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

Cognitive Automation and Its Importance for Enterprises

One of Japan’s leading insurance firms — Fukoku Mutual Life Insurance claims to have replaced 34 human tasks with IBM Watson (AI technology).

Cognitive automation is a subset of artificial intelligence that uses advanced technologies like natural language processing, emotion recognition, data mining, and cognitive reasoning to emulate human intelligence. In simple words, cognitive automation uses technology to solve problems with human intelligence.

Cognitive automation vs Robotic Process Automation

The main pillars of cognitive automation

Consider an automated home security system programmed to function based on millions of decisions. It may still encounter situations when it does not know what to do. Machines can make logical decisions in many unforeseen situations using cognitive neuroscience. 

The technologies to make cognition-based decisions possible include natural language processing, text analytics, data mining, machine learning, semantic analytics, and more. The following table gives an overview of the technologies used in cognitive automation.

TECHNOLOGYDESCRIPTION
Machine LearningIt involves improving a system’s performance by learning through real-time interactions and without the need for explicitly programmed instructions.
Data MiningIt is the process of finding meaningful correlations, patterns, and trends from data warehouses/repositories using statistical and mathematical techniques.
Natural Language ProcessingNLP is a computer’s ability to communicate with humans in native languages. 
Cognitive ReasoningIt is the process of imitating human reasoning by engaging in complex content and natural dialogues with people.
Voice RecognitionIt is transcribing human voice and speech and translating it into text or commands.
Optical Character RecognitionIt uses pattern matching to convert scanned documents into corresponding computer text in real-time.
Emotion RecognitionIt is the understanding of a person’s emotional state during voice and text-based interactions.
Recommendation EngineIt is a framework for providing insights/recommendations based on different data components and analytics. For instance, Amazon was one of the first sites to use recommendation engines to make suggestions based on past browsing history and purchases.

Why is cognitive process automation important for enterprises?

Cognitive automation improves the efficiency and quality of computer-generated responses. In fact, cognitive processes are overtaking nearly 20% of service desk interactions. The following factors make cognitive automation next big enhancement for enterprise-level operations –

  1. Cost-effective: Cognitive automation can help companies to save up to 50% of their total spending for FTE, and other related costs.
  2. Operational Efficiency: Automation can enhance employee productivity, leading to better operational efficiency.
  3. Increased accuracy: Such systems are able to derive meaningful predictions from a vast repository of structured and unstructured data with impeccable accuracy. 
  4. Facts-based decision making: Strategic business decisions drill down to facts and experiences. Combining both, cognitive systems offer next level competencies for strategic decision making.
4 benefits of cognitive automation for enterprises

Also read – Cognitive approach vs digital approach in Insurance

Applications of cognitive automation

End-to-end customer service

Enterprises can understand their customer journey and identify the interactions where automation can help. For example, Religare — a leading health insurance company incorporated NLP-powered chatbot into their operations and automated their customer-support and achieved almost 80% FTE savings. Processes like policy renewal, customer query ticket management, handling general customer queries at scale, etc. are possible for the company through chatbots.

Processing transactions

Reconciliation is a tedious yet crucial transaction process. Banking and financial institutions spend enormous time and resources on the process. Paper-based transactions, different time zones, etc. add to the complicacy of settling transactions. With human-like decision-making capabilities, cognitive automation holds a huge prospect of simplifying the transaction-related processes.

Claims processing

In insurance, claims settlement is a huge challenge as it involves reviewing policy documents, coverage, the validity of insured components, fraud analytics, and more. Cognitive systems allow making automated decisions in seconds by analyzing all the claims parameters in real-time.

Also read – How intelligent systems can settle claims in less than 5 minutes

Requirements

Deloitte’s report on how robotics and cognitive automation will transform the insurance industry states that soon, automation will replace 22.7 million jobs and create 13.6 million new jobs. However, not all operations can be automated. The following are the requirements for successfully automating processes.

  1. Input sources: The input sources should be machine-readable, or needs to be converted into one. Also, there’s a limitation to the number of sources that the system can process for decision making. For instance, in an email management process, you cannot automate the resolution of every individual email. 
  2. Availability of the technology: Cognitive automation combines several technologies like machine learning, natural language processing, analytics, etc. Thus, all the technologies should be available to make automated processes functional. 
  3. Data availability: For the cognitive system to make accurate decisions, there should be sufficient data for modeling purposes.
  4. Risk factor: Processes like underwriting and data reconciliation are good prospects of cognitive automation. However, based on the risk value and practical aspects, human intervention may be required to make the final decision.
  5. Transparency & control: Cognitive automation is still in a nascent stage and humans may overturn machine-made decisions. Therefore, the system design needs to adhere to transparency and control guidelines.

Wrapping up

Cognitive systems are great for deriving meaningful conclusions from unstructured data. Many back and front office operations can be automated for improving efficiency, especially in consumer-facing functions to understand requirements and feedback. For instance, cognitive automation comes with powerful emotion recognition capabilities. It can help with making sense of customer tweets, social updates, through face recognition and analyzing texts. 

Since cognitive automation solutions help enterprises to adapt quickly and respond to new information and insights, it is becoming crucial for customer-centric businesses. The following graph shows how important cognitive technology adoption is for businesses that focus on consumer centricity.

Customer centricity and cognitive technology adoption
Source: Deloitte

Also read – 5 Front office operations you can improve with AI

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