67% of consumers worldwide used a chatbot for customer support in the past year, a report from Invesp in 2023 suggests. Conversational AI and Enhanced Customer Experience have become almost synonymous and complementary to each other. By bringing round-the-clock service, personalized support, and instant resolution to the table, Conversational AI has redefined the consumer industry landscape.

Emergence of Conversational AI

Conversational AI is a sophisticated technology that facilitates human-like interaction through machines. This realm of AI includes but isn’t limited to:

Chatbots
Voice assistants
AI-powered messaging applications

Conversational AI has wide range of applications across consumer industries

Working Mechanism

Relying on Machine Learning, Natural Language Processing (NLP), and complex AI algorithms, these technologies accurately interpret human language, understand the context, and deliver fitting responses.

Conversational AI: A Customer Experience Game-Changer

Impact on Customer Experience

Embedding Conversational AI and Enhanced Customer Experience can lead to a 25% elevation in operational efficiency by 2025 (Gartner). This technological leap allows businesses to cater to the evolving expectations of customers who prefer immediate and personalized service.

Case Study: ICICI Bank’s Leap Towards AI

Taking a step towards AI, ICICI Bank, India, launched a voice-based AI assistant to help customers with banking transactions and services. The AI assistant significantly reduced service delivery time and eased the burden of customer service representatives. It impressively handled over 7.2 million queries in its first year, demonstrating AI’s potential in managing large-scale customer interactions.

Conversational AI: Setting New Standards in Customer Service

Case Study: Myntra’s FashionGPT

Fashion e-commerce giant, Myntra, entered the Conversational AI space with the innovative MyFashionGPT. Designed to answer fashion-related queries, it created a personalized shopping experience for customers.

Case Study: Mantra Lab’s Hitee Chatbot

Tech innovation firm Mantra Labs transformed customer service in the healthcare sector with their Hitee Chatbot. Designed to answer queries related to insurance claims, appointments, and healthcare services, Hitee has significantly improved service delivery time and customer satisfaction. The chatbot helped the company reduce their response time by 60%, highlighting the efficiency that Conversational AI can bring to customer service.

Personalization: The Key to Enhanced Customer Experience

Emphasizing Individuality with AI

Conversational AI is not just about addressing customer queries, it’s about understanding each customer’s unique needs. By using machine learning algorithms and large datasets, AI can tailor responses based on customer’s previous interactions, ensuring a truly personalized experience.

Case Study: Spotify’s AI Recommendation System

Take Spotify for instance. While it’s not a conventional chatbot, it leverages the power of Conversational AI to understand user preferences and recommend music. As a result, it creates a unique, individualized experience for its millions of users.

Conversational AI: Beyond Customer Service

Expansion to Other Sectors

While Conversational AI has largely been utilized in customer service, it’s potential goes beyond. Industries from healthcare to finance are harnessing the power of AI to streamline operations and improve user experience.

Case Study: Ada Health’s AI-Powered Symptom Checker

Ada Health, a global health company, has developed an AI-powered symptom checker that interacts with users to understand their health issues and provide possible diagnoses. It serves as a primary example of how Conversational AI can enhance user experience beyond traditional customer service.

Addressing Challenges and Ethical Considerations

Privacy and Security

As AI becomes more integrated into our lives, concerns around privacy and security grow. Businesses leveraging Conversational AI must ensure robust security measures to protect sensitive customer information.

Building Trust

For AI to be successful, businesses must also build trust with customers. Transparency around data usage can help build this trust and ensure customers feel comfortable interacting with AI.

Investment Trends and Future Potential

Companies across the globe are ramping up their investments in Conversational AI to stay ahead of the curve. Global spending on Conversational AI is projected to reach $5.5 billion by 2024, a staggering growth from $3 billion in 2019 (MarketWatch).

Mantra Labs, a frontrunner in this area, is investing heavily in Conversational AI to develop innovative solutions that enhance customer experiences. Their work is reflective of a larger global trend as more companies recognize the potential of Conversational AI and Enhanced Customer Experience.

Looking ahead, the consumer industry can anticipate a future dominated by more sophisticated AI tools that can understand complex queries, comprehend different languages, and offer even more personalized solutions. Conversational AI is not merely a fleeting trend but a fundamental shift in how businesses connect with their customers. The future of customer experience is here, and it’s automated, instant, and intelligent.

Conclusion

With its potential to deliver personalized, efficient, and round-the-clock customer service, Conversational AI is truly revolutionizing the consumer industry. However, as with any technology, businesses must be aware of and address potential challenges, particularly around privacy and trust. The future of Conversational AI in customer experience is bright, and it’s just the beginning of what’s to come.

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

By : Sampada

Published on Nov 13, 2024

App development Platform Engineering

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.

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