Generative AI: Quietly Powering Innovation in Tech

Published on Jul 31, 2023

Updated on Nov 2, 2023

In the mosaic of Artificial Intelligence (AI), generative AI subtly emerges as an increasingly significant component. Rather than making loud strides, it quietly integrates into the operational structures of tech companies, amplifying efficiencies, and innovating solutions. This article will shed light on the spectrum of opportunities generative AI presents and its influence on shaping industry dynamics.

Understanding the Invisible Artist

Let’s begin by demystifying generative AI. It’s a technological field that leverages machine learning to generate new data, modeled after the input it’s been trained on. From crafting emails to creating realistic human portraits, generative AI applications are multifold.

Re-imagining Content Creation

“Content is king,” Bill Gates famously remarked in 1996. Fast forward to today, and generative AI has taken the throne as the kingmaker. Trained on a myriad of data, AI models can generate diverse content forms from textual to audio-visual. As reported in 2020, GPT-3, developed by OpenAI, could draft contextually relevant textual content indistinguishable from human-created text. This capacity alleviates the burden of producing routine content from tech companies, allowing them to allocate resources more strategically.

Case Study: The Associated Press and Automated Insights have used AI to automate the generation of news stories, enabling the production of over 3,700 earning reports stories per quarter, a tenfold increase from the manual capacity.

Streamlining Software Development

Software development is another domain that generatively AI has been quietly revolutionizing. AI-powered tools like Codota and Tabnine suggest code completions by learning from billions of code lines, reducing debugging time and enhancing productivity.

For instance, GitHub’s pilot project, Copilot, uses AI to suggest code as you type, accelerating the development process and improving code quality.

Power of Data Augmentation

When real data is scarce, expensive, or privacy-sensitive, generative AI steps in to synthesize data that mirrors real-world attributes. This data synthesis capability has the potential to enhance machine learning model training, thus improving models’ robustness and precision.

Fact: A 2020 report by Gartner predicts that 60% of the data used for the development of AI and analytics projects will be synthetically generated by 2024.

We’ll now delve deeper into this technology’s transformative potential in user experience personalization, design prototyping, conversational systems, and anomaly detection.

Beyond the Visible Horizon – Unveiling More Potential

Unraveling the broader horizon of generative AI, let’s delve into the impact this transformative technology has on shaping user experiences, expediting prototyping, powering conversational systems, and bolstering anomaly detection in tech companies.

Tailoring Experiences: The Personalization Paradigm

“Personalization – it is not a trend, it’s a marketing tsunami,” remarked Avi Dan, a veteran marketing executive. Tech companies are riding this tsunami using generative AI. Based on a user’s behavior, preferences, and past interactions, AI systems can generate personalized content, creating a tailor-made user experience.

Netflix, for instance, is an industry leader in utilizing AI for personalized content recommendations, contributing to its substantial user engagement rates.

Prototyping: Painting with a Broader Palette

Generative AI offers a broader palette to paint from when it comes to design prototyping. It can generate numerous design prototypes based on specific parameters or criteria, speeding up the prototyping process, and fostering innovation.

A prominent example of this is Airbnb’s use of AI in their design process. They leverage generative models to rapidly create multiple design layouts, enhancing user experience and expediting the design process.

Conversational Systems: Enhancing Interactions

Generative AI’s role in powering advanced conversational agents exemplifies its quiet efficiency. Capable of generating human-like responses, AI-powered chatbots like Hitee developed by product engineering firm Mantra Labs and virtual assistants make interactions more engaging and natural.

Use Case: Mantra Labs’ Hitee, Google’s Meena, and OpenAI’s GPT-3 are advanced conversational AI models that can generate contextual and meaningful responses, significantly improving user engagement.

Anomaly Detection: The Hidden Watchman

In the realm of cybersecurity, fraud detection, and quality control, generative AI serves as an unsung hero. Trained to understand ‘normal’ patterns within a dataset, it raises alerts when data deviates from this norm.

In 2021, MasterCard integrated AI into its systems to detect and predict fraud before the user notices it, saving millions of dollars annually.

Conclusion

The integration of generative AI in the operational fabric of tech companies is subtly ushering in a transformative era. It has proven to be an instrumental tool in optimizing tasks and innovating solutions, all the while being unobtrusive.

However, the true prowess of generative AI lies not in what it has achieved, but in its potential. With continuous advancements, generative AI holds promising prospects for tech companies, offering a wider canvas for them to explore, experiment, and innovate.

As we step into the future, it’s clear that the quiet symphony of generative AI will continue to play a harmonious tune, enhancing the rhythm of the tech industry’s dance with progress.

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.

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.

Generative AI: Quietly Powering Innovation in Tech