Insights

The future of digital

Discover the trends, tech, and strategic insights shaping tomorrow's digital landscape. Written by experts, curated for innovators.

When Bluetooth devices compete: how we solve multi-device BLE challenges

icapps
May 19, 2026
·
5
min

The challenge most apps underestimate

Once the platform scaled, new needs emerged:

  • Reliable location detection: To ensure users could always end their rides, even in GPS-poor environments like underground stations, we implemented Beacon technology as a vital fallback.
  • Maintenance access: Service teams needed dedicated BLE access to battery locks for maintenance purposes.

Suddenly, the app wasn’t talking to one device anymore. It was juggling multiple Bluetooth interactions at the same time.

And that’s where things started to break.

One BLE radio, multiple demands

Smartphones only have one Bluetooth radio. Yet many apps treat it like an unlimited resource.

In practice, this leads to:

  • Silent scan interruptions (especially on Android)
  • Features interfering with each other
  • Device-specific bugs that are hard to reproduce
  • “Works most of the time” experiences that frustrate users

For platforms like Blue-bike, this directly impacts both user experience and operations. To solve this, we leveraged our partnership to build a robust architecture that treats Bluetooth as a shared system resource.

Our approach: treat BLE as a shared resource

At icapps, we’ve seen this pattern before across multiple projects. When apps evolve, Bluetooth complexity grows with them.

Instead of patching issues later, we design for it upfront.

The key insight is simple: Bluetooth should be managed like any shared system resource.

Meaning: just as a processor decides which app gets processing power, there needs to be a system that determines which function is allowed to use the Bluetooth antenna at any given moment. Without this central management, different parts of the app (such as unlocking the lock versus searching for beacons) will compete with each other for the connection, leading to failed actions and a frustrated user.

The solution: a scan coordinator

To prevent conflicts between BLE features, we implemented a centralized scan coordinator.

In short, it:

  • Controls who can scan at any given time
  • Assigns priorities (user actions over background processes)
  • Temporarily pauses lower-priority scans
  • Applies rate limiting to avoid OS restrictions
  • Ensures consistent behavior across devices

This creates a predictable and stable Bluetooth layer, even as new features are added.

Why this matters for your product

If your app connects to just one device, you might never notice this problem.

But if you’re building:

  • A connected product ecosystem
  • A mobility or IoT platform
  • A feature roadmap with future integrations

…this challenge will surface sooner or later.

And when it does, it won’t show up in testing. It will show up in production.

Designing for scale from day one

What we built for Blue-bike is not a workaround. It’s a scalable foundation.

By centralizing BLE coordination:

  • User interactions become reliable
  • Background processes stay invisible but effective
  • New integrations don’t introduce new risks

Most importantly, it allows teams to keep innovating without breaking existing functionality.

What this says about how we work

This project reflects how we approach digital products at icapps.

We don’t just build what’s needed today.
We anticipate what your product will need tomorrow.

Because in connected ecosystems, small technical decisions can have a big impact on user experience.

If you’re working on a product with Bluetooth, IoT or multiple device integrations, it’s worth asking: Are we building for today’s use case… or tomorrow’s complexity?

FAQ: Bluetooth and multi-device BLE

What is multi-device BLE?

It refers to apps interacting with multiple Bluetooth Low Energy devices, common in IoT, mobility, and connected products.

Why does Bluetooth fail with multiple devices?

Because smartphones only have one BLE radio. Multiple scans or connections can interfere, causing unreliable behavior.

How do you manage multiple BLE interactions?

By using a centralized approach, like a scan coordinator, to control access, prioritize actions, and prevent conflicts.

What are common BLE issues in mobile apps?

Unstable connections, background limitations, Android restrictions, and conflicts between multiple Bluetooth processes.

When do you need a scan coordinator?

As soon as your app connects to multiple devices or combines background and foreground BLE features

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Web development vs mobile app development: which one fits your business?

icapps
August 19, 2025
·
3
min

In this blog, we break down the core differences between web development and mobile app development. You’ll learn the pros and cons of each approach, what type of experience they offer, and how to choose what’s right for your business.

Web development: accessible, flexible and fast to market

What is it?
Web development refers to building websites and web applications that run on internet browsers like Chrome or Safari. These can range from simple marketing sites to complex platforms like online shops or SaaS tools.

Pros of web development:

  • No downloads needed: Your users only need a browser and internet connection. No app store approval or storage space required.
  • Easier to maintain: Changes go live instantly across all devices, no need for user updates.
  • Cross-platform by default: Web apps work on desktops, tablets and mobile devices with one codebase.
  • Faster time to market: Generally quicker and cheaper to build than native apps.

When to choose web development:
Web development is a solid choice if your goal is to inform, reach a wide audience, or launch quickly. Think content platforms, dashboards, internal tools, or services that benefit from easy access across devices.

Mobile app development: powerful, personal and performance-driven

What is it?
Mobile app development focuses on building applications specifically for mobile devices like smartphones and tablets. These can be native apps (built for iOS or Android), cross-platform apps (like React Native or Flutter), or hybrid apps (web tech wrapped in a mobile shell).

