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AI in Mobile Development

How hard is it to develop an AI app? – In the realm of AI, it is a constant journey and not a destination. Indeed, AI developers and experts are on a mission of solving the most complex problem – human behaviour. They are on a path to study patterns and produce results that a human being would most likely exhibit.

In the making of all of this fabulous innovation, what kind of challenges does an AI developer face? What are the hindrances in their role? Does AI Development manager approach in a responsible manner? To answer many such question lets dive deep into some of the stories of AI development.

‘AI – Opportunities’ in mobile app development

AI is kind of magic wand to its innovators, true to its nature of being complex it hosts a bunch of opportunities’ for developers to explore the world….

Voice Enablement Helps in understanding customer better and delivering the best

How often have you called up customer care to complain when the internet is not working or DTH not working? The first thing they ask you is – what kind of problem are you facing? While at times the problem is simple, many times the executives try to know the exact steps to reach a particular problem. While manually saying click this, click that could help, voice recognition or voice enablement allows developers in identifying the exact process that was followed.

As the user says OK Google on his phone, followed by instruction check new emails or the weather or the best deal for iPhone, it helps developers in understanding the behaviour of the customers. The kind of apps they use most, what are the instructions provided, what kind of instructions not working. The voice input also helps in understanding customers expectations from an app. I remember when my nephew instructed Google Home “You are useless,” the answer came in was I am sorry to disappoint you, and I would let my engineers know about it.”

Simplifying Complex needs

The most exciting opportunity for an AI app developer is about streamlining complex processes and workflows. Well, indeed otherwise how would the language translation work out? Or how could a chatbot help in resolving human beings technical problems? Or could you fathom of any human being going through thousands of lines of log to look for something suspicious? Or how about commanding Voice assistant to locate the best restaurant near you serving Mediterranean food?

All these are the needs to structure and present data in the simplified form. Thanks to AI app developer.

‘AI – Challenges’ in Mobile App Development

Well, the aim is to simplify lives but what are the challenges faced by developers?

No Standards tools and languages

While Google has launched some of the projects like Teachable Machine and Google AI tools to let users experience how AI works, it is still a challenge for developers to start off. In fact, Quora is flooded with queries like what are the languages or software used to develop an AI app. Many firms use Python due to the benefits it offers but has its limitations like weak in mobile programming and enterprises desktop shops. Similar is the case for other software languages like – Prolog, JAVA, C++ and LISP programming languages for artificial intelligence research

Lots of data create confusion

However, it’s the data that helps in creating the best AI app; the irony is that its also in a massive amount at times challenging to segregate and structure. With big data buzz and data tracking now a trend, developers at times face a hurdle in putting the data sets in a meaningful way.

The new availability and advancement of AI and ML are causing a revolutionary shift in the way that developers, businesses, and users think about intelligent interactions within mobile applications.

 

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Why Netflix Broke Itself: Was It Success Rewritten Through Platform Engineering?

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Let’s take a trip back in time—2008. Netflix was nothing like the media juggernaut it is today. Back then, they were a DVD-rental-by-mail service trying to go digital. But here’s the kicker: they hit a major pitfall. The internet was booming, and people were binge-watching shows like never before, but Netflix’s infrastructure couldn’t handle the load. Their single, massive system—what techies call a “monolith”—was creaking under pressure. Slow load times and buffering wheels plagued the experience, a nightmare for any platform or app development company trying to scale

That’s when Netflix decided to do something wild—they broke their monolith into smaller pieces. It was microservices, the tech equivalent of turning one giant pizza into bite-sized slices. Instead of one colossal system doing everything from streaming to recommendations, each piece of Netflix’s architecture became a specialist—one service handled streaming, another handled recommendations, another managed user data, and so on.

But microservices alone weren’t enough. What if one slice of pizza burns? Would the rest of the meal be ruined? Netflix wasn’t about to let a burnt crust take down the whole operation. That’s when they introduced the Circuit Breaker Pattern—just like a home electrical circuit that prevents a total blackout when one fuse blows. Their famous Hystrix tool allowed services to fail without taking down the entire platform. 

Fast-forward to today: Netflix isn’t just serving you movie marathons, it’s a digital powerhouse, an icon in platform engineering; it’s deploying new code thousands of times per day without breaking a sweat. They handle 208 million subscribers streaming over 1 billion hours of content every week. Trends in Platform engineering transformed Netflix into an application dev platform with self-service capabilities, supporting app developers and fostering a culture of continuous deployment.

Did Netflix bring order to chaos?

Netflix didn’t just solve its own problem. They blazed the trail for a movement: platform engineering. Now, every company wants a piece of that action. What Netflix did was essentially build an internal platform that developers could innovate without dealing with infrastructure headaches, a dream scenario for any application developer or app development company seeking seamless workflows.

And it’s not just for the big players like Netflix anymore. Across industries, companies are using platform engineering to create Internal Developer Platforms (IDPs)—one-stop shops for mobile application developers to create, test, and deploy apps without waiting on traditional IT. According to Gartner, 80% of organizations will adopt platform engineering by 2025 because it makes everything faster and more efficient, a game-changer for any mobile app developer or development software firm.

All anybody has to do is to make sure the tools are actually connected and working together. To make the most of it. That’s where modern trends like self-service platforms and composable architectures come in. You build, you scale, you innovate.achieving what mobile app dev and web-based development needs And all without breaking a sweat.

Source: getport.io

Is Mantra Labs Redefining Platform Engineering?

We didn’t just learn from Netflix’s playbook; we’re writing our own chapters in platform engineering. One example of this? Our work with one of India’s leading private-sector general insurance companies.

Their existing DevOps system was like Netflix’s old monolith: complex, clunky, and slowing them down. Multiple teams, diverse workflows, and a lack of standardization were crippling their ability to innovate. Worse yet, they were stuck in a ticket-driven approach, which led to reactive fixes rather than proactive growth. Observability gaps meant they were often solving the wrong problems, without any real insight into what was happening under the hood.

That’s where Mantra Labs stepped in. Mantra Labs brought in the pillars of platform engineering:

Standardization: We unified their workflows, creating a single source of truth for teams across the board.

Customization:  Our tailored platform engineering approach addressed the unique demands of their various application development teams.

Traceability: With better observability tools, they could now track their workflows, giving them real-time insights into system health and potential bottlenecks—an essential feature for web and app development and agile software development.

We didn’t just slap a band-aid on the problem; we overhauled their entire infrastructure. By centralizing infrastructure management and removing the ticket-driven chaos, we gave them a self-service platform—where teams could deploy new code without waiting in line. The results? Faster workflows, better adoption of tools, and an infrastructure ready for future growth.

But we didn’t stop there. We solved the critical observability gaps—providing real-time data that helped the insurance giant avoid potential pitfalls before they happened. With our approach, they no longer had to “hope” that things would go right. They could see it happening in real-time which is a major advantage in cross-platform mobile application development and cloud-based web hosting.

The Future of Platform Engineering: What’s Next?

As we look forward, platform engineering will continue to drive innovation, enabling companies to build scalable, resilient systems that adapt to future challenges—whether it’s AI-driven automation or self-healing platforms.

If you’re ready to make the leap into platform engineering, Mantra Labs is here to guide you. Whether you’re aiming for smoother workflows, enhanced observability, or scalable infrastructure, we’ve got the tools and expertise to get you there.

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