Optimized Production Support for Insurtech Scale

A leading SaaS insurtech provider needed to scale production support across roughly 40 client implementations while improving efficiency and quality. Myridius delivered a coordinated support model combining L2 service delivery, product configuration, and QA discipline, with MS Copilot assisting JavaScript updates, improving code quality, optimizing cost, and strengthening client relationships.

Key Outcomes

• Improved code quality and performance through optimized revisions.
• Lower operating cost by extending support beyond the internal model.
• Enhanced service quality and stronger client relationships.

Overview

A leading SaaS-based insurtech provider needed to scale production support across approximately 40 client implementations while improving system efficiency and delivery quality. The status quo depended on maintenance, enhancement, and defect remediation under strict SLA expectations, while the desired state called for a more efficient operating model that could reduce cost, improve quality, and strengthen client relationships. Myridius delivered a coordinated production support model combining L2 service delivery, product configuration, and QA discipline, leveraging MS Copilot to assist with JavaScript updates. As a result, the provider improved code quality and performance, lowered operating cost by extending support beyond its internal model, and enhanced service quality and client relationships through SLA-aligned delivery.

Client Context

The client is a leading SaaS-based insurtech provider supporting roughly 40 client implementations on an Origami-based platform.

A scalable support model mattered here because the provider had to deliver maintenance, enhancement, and defect remediation under strict SLAs across many implementations. What was at stake was the ability to reduce cost, improve delivery quality, and strengthen client relationships without overextending the internal team.

The Challenge

The provider needed to scale production support across roughly 40 client implementations while improving efficiency and delivery quality. The status quo depended on maintenance, enhancement, and defect remediation under strict SLAs, while the desired state called for a more efficient operating model that could reduce cost, improve quality, and strengthen client relationships.

Consider supporting 40 implementations at once. Each carried SLA expectations for maintenance, enhancements, and defect remediation, straining an internal-only model. The provider needed a coordinated, scalable approach that improved quality while controlling cost.

Transformation Goals

The engagement focused on north stars that connected support scalability to quality uplift and cost efficiency.

• Support Scalability: Orchestrate repeatable L2 support and enhancement delivery across the client base.

• Quality Uplift: Improve product configuration, testing rigor, and defect remediation outcomes.

• Cost Efficiency: Optimize support delivery with stronger automation and AI-assisted coding practices.

The Solution

The engagement delivered a coordinated production support model combining service delivery, configuration maintenance, and QA discipline. Myridius orchestrated the L2 support foundation, embedded configuration and QA discipline into delivery, and reimagined support efficiency with AI assistance. The progression moved from deploying L2 service delivery, to embedding product configuration and QA discipline, to reimagining efficiency through AI-assisted coding.

• Orchestrated the foundation: Provided production support and client enhancements during standard US business hours across the implementation base.

• Embedded intelligence into the journey: Managed product configuration including policy forms and rates maintenance, and executed manual and automated testing including functional, smoke, and regression testing.

• Reimagined the operating model: Leveraged MS Copilot to assist with JavaScript updates, improving coding efficiency and quality.

Governance and Trust

Because this engagement delivered SLA-bound support across many insurtech implementations, quality discipline and SLA alignment were central. Manual and automated testing, including functional, smoke, and regression testing, established consistent quality control across enhancements and defect remediation rather than ad hoc fixes.

Careful product configuration, including policy forms and rates maintenance, ensured changes were applied accurately and consistently, while AI-assisted JavaScript updates through MS Copilot improved code quality. SLA-aligned delivery provided the accountability and predictability that strengthened client relationships across the base of roughly 40 implementations.

Results

The engagement transformed a strained, internal-only support model into a coordinated, scalable, quality-focused capability. The result was better code quality, lower cost, and stronger relationships.

The result:

• Improved code quality and performance through optimized revisions and issue identification.

• Lowered operating cost by extending support beyond the insurtech's internal model.

• Enhanced service quality and strengthened client relationships through SLA-aligned delivery.

Technology Stack

Core Platform

Origami-based insurtech platform
The platform under production support

Managed Services

L2 support, client enhancements, product configuration, policy forms and rates maintenance
Maintain and improve the platform across implementations

Quality Engineering

Manual, automated, functional, smoke, and regression testing
Ensure consistent quality and defect remediation

AI and Developer Acceleration

MS Copilot, JavaScript
Assist and accelerate coding updates

For a SaaS insurtech provider, scaling support across many implementations strains an internal-only model. This case shows how coordinated, AI-assisted support turns scale into a quality and cost advantage. This was not staff augmentation. It was a coordinated support model with QA discipline and AI-assisted coding.