Pros of mobile app development:

  • Better performance: Native apps offer smoother interactions and faster response times, especially for graphics-heavy or real-time features.
  • Offline access: Apps can work without an internet connection, depending on the use case.
  • Push notifications: Great for engagement, updates and retention.
  • Device integration: Take full advantage of the devices’ hardware features like GPS, camera, biometrics and sensors.

When to choose mobile app development:
If your product relies on user engagement, personalization or hardware features, an app is the way to go. Think health tracking, navigation, mobile banking, loyalty programs or interactive content.

Web or app? Ask yourself these 5 questions

Still not sure? Start by answering the following:

  1. Who is your audience?
    Are they mainly mobile users, desktop users or both?

  2. What’s your goal?
    Is it awareness and reach? Go web. Engagement and retention? Consider mobile.

  3. What’s your budget and timeline?
    Web is usually more cost-effective and quicker to develop.

  4. What features do you need?
    If you need GPS, camera or offline access, an app might be essential.

  5. How important is visibility in the app stores?
    App presence can boost credibility but also adds friction to user acquisition.

What about both?

Sometimes, you don’t have to choose. Many businesses start with a responsive website to validate their idea, then scale with a mobile app for a deeper experience. Or they combine both from day one for maximum impact.

At icapps, we help you find the right digital approach, whether that’s a custom-built mobile app, a responsive web platform, or a cross-platform solution that blends the best of both worlds.

Need help deciding between web and mobile?
We’ve built digital products for companies like NMBS, TADAAM and Immoscoop. Let’s chat about your idea and see what fits best.

Get in touch with us!

Development

Is your digital product ready for AI implementation?

icapps
August 4, 2025
·
4
min

The primary issue isn't technology itself, but the foundation it's being built upon. Many organizations attempt to implement AI onto legacy systems. Today we notice that in many companies over 50% of the IT budget is consumed by simply maintaining outdated systems, you aren’t just losing money; you are sacrificing the ability to compete in this fast paced market. That's where Application Modernization becomes essential.

In this blog, we explore how to prepare your digital product for AI and why modernizing legacy systems is a crucial first step.

AI starts with a solid, modern foundation

AI only works when the foundation is right. That means modern, scalable, and data-driven systems, something legacy software often can’t deliver. Application modernization helps you get there, but success depends on more than just tech upgrades.

These four pillars are essential:

  1. Modern tech stack
    Cloud-native, API-first systems create the flexibility and scale AI needs.
  2. Data strategy
    AI runs on data. Break down silos with a central, event-driven architecture to unlock real-time insights.
  3. AI culture
    Adopting AI is an organizational shift. Invest in awareness, training, and change management to embed AI in your way of working.
  4. Continuous improvement
    With DevOps and MLOps, you keep evolving, training and improving AI models over time.

Not sure where to start? A solid Application Modernization assessment helps map out your current state and your path to becoming AI-ready.

What is Application Modernization?

Application Modernization is the process of updating old systems to align with today’s technologies, frameworks, and user expectations. It helps businesses improve performance, lower maintenance costs, and increase agility in order to compete in today's digital market.

Depending on your setup, modernization can be; legacy rebuilds, migration to cloud, full architecture redesign to embrace intelligence

How legacy systems block AI adoption

Legacy software is often deeply embedded in business operations, but that doesn't mean it's ready for the future. These systems typically lack:

  • Real-time data capabilities
  • Scalable infrastructure for AI models
  • APIs or integration layers
  • Support for cloud-native services

As a result, implementing AI within a legacy system is costly, time-consuming, and risky. You need to consider legacy system modernization approaches before exploring any AI use case.

Start with an Application Modernization assessment

A clear view of your current architecture is the first step. An Application Modernization assessment helps you understand what’s working, what’s not, and where AI might fit in.

This assessment should cover:

  • Data structure & availability
  • Integration & API readiness
  • DevOps & Automation
  • Cloud Readiness
  • Security, Compliance & Observability

The outcome will guide your Application Modernization strategy, ensuring you're not just chasing trends but making informed, scalable decisions.

Modernization approaches tailored to your product

There’s no one-size-fits-all path to modernization, but it always starts with an evaluation. This crucial first step helps us align on where your investments will make the most impact. From there, depending on your goals and available resources, you can choose from several Application Modernization approaches, such as:

  • Rehosting: Move your app to the Cloud without changing its code
  • Refactoring: Clean up and reorganize your code for better performance
  • Replatforming: Make minimal changes to your app to take advantage of Cloud capabilities
  • Rearchitecting: Redesign the architecture to support AI or microservices
  • Rebuilding: Recreate the app with a modern tech stack

For legacy systems, legacy Application Modernization strategies focus on minimizing disruption while upgrading functionality. Many companies also rely on legacy modernization services to support this transition.

Cloud migration as a stepping stone to AI

AI and Cloud go hand in hand. Whether you’re exploring Cloud Application Modernization or developing a full application migration to cloud strategy, this move sets the stage for smart, scalable solutions.

