Interview with Daniel Warner, CEO and Co-Founder at Edge AI Company: LGN

LGN offers a suite of products that helps organisations deploy scaled edge AI solutions. Our solutions are around three pillars – Scale, Optimisation and Resiliency. We have an orchestration tool that allows for scaled deployment, monitoring, A/B testing and connectivity of edge devices running AI.

Our expertise and core IP allows us to create ultra low latency inference so that we can get sophisticated models running on real-world, scalable hardware. Our software adds robustness to a customer’s system, both in real-time by reducing the need for finely tuned calibration but also in the long term by creating optimal learning loops where anomaly data is fedback to the central point for quick and efficient re-training / updates.

Post Urban Ventures - AI & Deep tech venture builder

How did you come up with the idea for the company?

In October 2019, we were previously working on defence projects improving laser vision systems in less-than-ideal environments (cloud/fog). We were generating extreme amounts of data at the edge and needing to constantly retrain and improve a model whilst having to go through the same jagged training process that everyone else does.

We figured there must be a better way to leverage existing scale to optimise this flow. These moments were the early days in us seeing how the future would unfold and plan to address them.

How has the company evolved during the pandemic?

We were a remote team before the pandemic, but we travelled a lot and spent lots of high output and intensive sessions together -we’ve missed these and are excited for the world to recover so we can get back to those sessions. We’ve seen new opportunities arise across the automotive, manufacturing, and agri-tech sectors, which form our key verticals/markets.

More traditional industries are now adopting digital transformation at pace. For example, for the automotive industry, this is the fastest push into autonomous vehicles and smart mobility in recent years. Further to this, people still required access to farming/food, but seasonal labour was off the menu, so demand in agri-tech capabilities increased significantly, and this is one of the sectors we were able to re-focus our efforts.

What can we hope to see from LGN in the future?

In the future, we see a world of interconnected, self-directing and self-improving AIs. We want LGN to be creating the fabric of that common AI language of tomorrow, today.