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TurinTech innovates with Artemis code optimisation

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TurinTech, a London-based technology vendor, plans to revolutionise code optimisation with its GenAI Artemis solution. Artemis is based on a proprietary large language model (LLM) – although it can be used with other LLMs – that is trained to help financial firms optimise software code, speed up execution, reduce cloud costs and lower carbon emissions. To date, Artemis has been implemented by investment banks in the UK, France and US.

The company was set up in 2019 by co-founders who met at University College London while doing PhD research work. They went on to work in financial institutions, where they experienced problems of getting code into production at any speed, internal bottlenecks holding up developers, and the pain points of code reviews.

 “There had to be a better way of doing things and a way to resolve these problems,” says Leslie Kanthan, CEO and co-founder of TurinTech, noting that while financial institutions tend not to have code optimisation teams, Artemis code optimisation can help them improve code quality, make developers more efficient, and give firms spending vast amounts of money on cloud savings of about 10% by optimising code, a potentially huge saving.

As well as optimising code and reducing costs, Artemis plays well into financial institutions’ sustainability goals by running better code faster, descreasing compute usage and providing energy savings.

Artemis scans software code on-premises or in the cloud. It uses TurinTech’s LLM, which has been trained on millions of lines of code and informed by the team’s proprietary knowledge, although it can also be used with other LLMs, perhaps less effectively, and takes hardware into consideration to allow legacy systems to perform to the best of their ability.

Use cases of the solution include identifying weaknesses in code and providing recommendations for optimal changes that enhance performance, noting code that could be sped up or improved by modifying particular lines, and analysing code bases to predict their efficiency – all with a human in the loop but reducing resource requirements overall.

Kanthan concludes: “Everyone wants to use AI, but will it add value to the business? LLMs are just another form of data, so you need apps for use cases. TurinTech has an app for code optimisation and is, at the moment, leading the market.”

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