When an InsurTech unicorn scales rapidly, its internal infrastructure has to keep pace. Zego, a UK leader in flexible commercial motor insurance, reached a moment where its growth began outstripping its core tools. To clear these operational bottlenecks, they partnered with STX Next on a multi-track data and engineering project, including:
Every single workstream resulted in a stable production system that powers Zego’s day-to-day business and future-readiness.
Founded in 2016, Zego built a reputation as an agile, engineering-led insurance company. By designing flexible policies for rideshare drivers, delivery couriers, and fleet operators, they quickly climbed to unicorn status.
Zego has always invested heavily in modern software solutions and AI-powered tools. Recently, they even stepped into broader financial services by launching a strategic credit product. But behind this rapid public success, their internal engineering systems were struggling to keep up with the sheer volume of the business.
To sustain its growth, Zego had to confront three infrastructure challenges:
The internal application used by Zego’s customer agents was already on its third iteration. While previous teams had successfully shipped versions of the tool, they couldn’t deliver the combination of clean design, underlying code quality, and long-term extensibility the business required. Meanwhile, support agents were stuck using it all day. As the Zego team candidly put it:
“Imagine our agents having to grind on that thing for 8 hours a day!”
Through its sister entity Extracover Limited, Zego held millions of critical policy, customer, and vehicle records. Unfortunately, this data was scattered across multiple databases buried inside a legacy monolithic architecture. Data formats were highly inconsistent, making normalization a headache, reporting unreliable, and downstream data consumption frustratingly slow.
The Claims team relied on manual workflows built around Salesforce. As claims volumes increased, this setup made it incredibly difficult to validate customer policies in real time during an incident, catch fraud automatically, or send fast updates to drivers and gig platform partners.
To address these challenges, Zego needed a partner with expertise in software engineering, data engineering, and cloud computing, as well as rigorous project management. The company was looking for a vendor with experience in the insurance industry who understood its specifics.
STX Next stood out for several reasons. Beyond a 20-year track record in Python and software engineering, data engineering, and cloud solutions, the team brought deep, practical experience solving niche insurance problems like claims processing, regulatory integrations, and policy data normalization. Additionally, an AI-first approach to software development was a key factor for Zego when selecting a vendor, and STX Next also met this requirement.
The joint engineering effort was divided into three tracks, each targeting a specific operational bottleneck.
STX Next led the charge of Zego’s internal employee tool, opting for a clean slate on the frontend using React.js, while extending Zego’s existing Python and FastAPI services on the backend. To ensure the long-term flexibility that previous versions lacked, the team implemented gRPC and protobuf for robust service-to-service communication.
During this track, the team successfully delivered:
A standout element of this track was the introduction of AI coding tools like Cursor AI and Claude Code into the daily workflow. By setting up the right guardrails and architecture, almost any engineer at Zego can now safely extend the platform—and several non-technical team members have even shipped smaller features themselves.
For the Claims team, STX Next developed and optimized Python and Django microservices to enable real-time claims handling. The new system automatically validates policies the moment an incident is reported, triggers automated fraud detection logic, and keeps everyone informed.
To make this happen, the team built out vital integrations:
Behind the scenes, Apache Airflow pipelines were built on AWS to orchestrate data flows and feed fraud detection algorithms, utilizing Snowflake as the analytical data store. The result is a secure, highly intuitive interface designed completely around how the claims team actually works.
To unlock the value of Extracover Limited's data, STX Next systematically migrated millions of policy records out of the legacy monolith.
The new data architecture includes:
Today, the systems built during this partnership are fully live, transforming how Zego manages its data, supports its agents, and launches new financial products.
The new portal replaced what the team previously remembered as a UX nightmare. Agents immediately embraced it, pointing to the modern layout and the snappy responsiveness of the system.
Furthermore, because the architecture was built right, Zego’s highly strategic new credit product launched with a smooth agent workflow already baked in on day one.
What began as a mission to solve three pressing operational bottlenecks has evolved into a lasting engineering partnership. With the foundations of the new employee platform, automated claims processing, and the Snowflake data warehouse firmly in place, Zego is primed for its next phase of market growth.
The collaboration is already moving forward into its next phase, focusing on deeper AI-powered features inside the employee portal and systematically migrating remaining legacy systems. Zego has given the engineering team the creative runway to push AI adoption to its limits, ensuring their technology remains as flexible as the insurance products they sell.
From the engineering side, the verdict on the partnership is straightforward: "Zego, as a unicorn, has built a proven track record over the years. It's the perfect place to build serious products while working with a seriously great team."
Schedule a call with our experts to discover the benefits of partnering with STX Next.
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