Taking a real-time fraud intelligence platform from mock data to production with one embedded engineer
Retail Cache is a London-based fraud intelligence platform that helps retailers and carriers detect, prevent and stop return and refund fraud across every order, claim and return in real time, cross-referencing data between participating retailers to catch repeat patterns no single retailer could see alone.
Digital Back Office gave us exactly what we needed at our stage, a strong engineer who took our platform from prototype to production. Fraud detection, real APIs, cloud infrastructure, all handled end to end by one person. Working across UK and India was seamless, quick decisions, no overhead. What stood out was having someone who thought about the product, not just the code.
Problem
Retail Cache had a basic data pipeline handling claims and orders, and a web app running entirely on mock data. The foundations were there, but turning it into a platform that could onboard real retail partners meant rebuilding it layer by layer, with real production work needed across every layer of the stack.
Solution
We extended the data pipeline, built the real GraphQL APIs, implemented fraud signal detection and graph-based network analysis, deployed the cloud infrastructure and delivered a fully functional analyst dashboard connected to live data, all through a single embedded engineer.
Results
Retail Cache now has a complete data pipeline, real-time fraud detection, graph-based network analysis and a full analyst dashboard, built by one dedicated engineer working remotely from India.
Completing the data pipeline, building real APIs, integrating external services like CIFAS, standing up graph-based fraud detection, deploying cloud infrastructure and shipping a working frontend spans backend, data, infrastructure and UI, which is normally a job for a full team.
Retail Cache could not afford long timelines or heavy processes. They needed someone who could move fast, make sound calls independently and think about the product rather than execute tickets.
We mapped the gap between the existing mock-driven prototype and a production-ready platform, then planned a build that shipped working features incrementally rather than all at once.
We built the fraud detection engine across 10+ scenarios, integrated CIFAS, added graph-based network analysis for fraud ring detection, and replaced every mock endpoint with real GraphQL APIs.
Screening pages for orders, claims and returns, a live fraud dashboard, case detail views, bulk actions, dispute pack exports, audit logs and end-to-end tests, all wired to live data with authentication and multi-tenant support built in.
The entire cloud environment, compute, databases, auth and event processing, was deployed as infrastructure as code in Terraform, with CI/CD and migrations set up for safe, repeatable deployments.
10+ configurable fraud scenarios run across every order, claim and return, in real time.
Pseudonymised data matching across retailers surfaces repeat offenders no single retailer could catch alone.
A full analyst dashboard to screen, investigate and act on cases, and to generate dispute packs, connected to live data.
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We are based in UK, but we can support your business in any time zone, we work 24x7.
Difficult is done at once, the impossible takes a bit longer. If it is possible, consider it done. The impossible, it will be done!
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