Peregrine.ai flagged a clip involving a truck driver using an edge AI model for tailgating
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Our tailgating model at Peregrine.ai flagged this clip the other day.. 🚜 My first reaction: it got it wrong. The machine in front barely looks like a road vehicle, and…
Company
Peregrine.ai
AI-powered vision, for smarter cameras. Edge AI for safety, security and real-world operations.
- Industry
- Software Development
- Location
- Berlin, DE
- Company size
- 11–50 employees
About Peregrine.ai
Peregrine.ai | Real-World Visual Intelligence at the Edge We build one of the most effective visual intelligence engines in the world—turning ordinary cameras into a network of real-time, context-aware sensors. At Peregrine.ai, we combine ultra-efficient Edge AI with a scalable data engine to unlock actionable insights from live video—without compromising privacy. Our platform powers smart safety systems in transportation, enables live annotation and search of camera data, and delivers high-quality geospatial and contextual datasets for enterprises. 🚚 Transportation & Telematics — We enhance road safety and risk visibility by transforming dashcams into perception devices, in partnership with leading telematics providers across Europe and North America. 🗺️ Data-as-a-Service — We deliver anonymized, structured visual data (road assets, signage, infrastructure) to power mapping, logistics, and smart city platforms. 🏭 New Domains — Through Peregrine Labs, we’re expanding into factory safety, public security, and dual use applications. Powered by a real-time data flywheel, our system continuously improves through every mile and moment captured—delivering smarter insights, safer operations, and scalable impact.
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Latest activity from Peregrine.ai
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Peregrine.ai is running a beta version of its retrofit cabin monitoring system in select vehicles.
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Peregrine.ai is building a lightweight system for retrofit cabin monitoring alongside road-facing vision and edge processing.
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Peregrine.ai runs on the device, interprets the scene, and sends structured, anonymised events back to the existing platform to identify tailgating and distraction, assign risk scores, and provide context for the platform.
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