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Insurance as a service

4 minutes, 26 seconds read

The past years have seen strong traction in “as a Service” business model across several industries. The insurance industry is no different. 

The idea behind XaaS, or “as a Service” is that one can buy services from vendors on a subscription-basis – depending on their needs and requirements. It is especially beneficial to reduce time to benefit, installation costs, ensure scalability and swift upgrades. XaaS often corresponds to the availability of service on the cloud.

[Read More: Everything as a Service]

Now, 

What is Insurance as a Service?

Insurance as a Service implies that individuals or companies can buy pre-built elements of Insurance services on subscription-basis as per their needs and requirements.

How is Insurance-as-a-Service different from Sandbox?

The Sandbox approach emphasizes on experimenting and learning before finally adopting technology or systems to reduce the impact of failure. Whereas Insurance as a Service is a platform built after testing done on a wide user base and is available for users on a subscription basis. Insurers use a sandbox approach to test product-market fit before the actual release. Individuals, corporates, and even insurance companies can benefit from Insurance as a Service.
Details – Sandbox Approach in Insurance

What makes Insurance as a Service model impressive?

Insurance as a Service model requires only a little to no capital expenditure. The service infrastructure, owned by the provider, distributes the cost across users. 

After studying business cases, primarily for incumbent processes, corporates and stakeholders can test a particular service before actually investing in it. Businesses need not overhaul their core functions for integrations. A small-scale trial can be enough to adopt a specific model. In many such ways, Insurance as a service is an excellent option for incumbents, entrepreneurs, and startups.

Prerequisites

XaaS products are, in general, scalable and can be integrated across a variety of platforms without compromising customization and customer experiences. Their infrastructure relies heavily on data, analytics and contextual tools. The fundamental requirements from Insurance as a Service infrastructure are:

1. Customer analytics

Why: Advanced analytical technologies are great to get an insight about customer psychology and implement them to create related products. 

How: NLP-powered chatbots can create a transparent platform for communication with customers and dive into the functional requirements of the product.

[Related:The State of AI Chatbots in Insurance Report]

2. Personalized data

Why: This is a high-time to humanize conversations with customers and establish a real-time personalized relationship.

How: Through the omnichannel approach, it is possible to gather and unify customer data collected from various sources like social media, website, communication with agents, to name some.

3. Contextual tools

Why: To formulate products that can match customer expectations, offer convenience and empathy-based experiences.

How: Leveraging analytics, emotion AI and NLP-based technologies to analyze customers’ intent and perceptions about your brand from multiple sources (e.g. social media, forums, etc.)

How are start-ups developing models for Insurance as a Service?

As per recent InsurTech developments, start-ups are pursuing the following 3 Insurance as a Service model:

1. Full-stack

It involves an end-to-end infrastructure to deploy digital insurance. Here, a technology company can develop a platform for Insurance processes as well as licensed white-label backend. For example, Swiss startup Stonestep provides Micro-insurance as a Service by partnering with mobile network operators, retailers, and vendors who already have an existing distribution presence. 

Working with partners helps them to save infrastructure costs and helps them to make insurance available for even the most remote geographical locations.

[Related: Four New Consumer-centric Business Models in Insurance]

2. Digitizing Process Assistance

Most of the incumbents still rely on legacy systems and processes for underwriting, policy distribution, claims, and agent onboarding. The Insurance-as-a-Service model also assists companies to digitize and channelize insurance operations in a single system and then connect them to their engine. Mantra Labs is a leading provider of InsurTech services and offers plug and play products for digital insurers such as:

Insurance Chatbot: An NLP-powered that works on a self-learning model and is updated from time to time based on the interactions between agents and customers. It brings unparalleled benefits in terms of ROI saving licensing and agent salaries costs.

Paper to digital document parser: Mantra Labs’ Intelligent Character Recognizer allows users to convert and store paper-based or handwritten documents into a digital format. 

Today we need situation-dependent personal risk management products. Insurers can remodel their offerings based on real-time scenarios which will not only urge the customer to invest in the insurance policies but also work towards improving their customers’ health and welfare. For instance, you may not have comprehensive auto insurance. But, how good it will be if your insurer provided theft insurance whenever you enter a theft-prone area? It is a win-win situation for both — the policyholder as well as provider.

3. Digitizing Core Services

Some startups offer their services in a specific field of insurance. For instance, Mantra Labs focuses on customer engagement, new revenue streams, and security features. Some companies like Riskpossible help with underwriting, RightIndem for claims, and others for customer data management and fraud detection. 

Because these companies focus on specific insurance domains they are much more efficient in making Insurance services a winner.

[Related: Visual AI Platform for Insurer Workflows]


Mantra Labs is an InsurTech100 firm specializing in AI-first products and solutions for the new-age digital Insurers. For your specific requirements, please feel free to drop a line at hello@mantralabsglobal.com.


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Smart Machines & Smarter Humans: AI in the Manufacturing Industry

We have all witnessed Industrial Revolutions reshape manufacturing, not just once, but multiple times throughout history. Yet perhaps “revolution” isn’t quite the right word. These were transitions, careful orchestrations of human adaptation, and technological advancement. From hand production to machine tools, from steam power to assembly lines, each transition proved something remarkable: as machines evolved, human capabilities expanded rather than diminished.

