Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(20)

Clean Tech(8)

Customer Journey(17)

Design(43)

Solar Industry(8)

User Experience(66)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(29)

Technology Modernization(7)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(38)

Insurtech(66)

Product Innovation(57)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(143)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(7)

Computer Vision(8)

Data Science(19)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(47)

Natural Language Processing(14)

expand Menu Filters

How is AI extending customer support during COVID-19 pandemic

4 minutes, 14 seconds read

With over 3 million confirmed cases of COVID-19 throughout the world and more than 200,000 deaths to date since the first report; coronavirus has spread wreaking havoc on any back-office operation, and more intensely on call centers throughout the globe.

For a couple of years now, organizations have only been theorizing the possibility of AI to enhance customer support. It was always a thing that could wait. However, now AI is proving to be a pressing matter over other priorities, and organizations are ready for widespread development than perhaps assumed.

Improved Customer Satisfaction

From banking to travel to finance; given reduced staffing and limited work-from-home options, the call center agents are overwhelmed by the influx of calls; for which the consumers are facing long latencies. These circumstances can, in turn, lead to a huge strain on the workforce and the industry as well. As businesses struggle to cover an increase in call volume, according to an old adage “necessity is the mother of invention.”, AI-enabled customer support has come to rescue. 

“People want what’s best for them, and they can switch on a dime because there’s always a new disruptor disrupting the last disruptor. So companies should just strive to keep changing and adapting to their customers’ needs.”

Ben Chestnut, Co-founder & CEO of MailChimp

AI has the capability of revolutionizing the relationship between a company and it’s clients. 64% of consumers and 80% of business buyers said that they want companies to interact with them in real-time. AI in customer support today can provide significant cost saving, triage calls on priority, volume elasticity, and meet customer expectation; that will eventually benefit the business in the long term.

Primary Concerns

Due to the pandemic outbreak and prolonged lockdown periods in several countries, businesses are forced to transition to work from home models. However, companies are not in favour of giving access to sensitive data to its employees outside the office premises. Along with privacy concerns, there are mobility concerns with the call center operations. Theoretically, technology can simplify mobility solutions. In a developing country like India, where only 2-3% of people use wired broadband and the majority of users rely on mobile data, uninterrupted internet connection is a real struggle.

“Now more than ever, customers need fast responses and AI and Automation can help”

Gadi Shamia, CEO of Replicant.

AI in Customer Support

Artificial intelligence in customer service is extremely useful to answer FAQs and resolve common customer support issues without the presence of a live agent. It can classify calls on the basis of options, business priorities and suggest solutions to the consumer according to their specific needs. Unlike the generation-old IVRs, the AI-enabled customer service, powered by NLP, shall understand the customer’s needs and allow him to converse as if he was speaking with a live agent. 

With the rising number of COVID-19 cases, customer queries at hospitals are increasing exponentially caused by high demand in consultation. To adapt to the situation, hospitals are turning to chatbots and virtual assistants. Here are some interesting use cases of AI in customer support bots.

Lili

Vozy’s Lili, is a conversational AI platform that provides customer assistance by alleviating pressure due to high call volume.

WHO Health Alert chatbot

The World Health Organization (WHO) has launched a dedicated messaging service, the WHO Health Alert chatbot to provide the latest news and information on COVID -19.

Read: How is technology helping to combat coronavirus pandemic?

Illinois

In partnership with Google AI, Quantiphi and Carahsoft created a 24/7 AI-enabled customer service bot, Illinois to provide immediate assistance to the filers with the FAQs.

Hitee

Hitee is the world’s first insurance specific chatbot solution. It allows integrating document processing workflows, ticket management systems, etc. to further simplify and automate customer support. Apart from 10x increasing customer interaction, Hitee also brought in new business leads and renewals for an eminent insurance company, Religare.

The crux

One fit for all is a myth now, even in customer support. AI-powered bots are proving to be revolutionary in customer support when it comes to customization of User Experience. Companies like Amazon, Starbucks and Netflix are implementing AI to track and analyse customer data and provide quick and easy resolutions to the customer problems. It also provides companies with deeper insights into the product based on demographic gender and various other factors.

AI-powered bots are capable of providing 24 X 7 customer support, more importantly after working hours and holidays. They prove to be not only cost-effective but also scalable throughout the enterprise. 

Customer support is the mainstay of any business. In these testing times, every call centre is under intense pressure due to the pandemic outbreak. Since customer expectations are higher than ever businesses are looking for advanced technological capabilities to bridge the gap. By adding AI-powered tools in customer support operations, businesses can not only improve customer experience but also have numerous business implications such as lower customer churn, higher revenues, less staff turnover and increased growth. If you need interfacing software for your specific business needs, please feel free to write to us at hello@mantralabsglobal.com.

Cancel

Knowledge thats worth delivered in your inbox

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

By :

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.

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot