Meet Dr Leslie Kanthan, Co-Founder and CEO at TurinTech: The Leader In Code Optimisation
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TurinTech was created following time spent in the financial industry where machine learning and code optimisation processes were mostly manual. These processes were time-consuming, resource-intensive, and often required deep domain expertise.
My co-founders and I believed that there was a more efficient way to overcome these difficulties. We began to build an end-to-end optimisation platform – evoML – for building efficient AI that delivers optimal results with production-quality code: faster prediction, reliable deployment, and lower costs.
evoML is the only platform embedded with code optimisation powered by our proprietary research. evoML helps businesses speed up the end-to-end data science process from months to weeks; optimise model efficiency for quicker inference speed, lower memory and energy consumption and reduce carbon emissions by 50%
The company works within various industries including banking, fintech, and investments. Within these sectors, the technology helps companies stay competitive, and improve customer experience with better accuracy and efficiency.
What do you think makes this company unique?
Our evoML platform is particularly unique as it improves the performance of ML models using intelligent optimisation techniques. It automatically identifies performance-critical lines of code, and recommends optimal solutions to accelerate speed and reduce compute costs.
The platform also offers a multi-objective optimisation feature, which gives users the ability to customise their AI models to better suit their business objectives. Different aspects such as explainability, execution time, and other user-defined objectives can be easily identified and tailored.
Another key element of evoML is the transparency the platform provides its users. More intelligent automation can be provided at each step of the data science process, enabling users to easily understand the model through visualisation. In addition, users can reproduce experiments, generate reports, and identify performance gaps and biases quickly, for AI validation and debugging.
Another unique aspect of our company is our culture. The company currently has over 35 employees from over 20 different nationalities. We aim to continue expanding and increase the diversity throughout the company to continue fostering an environment that is inclusive for all.
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How has the company evolved over the last couple of years?
Since its inception, TurinTech has grown from four founders to 40+ employees within the space of three years, raising over £4 million in funding from IQ Capital and SpeedInvest. Our evoML platform has also seen significant growth. It now has a range of applications, such as generating ML code from data, accelerating ML code for targeted hardware, and optimising any application code with ML-powered recommendations.
We have partnered with several prestigious academic institutions, such as UCL and King’s College London. This has allowed us to continuously advance AI research, as well as give students the opportunity to benefit from industry knowledge. We have also formed several strategic partnerships, helping several businesses improve their services through AI. For example, we partnered with a financial engineering consulting company which helped to elevate its consultancy services through AI.
What can we hope to see from Turin Tech in the future?
We are planning to scale on a few dimensions to capture a larger market share, improve the product and further develop the team. As the leader in code optimisation, we continue to advance our research and implement our findings into our product to enable our customers to stay on the cutting-edge. In terms of the platform specifically, we hope to advance the features available for the evoML platform, to increase the role it is playing throughout the code optimisation process.
Currently, the underlying optimisation technology we use can be considered generic. We want to develop more domain-specific optimisation products such as algorithmic trading optimisation.