Microsoft Financial Services United States

Nsure.com

Customer Service Automation · Document Processing · Enterprise Knowledge Search

Most recently, it has used the generative AI capabilities in Power Automate, including text recognition and sentiment analysis, to reduce manual processing time by 60% while also reducing associated costs by 50%.

Even with extensive automation in place, more than 100 customer representatives were processing over 100,000 customers interactions each month through calls...
From 2020-2023, Nsure's revenue grew at a Compound Annual Growth Rate (CAGR) of over 100%.
This, in turn, has reduced associated costs by 50%
This, in turn, has reduced associated costs by 50% and led to a significant improvement in customer satisfaction, resulting in a 4.7 (out of 5) star rating...

Reported impact

Business outcome

The results were impressive: thanks to AI-driven automation through these new Power Platform tools, Nsure.com customer service representatives reduced time spent on manual tasks by more than 60%, allowing them to focus on complex escalations and building customer relationships.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth
  • Risk Reduction

Solution

Nsure.com used Microsoft Fabric, Power BI, Azure AI Foundry to replace disconnected manual workflows with a more consistent digital operating model. Low-code automation and copilot capabilities orchestrate tasks, route work, and surface context to agents or business users without requiring separate point solutions. The results were impressive: thanks to AI-driven automation through these new Power Platform tools, Nsure.com customer service representatives reduced time spent on manual tasks by more than 60%, allowing them to focus on complex escalations and building customer relationships. The architecture emphasizes managed services, reusable integrations, and governed data handling so the organization can scale adoption without rebuilding the solution for each new team or use case.

Data flow

Business data is unified in the service data layer, workflow automation coordinates the workflow across connected systems, and Microsoft Copilot Studio surfaces the experience to employees or customers in a single guided interface.

Solution architecture

5 components · 4 layers
  1. Compute
    • Microsoft Copilot Studio Builds conversational agents and copilots with enterprise integrations and controls.
  2. Storage
    • Microsoft Fabric Unifies lake, warehouse, analytics, and BI workloads on a single managed data platform.
  3. Serving
    • Power BI Delivers dashboards, reports, and self-service analytics to business users.
  4. Orchestration
    • Azure AI Foundry Provides the managed environment for model orchestration, experimentation, and responsible AI operations.
    • Power Platform Provides the low-code automation and application platform for workflow modernization.

Architecture clues

  • 20 data sources
  • Consolidates analytics and BI workloads on Microsoft Fabric as a shared data foundation.
  • Implements enterprise copilots or agents with Copilot Studio guardrails and integrations.

Evidence from the source

"Automation using Power Platform has allowed us to grow rapidly, while maintaining high quality of service.
And that's when the company began using Microsoft Power Platform to automate business processes.
For over five years, the company has been using Power Platform to automate business processes.
Most recently, it has used the generative AI capabilities in Power Automate, including text recognition and sentiment analysis, to reduce manual processing time by 60% while also reducing associated costs by 50%.
The results were impressive: thanks to AI-driven automation through these new Power Platform tools, Nsure.com customer service representatives reduced time spent on manual tasks by more than 60%, allowing them to focus on complex...
Transparency Evidence & methodology 95/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    4 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Microsoft

    Nsure.com customer story

    First-party
    https://www.microsoft.com/en/customers/story/1728829430186194098-nsure-power-platform-insurance-usa
    Source type
    Vendor-published customer story
    Published
    Jan 25, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Jan 25, 2024 · Microsoft · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.