Funding has been sourced from private angel investors including Growceanu, and non-equity funding from Innovate UK as part of an industry collaboration with Domin and University of Bath.

T-DAB.AI’s state-of-the-art Edge AI platform will revolutionise AI for IoT applications. It will enable Edge AI to be implemented across IoT infrastructure without the need to move vast volumes of data to the cloud. Hence increasing security and privacy, reducing expensive data and compute costs, and reducing the need for continuous network connectivity for connected device intelligence. Key issues amid global supply chain challenges. 

At the core of the platform is a new paradigm known as Federated Learning. T-DAB.AI’s vision is to serve seamless and massively scalable Federated Learning to the full spectrum of Edge compute, all the way down to the chip.

Designed by data scientistsT-DAB.AI’s platform enables teams to build or buy machine learning solutions, trained at the Edge, and push models into production and deploy them faster, more privately, and cost-efficiently than ever before. Users of the platform can either build their own solutions, or adopt pre-packaged solutions developed by T-DAB.AI and use AI to solve high-value industrial machine intelligence use cases.

This round’s funds will bring forward more advanced Federated Learning capabilities, automated Edge ML-Ops, and accelerated deployment mechanisms. In addition, funds will support the evolution of the sales and marketing functions of T-DAB.AI

"We invested in T-DAB.AI because it is a company made up of an excellent team, with people very well trained in business, technology and sales. They have also developed a solid set of innovative products and services in a technical field applicable in many industries, including production, energy, automotive, HVAC and Smart Building. The solution is already validated, the current sales are excellent and have a very high traction",

Ciprian Man, one of the investors and founders of Growceanu. Tweet

“This is great news for T-DAB.AI and for manufacturing more generally, to be able to utilise AI at the edge without the need for huge amounts of data collection and analysis is game changing.

Nick Hussey, CEO of The Manufacturer

ABOUT T-DAB.AI

T-DAB.AI is a technology company on a mission to revolutionise the building and transformation of Internet of Things (IoT) powered industrial businesses through decentralised artificial intelligence (AI).

Founded in 2016, T-DAB.AI has enjoyed year-on-year growth, winning large global clients across the industrial IOT sector as well as significant funding from Innovate UK. T-DAB.AI set out to solve the common challenges faced by data scientists across industry to rapidly develop, productionise and deploy AI models to the Edge at scale to solve high value industry use cases.

Artificial Intelligence and Machine Learning can deliver high ROI and new revenue sources across many applications in industry, but its widespread adoption is hampered by a lack of suitable machine learning infrastructure and the need to centralise data, which in turn leads to privacy risks, high costs, overdependence on cloud platforms and network availability.

To remove these barriers to AI for Industrial IoT, T-DAB.AI are harnessing a simple but revolutionary idea. Rather than move data from Edge devices to central cloud platforms to train algorithms, move the algorithms to the data and learn at the Edge. Intelligence is achieved by combining learning on many devices via a novel technology called Federated Learning.

“We’re hugely excited to have accelerated the next evolution of T-DAB.AI through this funding round. This is a big step towards bringing our revolutionary distributed Edge AI solution, OctaiPipe to the market. We’re delighted to have added a fantastic group of investors to our team”

Dr. Eric Topham, CEO & Founder

Read more in our success story

In this case study, see how we helped an OEM manufacturer maximise equipment productivity with a cloud-based ML/AI solution. Read more!

Do you want to find out more?