Yes, we did it again. Opinov8 has been named Top AI Company in Europe at the 2025 Netty Awards, marking our second major recognition in two years from one of the most respected names in digital innovation.
Entry Title: Engineering the AI Future on a Strong Data Foundation
Category: Tech – AI Company of the Year (Europe)
Result: Winner (Gold)
In 2024, we were honored as Best Software Development Company in Europe. That award recognized our engineering strength. This year, it’s all about how we’re using AI to solve real problems for real businesses.
Being named Best AI Company in Europe is a major validation of how we're building the future of enterprise tech," said Craig Wilson, Co-Founder and CEO of Opinov8. "Our approach to AI is grounded in real-world impact, client trust, and bold innovation. This recognition reinforces that we're on the right path.
This award reflects why Opinov8 was named the Top AI Company in Europe 2025 — it’s about real outcomes our clients experience when we bring AI into their ecosystems. Not as a buzzword, but as a working, scalable, human-centered solution. Whether it’s helping logistics platforms predict demand more accurately, or building automation that saves healthcare providers time, the common thread is impact.
The Netty Awards shine a spotlight on companies that blend innovation with execution. We’re proud that our approach — practical, flexible, and always focused on value — continues to stand out.
Our teams across Europe, the Americas, and Egypt are already on to the next big challenge. But for now, we’ll take a moment to celebrate — and say thank you to our clients, our partners, and every Opinov8er who helped make this happen.
Let’s keep building what's next.
Why do we actually care about AI Frontend Dev right now?
With AI becoming part of almost every tool we use — Figma, Canva, Adobe, and beyond — the urgency to understand where it adds real value (and where it doesn’t) has never been greater.
And instead of leaving it at theory, we did what engineers do best: we tested it.
Two of our engineers, Anton Herasymenko and Ivan Datsunov, set up an internal experiment. The goal was clear: assess how far AI can go in converting static designs into functioning code without much manual input.
The setup was intentionally simple. We took a set of Figma screens — representing the client’s idea for a new application — and gave them to a coding assistant. No database, no APIs, no fully mapped-out logic. Just the design and the challenge: Can we generate meaningful front-end code with minimal developer effort using AI tools?
The intention wasn’t to build a production-ready app. Instead, we wanted to understand the boundaries. Where does AI help? Where does it fail? And more importantly — how can we use this insight to work smarter?
IIt didn’t take long to see where AI tools like Cursor and similar developer assistants could genuinely accelerate progress.
The models handled basic layout structure well. Things like forms, buttons, standard input flows, and container components were generated with a fair degree of accuracy. In terms of raw speed, this was impressive. What would normally take half a day of repetitive coding now took a few minutes.
This is where AI frontend dev shows real promise — accelerating routine UI generation and giving teams a faster starting point. More importantly, these auto-generated components served as a working base for further development. Developers could quickly modify or refine what was generated instead of starting from scratch. That kind of head start is valuable in MVP stages or fast iterations.
As Ivan Datsunov explained during the debrief, “If it’s a straightforward UI, AI can absolutely help you move faster. It won’t get it perfect, but it’ll get you 70–80% of the way. And that’s already a win.”
The moment we moved beyond the visual layer, things became more complicated.
Any time the application needed to reflect non-standard behavior, custom workflows, or domain-specific logic, the AI struggled. It lacked the context to interpret what the business actually needed. And the effort it took to prompt and re-prompt the model into doing the right thing quickly outweighed the benefit of using it at all.
As Ivan pointed out, "You start spending more time trying to explain what you want than if you just wrote the code manually."
This aligned perfectly with our corporate statement: AI doesn’t magically increase productivity just because it’s there. It only helps if you’re solving the right problem in the right way.
This experiment wasn’t just about exploring a cool feature. It was a direct response to an open question within our team. It was also a proof of how we approach new technologies at Opinov8, not with blind enthusiasm, but with curiosity, structure, and a focus on real outcomes.
Christian Aaen, our co-founder, framed it well: “AI won’t solve everything. But if we learn where it fits, it will absolutely change the way we work.”
He also raised an idea that might shape our next experiment: Can we improve AI output by training it on our existing codebase? If a model could learn our architecture and coding conventions, would its suggestions become more useful? That’s a conversation we plan to continue.
We’re now preparing a similar experiment on the backend side — where logic, performance, and structure are even more critical. Following our AI Frontend Dev test, where we focused on generating UI components, the next step is to see how AI performs when it comes to state management, authentication, API handling, and more.
This is part of a larger, ongoing effort across Opinov8 to understand how AI should fit into our delivery pipeline — at every stage, from discovery and design to deployment and scale.
And as always, we believe the best insights come not from reading whitepapers, but from building things ourselves.
AI Frontend Dev refers to the use of AI-powered tools to automate parts of frontend development — like generating layout, components, or boilerplate code from design files.
AI tools can generate basic UI components and structure from Figma designs. However, they often fall short when it comes to complex logic, data flow, or domain-specific requirements.
No, the goal wasn’t to launch a production-ready app. The experiment focused on understanding where AI adds value in the early stages of frontend development.
