated team has grown from a few engineers to over 50. That kind of long-term rhythm is hard to build in a rotating freelancer model.

For sure, dedicated teams aren't for everyone, but for long-term AI projects, where models keep changing, data keeps piling up, and requirements keep shifting, they outperform fragmented resourcing every time.

WLTech.AI builds teams that become part of the company's thinking and help products grow with fewer roadblocks, more focus, and clearer accountability. If you're thinking about that, we're always open to a chat.

16/06/2025

Our CTO and Head of AI, Oleksandr Knyga, recently joined a delegation of Ukrainian tech leaders in Washington as part of the U.S.–Ukraine Tech Partnership Initiative, organized by StrategEast and supported by the Ministry of Digital Transformation of Ukraine.

The delegation took part in a series of meetings with U.S.-based tech companies, strategic advisors, and global teams like AWS, discussing dual-use technologies, joint venture models, and opportunities for long-term cooperation between Ukrainian and American tech sectors.

Big thanks to the organizers and partners who made this visit happen. Deep gratitude to Diia City, Kharkiv IT Cluster and to the Ministry of Digital Transformation of Ukraine for supporting this initiative.

For WLTech.AI (WebLab Technology), it was a chance to share what we’re working on and speak with people who care about practical cooperation between Ukrainian and U.S. tech teams. With strong engineers in Ukraine and wide experience in the U.S., there’s a lot we can do together, especially in defense tech, digital infrastructure, and cybersecurity.

12/06/2025

Imagine having a conversation with someone you trust only to realize later that someone else was whispering in their ear, feeding them misleading cues, twisting their words, and quietly steering the outcome. That’s what prompt injection feels like in the world of AI. It’s sneaky, hard to detect, and more common than you might think.

If this sounds familiar, it’s because we’ve seen a version of it before. Think back to SQL injections - a major issue in traditional databases, where malicious actors manipulated queries to gain access to data they shouldn’t have. Prompt injections work in a similar way: they manipulate the input we feed into AI models, often in ways that are hard to detect until it’s too late.

For any business running on AI, this isn’t just a technical problem. It’s a matter of trust, security, and responsibility.

Here’s how to protect your AI systems:
🔹 Start with the basics. Watch what goes in. Just like we learned to guard against SQL injections years ago by cleaning up the data before it hits the database, we need to be just as careful with what we feed our AI. Think of it like filtering the noise before it becomes a message. Sanitize the inputs. Don’t let just anything in.
🔹Next, set boundaries. AI is smart, but it still needs rules. Clear, firm guardrails help prevent it from going off-script or reading too much into something it shouldn’t. Not every prompt deserves a response and not every instruction should be taken at face value.
🔹And finally, pay attention to what comes out. Don’t let AI speak into the void unchecked. Monitor the outputs, check for signs of trouble, and catch issues early before they become bigger problems.

It’s not just about keeping your system safe. It’s about protecting the people who rely on it. So, prompt injections are just one of the many concerns companies have when they're figuring out how to make sure AI works the way it's supposed to.

Gartner's latest report says that only 44% of CIOs are seen as AI-savvy by their CEOs. This is a sign that many leaders still have a hard time really understanding how AI works, which makes them vulnerable to risks like prompt injections that could disrupt their systems. IDC found that 85% of AI projects fail because the data is just not right, whether it's messy, out of order, or not right for the purpose. This data problem is a big reason why AI doesn't work as well as it should and can cause problems.

At WLTech.AI, we recognize the risks related to prompt injections and AI security. In our projects, we have taken these risks seriously and implemented a variety of protections to keep AI safe.

If you're looking for a team that understands AI security, you can contact us and discuss with our Head of AI the specifics of AI projects. Your AI should work, but it should also be safe!

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