Take the First Industrial Revolution, where the shift from manual production to machinery didn’t replace craftsmen, it transformed them into skilled machine operators. The steam engine didn’t eliminate jobs; it created entirely new categories of work. When chemical manufacturing processes emerged, they didn’t displace workers; they birthed manufacturing job roles. With each advancement, the workforce didn’t shrink—it evolved, adapted, and ultimately thrived.

Today, we’re witnessing another manufacturing transformation on factory floors worldwide. But unlike the mechanical transformations of the past, this one is digital, driven by artificial intelligence(AI) working alongside human expertise. Just as our predecessors didn’t simply survive the mechanical revolution but mastered it, today’s workforce isn’t being replaced by AI in manufacturing,  they’re becoming AI conductors, orchestrating a symphony of smart machines, industrial IoT (IIoT), and intelligent automation that amplify human productivity in ways the steam engine’s inventors could never have imagined.

Let’s explore how this new breed of human-AI collaboration is reshaping manufacturing, making work not just smarter, but fundamentally more human. 

Tools and Techniques Enhancing Workforce Productivity

1. Augmented Reality: Bringing Instructions to Life

AI-powered augmented reality (AR) is revolutionizing assembly lines, equipment, and maintenance on factory floors. Imagine a technician troubleshooting complex machinery while wearing AR glasses that overlay real-time instructions. Microsoft HoloLens merges physical environments with AI-driven digital overlays, providing immersive step-by-step guidance. Meanwhile, PTC Vuforia’s AR solutions offer comprehensive real-time guidance and expert support by visualizing machine components and manufacturing processes. Ford’s AI-driven AR applications of HoloLens have cut design errors and improved assembly efficiency, making smart manufacturing more precise and faster.

2. Vision-Based Quality Control: Flawless Production Lines

Identifying minute defects on fast-moving production lines is nearly impossible for the human eye, but AI-driven computer vision systems are revolutionizing quality control in manufacturing. Landing AI customizes AI defect detection models to identify irregularities unique to a factory’s production environment, while Cognex’s high-speed image recognition solutions achieve up to 99.9% defect detection accuracy. With these AI-powered quality control tools, manufacturers have reduced inspection time by 70%, improving the overall product quality without halting production lines.

3. Digital Twins: Simulating the Factory in Real Time

Digital twins—virtual replicas of physical assets are transforming real-time monitoring and operational efficiency. Siemens MindSphere provides a cloud-based AI platform that connects factory equipment for real-time data analytics and actionable insights. GE Digital’s Predix enables predictive maintenance by simulating different scenarios to identify potential failures before they happen. By leveraging AI-driven digital twins, industries have reported a 20% reduction in downtime, with the global digital twin market projected to grow at a CAGR of 61.3% by 2028

4. Human-Machine Interfaces: Intuitive Control Panels

Traditional control panels are being replaced by intuitive AI-powered human-machine interfaces (HMIs) which simplify machine operations and predictive maintenance. Rockwell Automation’s FactoryTalk uses AI analytics to provide real-time performance analytics, allowing operators to anticipate machine malfunctions and optimize operations. Schneider Electric’s EcoStruxure incorporates predictive analytics to simplify maintenance schedules and improve decision-making.

5. Generative AI: Crafting Smarter Factory Layouts

Generative AI is transforming factory layout planning by turning it into a data-driven process. Autodesk Fusion 360 Generative Design evaluates thousands of layout configurations to determine the best possible arrangement based on production constraints. This allows manufacturers to visualize and select the most efficient setup, which has led to a 40% improvement in space utilization and a 25% reduction in material waste. By simulating layouts, manufacturers can boost productivity, efficiency and worker safety.

6. Wearable AI Devices: Hands-Free Assistance

Wearable AI devices are becoming essential tools for enhancing worker safety and efficiency on the factory floor. DAQRI smart helmets provide workers with real-time information and alerts, while RealWear HMT-1 offers voice-controlled access to data and maintenance instructions. These AI-integrated wearable devices are transforming the way workers interact with machinery, boosting productivity by 20% and reducing machine downtime by 25%.

7. Conversational AI: Simplifying Operations with Voice Commands

Conversational AI is simplifying factory operations with natural language processing (NLP), allowing workers to request updates, check machine status, and adjust schedules using voice commands. IBM Watson Assistant and AWS AI services make these interactions seamless by providing real-time insights. Factories have seen a reduction in response time for operational queries thanks to these tools, with IBM Watson helping streamline machine monitoring and decision-making processes.

Conclusion: The Future of Manufacturing Is Here

Every industrial revolution has sparked the same fear, machines will take over. But history tells a different story. With every technological leap, humans haven’t been replaced; they’ve adapted, evolved, and found new ways to work smarter. AI is no different. It’s not here to take over; it’s here to assist, making factories faster, safer, and more productive than ever.

From AR-powered guidance to AI-driven quality control, the factory floor is no longer just about machinery, it’s about collaboration between human expertise and intelligent systems. And at Mantra Labs, we’re diving deep into this transformation, helping businesses unlock the true potential of AI in manufacturing.

Want to see how AI-powered Augmented Reality is revolutionizing the manufacturing industry? Stay tuned for our next blog, where we’ll explore how AI in AR is reshaping assembly, troubleshooting, and worker training—one digital overlay at a time.

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