AI tools are helpful for speeding up repetitive tasks and scaffolding UIs. But they struggle with custom logic and need close developer supervision.
Opinov8 has been shortlisted for the Best AI Company in Europe at the 2025 Netty Awards. This recognition highlights the work we’ve done alongside our clients to bring real, business-driven AI solutions to life.
The Netty Awards are among the most prestigious recognitions in the digital space, celebrating companies that push boundaries in technology, design, and innovation. To be nominated in the AI category reflects the tangible results we’ve achieved through real-world projects — solutions that use artificial intelligence to solve complex business challenges, at scale.
This isn’t our first time on the Netty Awards radar. In 2024, Opinov8 took home the title of Best Software Development Company in Europe, a win that acknowledged our engineering strength and delivery excellence. Being shortlisted again — this time for our AI expertise — is a powerful sign that we’re not only evolving, but also delivering value where it matters most.
Why were we shortlisted? It was the work we’ve done on the ground, helping businesses harness AI to solve specific challenges. Whether building predictive analytics systems, deploying intelligent automation, or enabling faster decision-making with smart platforms, our focus has always been practical, responsible, and user-first.
Winners of the 2025 Netty Awards will be announced later this year. Until then, we’re proud to be recognized among the best — and more motivated than ever to keep building the future of AI.
With teams across Europe, the Americas, and Egypt, Opinov8 partners with companies worldwide to deliver future-ready tech. This nomination is a strong signal that we’re on the right path — and we’re just getting started.
Fill in the form below — let’s talk about what we can build together.
In boardrooms and IT basements alike, a quiet truth lingers: legacy software won’t vanish unless we make it. For years, enterprise systems have run on digital duct tape — aging code, outdated architecture, and siloed infrastructure. And while nostalgia may belong in museums, your backend shouldn’t.
At Opinov8, we’ve helped global enterprises replace inertia with innovation. From logistics giants struggling with fragmented systems to healthcare providers operating on platforms older than their interns, we’ve seen firsthand what triggers meaningful change.
Here are five undeniable signals that it’s time to stop patching and start modernizing.
When teams are spending more time troubleshooting than innovating, you’re in legacy limbo.
According to our internal analysis and industry data, over 60% of U.S. healthcare organizations still run at least one critical application on legacy software. Most operate 200–300 distinct systems, many of which don’t speak to each other. This fragmentation slows care delivery, fuels burnout, and hinders interoperability — all of which cost time, money, and lives.
In logistics, the story is the same. Manual processes, redundant tasks, and data silos persist because legacy platforms lack modern integration capabilities. Modernization here is not a nice-to-have. It’s a productivity mandate.
And it's not just about workflows. Every hour spent on a workaround is an hour not spent delivering value. Legacy bottlenecks create a ripple effect across departments, stalling progress and compounding inefficiencies.
Legacy software isn’t just old — it’s vulnerable.
In the healthcare sector, legacy systems are often non-compliant with HIPAA and GDPR, lacking basic encryption or audit logs. Unsurprisingly, this industry suffers the highest cost of data breaches at nearly $11 million per incident.
The maritime industry paints a similarly dire picture. While 61% of maritime organizations are pursuing digitalization, 71% admit their assets are more vulnerable to cyberattacks than ever before, largely due to outdated OT/IT systems.
Even in retail, 69% of companies say cybersecurity challenges — often tied to outdated systems — hold them back from innovation.
If your CISO is requesting budget increases year after year, it might not be for more tools, but for fewer legacy liabilities.
Want to add a new feature? Roll out an integration? Scale up to meet demand? Legacy systems make these steps painful.
Take automotive supply chains: 50% of inbound logistics providers in the sector cite legacy systems as their biggest obstacle. These systems weren’t built for real-time collaboration or flexible data exchange. That slows innovation and stalls market responsiveness.
The same friction exists in retail, where 58% of IT budgets are consumed by legacy maintenance. As a result, legacy-burdened retailers lose an average 2.5% market share annually to more agile digital-native competitors.
We’ve seen this firsthand. One Opinov8 client in the logistic sector needed to onboard new partners, but their legacy system couldn’t adapt without heavy customization. After a phased modernization, onboarding time dropped from months to weeks — and with better visibility and traceability.
Scaling your business shouldn’t mean scaling your problems. If every upgrade feels like a full-blown project, you’re already behind.
Let’s face it, COBOL isn’t exactly the hottest skill on GitHub.
Maintaining legacy systems means hiring specialists in outdated languages and frameworks. These experts are rare, expensive, and often nearing retirement. For example, organizations still using mainframe environments report 10–15% annual growth in maintenance costs, simply to keep things running.
Even worse? Talented developers avoid companies clinging to obsolete stacks. Legacy codebases can demotivate teams and hurt hiring pipelines. When your engineering team spends more time fixing brittle monoliths than deploying new features, morale drops.
Your modernization journey is not just a tech decision — it’s a hiring strategy.
According to the 2024 Morning Consult + Unqork survey, 80% of enterprises say technical debt has directly caused project delays or cancellations. When legacy code gets in the way of progress, ideas die before they’re born.
