Hardware Podcasts

Foghorn's Ramya Ravichandar | Ensuring Value with Edge AI in IIoT Applications

IoT For All Podcast · 20 Jul 2020 · 30m

AI in practice: 2 real uses of AI described

What this episode covers

Ramya Ravichandar, VP of Products at Foghorn, explains how the company uses edge AI to deliver real-time value in industrial and commercial IoT deployments. She details Foghorn's evolution from edge analytics to machine learning, highlighting use cases like defect detection at Stanley Black & Decker and workplace safety monitoring. Ravichandar argues that 'proof of value' is critical for scaling IoT projects, as edge computing enables low-latency responses and reduces bandwidth constraints compared to cloud-only architectures. She also discusses the complementary relationship between cloud-based model training and edge-based inference, and Foghorn's new 'Lightning' solutions for rapid deployment.

AI in practice

Where this conversation describes AI being used to build or deliver a product. Part of the AI in practice tracker.

ManufacturingFoghorn edge AI platform

The platform used machine learning models running on edge devices to identify real-time defects, such as ink smudges or overlapping numbers, on Stanley Black & Decker's manufacturing line, allowing operators to take immediate corrective action.

“we were deployed there to figure out, um, you know, defects as they were manufacturing a product in real time. … the idea was how do you really identify, you know, maybe a smudge of an ink or maybe 2 numbers overlap. But the idea is to be able to go in and identify those defects in real time.”

Speaker 2 · Hear it at 4:57

Quality & testFoghorn edge AI platformreduced crap tremendously

The platform analyzed video streams from an assembly line to detect product issues in real time, which significantly reduced the amount of scrap material produced at one stage of the manufacturing process.

“we've we've actually worked with one of the, um, brilliant factories of G, one of our very 1st deployments back in the day where we identified issues with a particular product manufactured. Um, and it was they had 3 different stages and we were able to get um, insights into stage one and reduced crap tremendously.”

Speaker 2 · Hear it at 17:24

Most relevant to

companies scaling up

Topics

Mentioned

Companies
Foghorn, Stanley Black & Decker, Cisco, Core Connect, Tesla
Products
Lightning Solutions

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