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British AI tech firm Kortical beats Google as it helps drive revenues for business

Success: Kortical has improved the success rate of machine learning implementation 10x for business

David vs Goliath: Kortical is configuring machine learning algorithms faster and making them learn better than Google’s Vertex, helping companies with first mover advantage

Growth: Kortical to raise an investment round in 2021 as growth hits 300% year-on-year helping more businesses adopt machine learning technology

9th August 2021, London: A British AI tech start-up Kortical, which helps companies use machine learning (ML) and artificial intelligence (AI) capabilities, has improved 10x their success rate of generating positive outcomes across their organisations vs the industry average.

Companies use ML and AI to help predict demand for their products and services, for pricing and an array of business decisions yet only 9% of these tech implementations are ROI positive. Kortical’s platform is able to quickly absorb the data sets of any company and creates algorithms that offer strategic and commercial insights for them. Saving them time and investment in large scale tech deployments, the platform has delivered 92% ROI positive outcomes for business.

Last month, Google released its latest offering Vertex AI and Kortical is already outperforming them and has been since they first went head to head in 2019 at the Schroder’s Datathon. Kortical tested both platforms using well known public datasets from Kaggle and another from a real life client. Data was funnelled through both and overall Kortical averaged 2.47% better across all datasets and 10.75% better on the real life customer data. This difference in performance would mean the project that resulted in a £500k saving on Kortical would have not been viable on Vertex due to subhuman performance and would be another failed project statistic. Furthermore, Kortical was also 7 times faster to create the results vs Google’s Vertex AI.

Andy Gray, CEO and co-founder at Kortical commented: “At the moment businesses are still in the early days of the machine learning gold rush, where you can crest a hill and stumble upon a nugget. Better ML accelerators are like better metal detectors helping you find those nuggets faster.”

Kortical is working with a wide range of customers across industries, from saving 54% on the blood supply chain waste for the NHS, to enabling faster and more impactful ML project delivery for Capita, to “significant operational efficiencies” through back office automation of tax processes at Deloitte and hyper-personalised marketing with Hyundai. As well as working with smaller start-ups.

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“Initially it was really only the big players that were the early adopters, where they had the luxury to experiment with new technology and those experiments have turned into significant business so increasingly we’re seeing smaller businesses that recognise the strategic advantage and huge potential of ML to really distance themselves from their competitors” added Andy Gray.

Kortical came into existence because the original founders Andy and Alex were trying to create an AI product but as they engaged different customers, they found that the data was always a little different and they needed to keep building new models but that the process was slow, error prone and repetitive. Kortical is the culmination of 7 years of trying to take the pain out of creating enterprise ready AI and ML solutions, quickly and easily but with enough control that expert users can still create exactly what they want.

Looking ahead Andy Gray said: “It’s great to see that the conversation has shifted from do I need a machine learning accelerator platform, to which platform should I use? I’m incredibly proud of what we’ve accomplished and excited to deliver on our plan to see what the future holds”

Kortical helps companies that have data sets and a business problem they want to solve. Kortical works with tabular, NLP and time series data and can take you right through to live ML web applications or self learning API based services. Some of the most popular use cases are back office automation, demand prediction and hyper-personalized marketing.

All the major commentators are expecting the machine learning platform market to boom, with Gartner estimating that by 2022 75% of all new end-user solutions using AI and ML techniques will be built using commercial solutions rather than open sourceAndy Gray concluded “Over the past 12 months businesses have focussed on continuity and their remote work set up but this year we’re seeing signs of growth getting back to 300% year on year and will be looking to raise an investment round by the end of the year as we scale our business”.



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