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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.


Further Reading:

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Smart Manufacturing Dashboards: A Real-Time Guide for Data-Driven Ops

Smart Manufacturing starts with real-time visibility.

Manufacturing companies today generate data by the second through sensors, machines, ERP systems, and MES platforms. But without real-time insights, even the most advanced production lines are essentially flying blind.

Manufacturers are implementing real-time dashboards that serve as control towers for their daily operations, enabling them to shift from reactive to proactive decision-making. These tools are essential to the evolution of Smart Manufacturing, where connected systems, automation, and intelligent analytics come together to drive measurable impact.

Data is available, but what’s missing is timely action.

For many plant leaders and COOs, one challenge persists: operational data is dispersed throughout systems, delayed, or hidden in spreadsheets. And this delay turns into a liability.

Real-time dashboards help uncover critical answers:

  • What caused downtime during last night’s shift?
  • Was there a delay in maintenance response?
  • Did a specific inventory threshold trigger a quality issue?

By converting raw inputs into real-time manufacturing analytics, dashboards make operational intelligence accessible to operators, supervisors, and leadership alike, enabling teams to anticipate problems rather than react to them.

1. Why Static Reports Fall Short

  • Reports often arrive late—after downtime, delays, or defects have occurred.
  • Disconnected data across ERP, MES, and sensors limits cross-functional insights.
  • Static formats lack embedded logic for proactive decision support.

2. What Real-Time Dashboards Enable

Line performance and downtime trends
Track OEE in real time and identify underperforming lines.

Predictive maintenance alerts
Utilize historical and sensor data to identify potential part failures in advance.

Inventory heat maps & reorder thresholds
Anticipate stockouts or overstocks based on dynamic reorder points.

Quality metrics linked to operator actions
Isolate shifts or procedures correlated with spikes in defects or rework.

These insights allow production teams to drive day-to-day operations in line with Smart Manufacturing principles.

3. Dashboards That Drive Action

Role-based dashboards
Dashboards can be configured for machine operators, shift supervisors, and plant managers, each with a tailored view of KPIs.

Embedded alerts and nudges
Real-time prompts, like “Line 4 below efficiency threshold for 15+ minutes,” reduce response times and minimize disruptions.

Cross-functional drill-downs
Teams can identify root causes more quickly because users can move from plant-wide overviews to detailed machine-level data in seconds.

4. What Powers These Dashboards

Data lakehouse integration
Unified access to ERP, MES, IoT sensor, and QA systems—ensuring reliable and timely manufacturing analytics.

ETL pipelines
Real-time data ingestion from high-frequency sources with minimal latency.

Visualization tools
Custom builds using Power BI, or customized solutions designed for frontline usability and operational impact.

Smart Manufacturing in Action: Reducing Market Response Time from 48 Hours to 30 Minutes

Mantra Labs partnered with a North American die-casting manufacturer to unify its operational data into a real-time dashboard. Fragmented data, manual reporting, delayed pricing decisions, and inconsistent data quality hindered operational efficiency and strategic decision-making.

Tech Enablement:

  • Centralized Data Hub with real-time access to critical business insights.
  • Automated report generation with data ingestion and processing.
  • Accurate price modeling with real-time visibility into metal price trends, cost impacts, and customer-specific pricing scenarios. 
  • Proactive market analysis with intuitive Power BI dashboards and reports.

Business Outcomes:

  • Faster response to machine alerts
  • Quality incidents traced to specific operator workflows
  • 4X faster access to insights led to improved inventory optimization.

As this case shows, real-time dashboards are not just operational tools—they’re strategic enablers. 

(Learn More: Powering the Future of Metal Manufacturing with Data Engineering)

Key Takeaways: Smart Manufacturing Dashboards at a Glance

AspectWhat You Should Know
1. Why Static Reports Fall ShortDelayed insights after issues occur
Disconnected systems (ERP, MES, sensors)
No real-time alerts or embedded decision logic
2. What Real-Time Dashboards EnableTrack OEE and downtime in real-time
Predictive maintenance using sensor data
Dynamic inventory heat maps
Quality linked to operators
3. Dashboards That Drive ActionRole-based views (operator to CEO)
Embedded alerts like “Line 4 down for 15+ mins”
Drilldowns from plant-level to machine-level
4. What Powers These DashboardsUnified Data Lakehouse (ERP + IoT + MES)
Real-time ETL pipelines
Power BI or custom dashboards built for frontline usability

Conclusion

Smart Manufacturing dashboards aren’t just analytics tools—they’re productivity engines. Dashboards that deliver real-time insight empower frontline teams to make faster, better decisions—whether it’s adjusting production schedules, triggering preventive maintenance, or responding to inventory fluctuations.

Explore how Mantra Labs can help you unlock operations intelligence that’s actually usable.

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