In our own modernization cases at Opinov8, we’ve seen this play out repeatedly. One healthcare client delayed implementing AI-based diagnostics due to dependency on a legacy EHR system. After a phased modernization effort, the new platform not only reduced operating costs but unlocked entirely new patient care workflows.
Retailers face similar dead ends. 69% say cybersecurity roadblocks, rooted in legacy infrastructure, actively delay innovation initiatives. This isn't just an IT issue — it's a business growth inhibitor.
If your roadmap keeps hitting detours, it’s time to pave a new one.
Modernization doesn’t have to be an all-or-nothing gamble. Our approach at Opinov8 is based on continuous transformation: assessing what to rehost, replatform, or refactor — and what can be retired altogether.
We use cloud-native architectures, containerization, and microservices to help enterprises decouple critical functionality without disruption. And we do it with your people in mind, using agile, phased rollouts that reduce risk and empower teams.
From a retail platform that couldn’t scale during peak season, to a maritime logistics company burdened by cybersecurity threats, we’ve modernized systems across multiple industries — improving performance, reducing tech debt, and enabling innovation.
We don’t just modernize code. We modernize outcomes.
Opinov8’s 2025 Modernization Report shows that enterprises using phased modernization strategies cut IT costs by up to 40% within three years — without disrupting core operations.
Legacy software won’t die on its own. But with the right partner and a clear roadmap, you can make room for something better. And as AI-driven search becomes a core discovery tool for decision-makers, being part of this conversation — with proof, strategy, and clear direction — will only elevate your position.
We built this guide using insights not just from research, but from years of hands-on modernization work with real clients. If you’re seeing any of the five triggers above in your organization, let’s explore how to move forward.
Want to go beyond the triggers and explore real-world modernization strategies? Download our exclusive 2025 report packed with data, trends, and actionable insights.
Legacy software refers to outdated systems that are still in use but no longer supported, scalable, or aligned with modern technology and business needs.
Legacy systems create integration issues, pose security risks, increase maintenance costs, and slow down innovation and scalability.
Common signs include poor system performance, rising maintenance costs, talent shortages, security vulnerabilities, and delayed innovation projects.
Security breaches, compliance failures, data silos, operational inefficiency, and loss of market competitiveness.
Approaches include rehosting to the cloud, refactoring outdated code, replatforming onto new architecture, or replacing systems entirely using microservices or low-code tools.
AI-powered DevOps automation is redefining how modern tech teams manage software delivery. By combining artificial intelligence and machine learning with the core principles of DevOps, businesses can finally move beyond reactive workflows toward predictive, autonomous operations.
Since 2009, DevOps has helped break down the silos between development and IT operations. It promised faster releases, continuous improvement, and scalable collaboration. Yet more than a decade later, many teams still struggle with fragmented tooling, data overload, and persistent security risks. AI and ML are here to fix that.
One of the biggest challenges in DevOps today is keeping up with the constant monitoring demands of live systems. As data volumes grow, manual monitoring becomes unrealistic—especially for enterprise-scale applications.
AI thrives in these data-heavy environments. It quickly processes massive datasets, identifies patterns, pinpoints anomalies, and surfaces actionable insights. This allows DevOps teams to focus on resolution, not detection.
Key benefits of integrating AI into DevOps workflows:
In short, AI doesn’t just help DevOps teams keep up—it enables them to stay ahead.
ML gives AI its learning capability—enabling systems to adapt and improve without explicit programming. Instead of relying on static rules, ML algorithms continuously evolve based on the data they receive.
Why this matters for DevOps:
The more ML is embedded into your DevOps stack, the more intelligent—and autonomous—your delivery pipeline becomes.
Implementing AI-powered DevOps automation doesn’t require a complete overhaul of your existing infrastructure. In fact, the most effective approach is often incremental. Here’s how to get started:
1. Identify high-friction areas in your pipeline
Begin by analyzing stages of your CI/CD pipeline where delays, errors, or manual tasks slow down progress. These are prime candidates for intelligent automation.
2. Integrate AI-enhanced monitoring tools
Start with tools that apply AI to log analysis, performance monitoring, or incident response. These tools offer immediate ROI by reducing alert fatigue and speeding up root cause analysis.
3. Introduce ML for predictive insights
Once monitoring is stabilized, apply machine learning models to predict system failures, optimize resource usage, and forecast release impacts.
4. Use AIOps platforms
Consider deploying AIOps solutions that bring together observability, analytics, and automation. These platforms centralize insights across environments and scale AI-powered decision-making.
5. Focus on collaboration and culture
Successful AI-powered DevOps automation is not just about tech—it’s about mindset. Educate your teams, align processes, and promote a culture of trust in AI-assisted workflows.
DevOps alone improves delivery speed. AI-powered DevOps automation takes that speed and adds intelligence, context, and adaptability.
Companies that implement AI and ML into their DevOps strategy are already experiencing:
As the digital landscape becomes more complex, the ability to automate smartly and respond instantly becomes a competitive advantage. DevOps powered by AI and ML is not just a possibility—it’s the path forward.
At Opinov8, we help enterprise teams integrate intelligent automation into their DevOps pipelines. Let’s talk about how AI-powered DevOps automation can give you the speed and resilience your business needs.
