Gabriel Mitchell

Client: Ford Motor Company

Building the AI underpinning Ford's first smart consumer product: next-gen vehicle security.

Building the AI underpinning Ford's first smart consumer product: next-gen vehicle security.

At the beginning of 2020, I co-founded an AI research and development company called Playground, based in London. We won a 7-figure contract with Ford Motor Company to build them an ML-based threat detection algorithm for a new security offering. This grew into a multi-year relationship where they recognised us as a tier-1 supplier for machine learning & training data collection services.

My Role

Truck and Van owners have seen a dramatic rise in their belongings being stolen out of their vehicles. In an effort to combat these thefts, Ford tasked us to develop an intelligent, real-time threat detection algorithm that would be able to run in power constrained environments using audio and motion data.

Challenge

Truck and Van owners have seen a dramatic rise in their belongings being stolen out of their vehicles. In an effort to combat these thefts, Ford tasked us to develop an intelligent, real-time threat detection algorithm that would be able to run in power constrained environments using audio and motion data.

Solution

Truck and Van owners have seen a dramatic rise in their belongings being stolen out of their vehicles. In an effort to combat these thefts, Ford tasked us to develop an intelligent, real-time threat detection algorithm that would be able to run in power constrained environments using audio and motion data.

Outcomes

Truck and Van owners have seen a dramatic rise in their belongings being stolen out of their vehicles. In an effort to combat these thefts, Ford tasked us to develop an intelligent, real-time threat detection algorithm that would be able to run in power constrained environments using audio and motion data.

Process Block 1

We spent a considerable amount of time understanding the vehicle security space, learning about techniques used in attacking and breaking into vans and trucks. We also drew up the best combination of sensors and how to apply ML in a pragmatic way.

We spent a considerable amount of time understanding the vehicle security space, learning about techniques used in attacking and breaking into vans and trucks. We also drew up the best combination of sensors and how to apply ML in a pragmatic way.

Process Block 2

We spent a considerable amount of time understanding the vehicle security space, learning about techniques used in attacking and breaking into vans and trucks. We also drew up the best combination of sensors and how to apply ML in a pragmatic way.

We spent a considerable amount of time understanding the vehicle security space, learning about techniques used in attacking and breaking into vans and trucks. We also drew up the best combination of sensors and how to apply ML in a pragmatic way.

Process Block 3

We spent a considerable amount of time understanding the vehicle security space, learning about techniques used in attacking and breaking into vans and trucks. We also drew up the best combination of sensors and how to apply ML in a pragmatic way.

We spent a considerable amount of time understanding the vehicle security space, learning about techniques used in attacking and breaking into vans and trucks. We also drew up the best combination of sensors and how to apply ML in a pragmatic way.

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