Edgify’s technology enables any connected device - known as ‘edge devices’ - to interpret vast amounts of data generated at the edge, train a complete AI model locally, and proceed to share that learning across an entire network of similar devices, thereby training them all to identify unique aspects of a given object for use of computer vision, NLP, voice recognition and any other form of AI.

Edgify’s framework can train AI on a network of edge devices ranging from MRI machines, connected cars, checkout lanes, mobile devices and anything that has a CPU, GPU or NPU. Due to the continuous  and distributed learning at the edge, on data generated locally the accuracy of an AI model trained on an Edgify Framework is averaged at 99.98% and never decreases. This helps to reduce the risks, costs and time associated with transferring sensitive-data to or from an external server and effectively eliminates the need for a Cloud-based learning infrastructure.

The strategic investor is already looking at embedding the Edgify Framework on its chipset, and the backing will enable Edgify to rapidly scale and commercialise its solution. Though the solution is agnostic in nature, Edgify has started deploying its framework within the retail sector initially following a successful proof-of-concept partnership with some of the world's leading checkout manufacturers and grocery retailers.

Edgify’s technology is already being used in supermarkets. Self-checkout machines are able to distinguish between barcodeless groceries before sharing the acquired knowledge or model across a distributed yet collaborative framework of point of sale machines, leading to faster, more accurate and low touch checkouts.

Businesses that deploy Edgify’s edge training framework will have no need to invest in new infrastructure as each edge device will accumulate and distribute knowledge across an entire network, without the need to transfer any of the data to the Cloud. This also allays any privacy concerns raised by Cloud-based training.

Lead investor from the Semiconductor Giant said: “Our Investment in Edgify is of a strategic nature that is by embedding their framework on our chipset we will be able to enable our customers to reduce their data communication and storage costs. We feel Edgify’s Federated Learning framework is industry leading and we look forward to our active collaboration.”

Ofri Ben-Porat, CEO and Co-founder of Edgify, commented: “Edgify allows companies, from any industry, to train complete deep learning and machine learning models, directly on their own edge devices. This mitigates the need for any data transfer to the Cloud and also grants them close to perfect accuracy every time, and without the need to retrain centrally.” 

Nadav Israel, Co-founder and CTO of Edgify, added: “The potential is enormous due to the volume of data each company generates, most of which is rendered redundant due to the processing power required to upload, interpret and analyse it in the Cloud. Edgify’s technology allows businesses to train on the entirety of their data, promising accuracy levels never achieved before.”

Mangrove partner Hans-Jürgen Schmitz who will join Edgify’s Board commented: “We have followed Ofri, Nadav and their ideation progress since 2017. When they decided to move away from consumer applications and focus their efforts on business use cases, the time was right for us to engage with them. We expect a surge in AI adoption across multiple industries with significant long term potential for Edgify in medical and manufacturing, just to name a few. Edgify is already our second data tech investment in 2020 alone and we are excited to join forces with them for the years to come.” 

Simon King, Partner and Deep Tech Investor at Octopus Ventures added: “As the interconnected world we live in produces more and more data, AI at the edge is becoming increasingly important to process large volumes of information. Edgify’s work is a shining example of using pioneering technology to address society’s challenges and improve the way we operate - driving forward progress across all industries. The Deep Tech team at Octopus Ventures looks forward to continuing to support Ofri and the team on their journey.”

In addition to the retail industry, Edgify is also in the process of deploying its Framework for the Medical and for the Automotive industries. Using the Edgify Framework to train AI models for pathologies in chest X Rays for medical, and to identify spare parts in the automotive industry.