Our Work

Using AI to detect disease

Artificial Intelligence

A spinout of Cambridge University had an algorithm that could detect disease from medical images, but they needed help to deploy it at scale.

We built a redeployable Kubernetes cluster that could process thousands of medical images per second, allowing the algorithm to be used in hospitals and research labs around the world. This meant that the solution could be cloud-based or on-premises, depending on their client's needs.

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Reducing the NHS waitlist

Healthcare

A UK company had the goal of reducing the NHS waitlist by allowing the most chronic patients to video call with GPs remotely. They wanted us to help them refactor their platform to make it more stable and scalable.

We recoded the worst parts of their platform, replacing it with a solid foundation. Then we trained their existing team on the work we did and how to maintain it when we left. This allowed them to scale their platform to meet the demands of their growing user base, reduce the NHS waitlist and eventually be acquired by a large insurance provider.

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Adding a modern CI/CD pipeline to a digital signage platform

Digital Signage

A digital signage company had a platform that was built in the early 2000s and was in need of a modern CI/CD pipeline to help them deploy their platform more efficiently. They were deploying every 6 months and wanted to deploy every day, ideally multiple times a day.

We moved their entire codebase (over 2.6 million lines of code) to a modern CI/CD pipeline, allowing them to deploy multiple times a day. This meant that they could respond to customer feedback faster and deploy new features more frequently.

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Trading bonds between banks

Finance

A privately-backed startup wanted to build a platform that would allow banks to trade bonds with each other. They needed help building the Minimum Viable Product platform from scratch.

We built a platform that allowed banks to trade bonds with each other, reducing the time it took to complete a trade from days to minutes. This meant that banks could trade bonds more efficiently and at a lower cost, potentially saving them millions of pounds per year.

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Detecting lung disease using AI

Healthcare

Two NHS doctors founded a startup that developed an ML algorithm that took in CT lung images and pinpointed where the damaged tissue was with the hopes of projecting where the disease would progress to in a years time, given there was no cure. The only problem was that they didn't know how to scale it.

Building their platform from scratch, we worked with them for their entire first year and built a redeployable Kubernetes cluster in the cloud that could process thousands of images per second. Their startup was recently valued at over £10 million.

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