Cloud environments provide:

  • On-demand compute power for training AI models
  • Seamless integration with data lakes and analytics tools
  • Elastic infrastructure that grows with your user base
  • Access to cutting-edge AI services like machine learning APIs

Without Cloud capabilities, your AI plans might hit a wall. That’s why Cloud migration is often one of the first steps in any legacy software modernization roadmap.

Why now is the time to modernize

Staying competitive means embracing change. AI offers real business value, but only if your systems can support it. By investing in legacy app modernization, you’re not just fixing outdated tech, you’re building a platform for future innovation.

Here are a few reasons to start now:

  • Reduce the cost and complexity of maintaining old systems
  • Improve customer experiences with smarter features
  • Increase operational efficiency through AI-driven automation
  • Stay ahead of Application Modernization trends and market expectations

With the right Application Modernization framework, your product becomes a launchpad for AI instead of a roadblock.

Final thoughts

Modernizing your digital product is not about keeping up appearances. It’s about preparing your business for what’s next. AI is powerful, but only when your product is ready to support it. 😉

So before investing in AI, ask yourself:

  • Are you still working with legacy software?
  • Have you explored a clear legacy modernization strategy?
  • Is your infrastructure cloud-ready and scalable?
  • Do you have the right Application Modernization services in place?

If the answer is no, it’s time to rethink your foundation.

Curious how your organization can start using AI?

Innovation

Challenges, trends and benefits of Application Modernization

icapps
July 17, 2025
·
1
min

In this blog, we’ll dive into the most common challenges of modernizing legacy systems, the trends that are shaping the future of Application Modernization, and the business benefits you can expect when you make the leap.

Of course, it’s not always smooth sailing. Some common hurdles include:

  • Complex architecture or dependencies
  • Lack of documentation for legacy systems
  • Downtime risks during migration
  • Change resistance among teams
  • Budget and time constraints

That’s why working with the right Application Modernization services partner can make or break your project.

Trends shaping the future of modernization

The world of legacy modernization is evolving fast. Some current Application Modernization trends and legacy modernization trends to keep an eye on:

  • AI-driven modernization: Automated code analysis and migration recommendations.
  • Composable architectures: Building flexible systems out of modular components.
  • DevOps integration: Modernization as part of continuous delivery pipelines.
  • Cloud-native by default: Designing for scalability and portability from day one.

Benefits of Application Modernization

So what’s in it for your business? Here are just a few of the benefits of Application Modernization:

  • Reduced technical debt
  • Lower maintenance costs
  • Improved security and compliance
  • Faster time-to-market
  • Better user experience
  • Higher developer satisfaction
  • Increased scalability and flexibility

Ready to modernize?

Development

How AI can change the way we detect asbestos

icapps
July 17, 2025
·
2
min

The problem: Time-consuming and error-prone inspections

Asbestos inspections typically require extensive manual work. Inspectors must visually examine materials, take notes by hand, and write detailed reports. This process is slow, increases the chance of human error, and can delay important decisions.

The solution: AI-powered automation

By using AI, we can automate many steps of the inspection process. This means less manual work, fewer errors, and faster results. Inspectors can focus on their job while AI helps streamline their tasks.

What makes this AI solution unique?

We’ve combined AI-powered detection with speech-to-text technology to make asbestos inspections smarter and more efficient. Here’s how:

  • Voice input instead of notes: Inspectors can simply speak their observations instead of writing everything down. AI-powered speech-to-text technology (like OpenAI’s Whisper) converts spoken words into structured text, ensuring no essential details are lost.
  • Automated image analysis: Traditional asbestos identification relies on human observation, which can be inconsistent. Our AI analyzes images taken during inspections, quickly identifying possible asbestos-containing materials with greater accuracy.

Less paperwork, more productivity

Writing detailed inspection reports can be time-consuming, often taking longer than the actual inspections themselves. Our AI-powered system automatically generates reports based on recorded voice notes and captured images, saving inspectors valuable time and reducing the chance of errors.

Smarter image analysis for better detection

Detecting asbestos isn’t always straightforward. It requires careful materials analysis; human judgment alone isn’t always enough. Our AI-powered image classification technology scans photos taken during inspections and flags potential asbestos-containing materials. We plan to refine this technology even further in the future, making inspections even more reliable.

A game-changer for asbestos inspections

Traditional methods are slow and prone to mistakes, but our AI-powered solution streamlines the entire process. This technology has the potential to revolutionize asbestos detection and other types of inspections requiring strict safety and compliance checks.

What’s next?

We’ve proven that AI can make asbestos detection more efficient, but this is just the beginning. Our next steps include:

  • Improving AI-powered image recognition to enhance detection accuracy.
  • Expanding the capabilities of AI-driven report generation.
  • Developing a fully market-ready solution that can be used across industries.

Conclusion: The future of asbestos inspections is AI-driven

With AI-powered speech-to-text, automated reports, and smart image analysis, we’re creating a future where asbestos inspections are quicker, safer, and more reliable. 

Innovation
Ready to bring

Clarity to your digital challenge?

Whether you’re modernising a complex IT landscape or building a digital product that must scale and last, it always starts with the right conversation.