Concirrus

Concirrus Inspire is an AI-native underwriting platform designed for specialty insurers who need to increase speed without compromising decision quality.

The platform brings together submissions, risk signals, and portfolio exposure into a unified decision environment, enabling underwriters to act quickly while maintaining full visibility of risk impact.

From accelerating quote turnaround to strengthening portfolio confidence, Concirrus empowers teams to write more profitable business, scale sustainably, and stay ahead in a competitive market.

Quest Cargo

Designing a data-driven platform for risk assessment in cargo insurance.

Overview

Quest Cargo is a SaaS platform designed for underwriters and brokers to support more informed decision-making when assessing cargo insurance risk. The product aggregates market data and proprietary customer datasets, enabling users to gain deeper insights into portfolios, exposure, and claims.

The goal was to transform a traditionally manual, fragmented process into a scalable, data-driven product experience.

My Role

Lead Product Designer.

Responsibilities included:

  • Defining UX strategy and product design direction for the new and existing products

  • Establishing design systems, brand guidelines, and reusable components

  • Facilitating workshops and discovery sessions

  • Conducting research with internal stakeholders and SMEs

  • Designing data-heavy interfaces and visualisations

  • Collaborating closely with product, engineering, and data science teams

  • Defining and tracking product success metrics

Team Structure

The team operated as a cross-functional product squad, consisting of:

  • Product Owner

  • Product Designer (my role)

  • Subject Matter Experts (SMEs) with insurance domain expertise

  • Frontend and Backend Engineers

  • QA specialists

  • Data Science team (close collaboration)

This structure allowed us to balance technical feasibility, domain expertise, and user needs throughout the product lifecycle.

Understanding the Challenge

The problem space was shaped by several complexities:

  • Advanced risk modelling logic requiring close collaboration with data science

  • Predominantly manual and fragmented underwriting workflows

  • Limited access to direct user behaviour and usage data

  • Restricted access to end users due to sensitive client relationships

To mitigate this, the company embedded domain experts (experienced underwriters) into the product team, enabling indirect but high-quality user insight.

Additionally, the product needed to be modular and scalable, allowing adaptation to clients with non-standard processes and supporting future growth.

Key Constraints

  • Data complexity: large, unstructured datasets requiring clear visualisation

  • Limited user access: reliance on SMEs and proxy research

  • Business pressure: need to deliver value quickly to support sales

  • Scalability requirements: product architecture needed to support rapid expansion

  • Domain specificity: highly specialised user needs and workflows

Product Strategy & Foundations

To establish a strong foundation, I introduced:

  • A UX strategy aligned with business and product goals

  • A design system and shared component library to ensure consistency and scalability

  • Product metrics framework to measure success across usability and business performance

  • Core artefacts such as journey maps and interaction patterns

We prioritised building foundational modules around:

  • policy management

  • accounts

  • claims

These core features enabled early value delivery while setting the groundwork for future expansion.

Research & Discovery

Due to limited direct access to users, we relied on:

  • SME-led interviews with internal and external underwriters

  • Workshops to synthesise domain knowledge

  • Continuous collaboration with customer-facing teams

Through this process, we identified two primary personas:

  • Senior Underwriters (focused on execution and decision-making)

  • Underwriting Managers (focused on oversight and portfolio performance)

This helped us build shared empathy across the organisation and align product decisions with real user needs.

Problem Definition

Key problem statements included:

  • Underwriters lacked centralised access to critical risk data

  • Data fragmentation prevented a holistic view of exposure and claims

  • Existing tools did not support efficient portfolio analysis

  • Decision-making was slowed by manual processes and poor data visibility

We used “How Might We” workshops to break down these challenges and explore targeted solutions.

Data Modelling & Visualisation

A significant part of the product focused on translating complex data into actionable insights.

We collaborated closely with the data science team through workshops to:

  • understand underlying risk models

  • define meaningful data representations

  • design intuitive visualisations for complex datasets

This ensured that the product delivered insight, not just data.

Design & Validation

The design process was highly iterative and collaborative:

Internal Validation

I facilitated usability testing sessions using wireframes and high-fidelity prototypes made in Axure to reduce usability risks early.

External Validation

High-fidelity prototypes were used by SMEs to gather feedback from prospective clients, allowing us to validate assumptions before development.

MVP Delivery

The MVP required continuous reprioritisation and scope adjustments due to competing demands between:

  • delivery capacity

  • UX strategy

  • business need for rapid market entry

Despite multiple pivots and scope reductions, we successfully:

  • delivered a functional product

  • onboarded the first client, validating the product direction

Post-MVP & Continuous Discovery

Following launch, we continued to:

  • iterate based on client feedback

  • expand into additional product streams

  • refine workflows and usability

Discovery became an ongoing process, embedded into the product lifecycle.

Analytics & Measurement

We defined a multi-layered metrics framework to evaluate product success:

User Experience Metrics

  • Satisfaction (NPS, surveys, support feedback)

  • Efficiency (task completion, activity ratios: DAU/WAU/MAU)

  • Effectiveness (error rates, task success)

Business Metrics

  • Revenue per customer

  • Customer acquisition cost (CAC)

  • Customer lifetime value (LTV)

  • Churn and upsell rates

This ensured alignment between user value and business outcomes.

Product Enablement

To support onboarding and usability, we introduced:

  • In-product guides for complex workflows

  • Supporting knowledge base content

  • Targeted guidance for high-value user journeys

This reduced friction and improved adoption across clients.

Impact

The product achieved several key outcomes:

  • Successful onboarding and integration with enterprise clients

  • Improved data accessibility and decision-making for underwriters

  • Increased engagement with clients during discovery and iteration

Established a scalable foundation for future product expansion