Revolutionizing Car Insurance with Microservices, High-Speed Risk Evaluation & Real-Time Decisioning through
Steady Rabbit’s Core-Flex Micro-GCC Model
Deliver instant, customer-first car insurance through real-time intelligence
Monolithic Node.js backend, PostgreSQL, limited event processing
Cuvva—a UK-based insurtech pioneer—set out to reinvent how customers purchase car insurance: flexible, instant, and radically user-centric. Their early traction proved demand for short-term insurance, yet the underlying system was limited: monolithic services, slow risk checks, and fragmented fraud-detection workflows. Enterprise partners and regulatory bodies demanded real-time underwriting, data integrity, and audit-ready compliance, all while maintaining sub-second customer experience.
Steady Rabbit deployed a Core-Flex Micro-GCC squad that, in record time, engineered:
This transformation enabled Cuvva to roll out new insurance products 3× faster, meet stringent UK data-compliance mandates, and achieve a competitive edge in the digital insurance ecosystem.
UK-based InsurTech company
2014
Deliver instant, customer-first car insurance through real-time intelligence
Backed by leading European VC firms
UK-wide coverage with active mobile user base
Monolithic Node.js backend, PostgreSQL, limited event processing
Need for real-time underwriting, stronger fraud detection, and faster product rollout
Cuvva’s ambition to lead the short-term insurance market required a future-ready technology foundation. Their legacy system lacked modularity, creating bottlenecks in quote generation, risk evaluation, and regulatory compliance.
To scale sustainably, they needed a microservices-based insurance engine capable of absorbing large traffic bursts, supporting new underwriting rules, and ensuring real-time data access across services.
Cuvva needed a system capable of processing thousands of real-time events per minute, ensuring instant decisions with complete transparency and traceability.
Micro-GCC Squad Blueprint
The outcome of Sprint 0: signed-off domain models, microservice boundaries, fraud timeline indexing strategy, and 88 SP/sprint velocity baseline.
During a critical peak-hour load test in Sprint 7, the Flex Data Specialist jumped in to re-index the fraud timeline model, improving search speeds by 4×—preventing a potential release delay.
65% faster risk evaluation → near-instant insurance decisions
Real-time policy checks reduced onboarding friction by 40%
Fraud detection accuracy improved by 2.7× with multi-index intelligence
99.97% uptime across underwriting and policy services
60% improved latency in quote generation
Compliance automation cut audit workload by 70%
New product rollout became 3× faster due to modular architecture
Regulatory reporting accuracy improved with full event traceability
Achieved significant competitive advantage in a fast-growing UK insurtech market
Ensured specialists (fraud indexing, compliance, scaling) were available within 48 hours.
And Plan-Left Gates eliminated rework by 38%.
Maintained sprint variance under 3%.
Across underwriting, compliance, and real-time event processing.
KPIs like latency, fraud accuracy, and rollout speed tied to team incentives.
weekly steering, live dashboards, zero surprises.
Steady Rabbit rebuilt our insurance engine into a real-time, ultra-scalable system. Their microservices approach transformed our quoting, policy checks, and fraud detection. We now move faster, scale easier, and deliver a customer experience we’re proud of.
