Overshoot

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AI Infra for real-time vision applications

Winter 2026Founded 20255 peopleSan Francisco, CA, USA

overshoot.ai ↗LinkedIn ↗

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AI insightcan contain mistakes

Real-Time Vision AI InfraAPI/InfraAI developers, robotics/security companiesMedium competition

Moat

Largest collection of vision models, low-latency inference, and scale handling differentiate; 300+ developers create network effects.

Key risk

Competing against cloud giants (AWS, Google) and specialized vision platforms; hardware complexity and inference costs high.

Why now

Physical AI, robotics, and real-time vision applications accelerating; developers demand simple abstraction.

Competitors

AWS, Google Cloud Vision, Roboflow, specialized vision platforms

About

Overshoot makes it easy for developers to build and run real-time vision applications. AI can see and understand the physical world. This unlocks new applications in physical security, safety, gaming, robotics and general consumer products. Soon, video agents will watch your home and your pet when you’re away. However, existing platforms make it painful for developers to build these real-time applications. Slow inference. Limited model availability. Break at scale. Overshoot solves this. Today, 300+ developers connect live video feeds to the largest collection of Vision Language Models with 3 lines of code. And get responses in less than 200ms. 10x faster than any existing inference platform. Zero infra headache. Our moat is focus. Image and Video are fundamentally different modalities from text. By focusing on them, we are able to make strong technical leaps from codec, streaming protocols to inference engines. Younes and Zakaria are cousins. Zakaria graduated top of his class at LSE and MIT, and later built low-latency high-throughput pricing systems (surge) at Uber and inference engines at Meta. He previously built and sold a software product and won several prominent AI hackathons. Younes was a founding engineer at Cosmonio (later acquired by Intel). He built a training and serving Computer Vision platform from scratch. He witnessed firsthand customers abandoning traditional Computer Vision because it lacked the “general” intelligence LLMs have today. Together we’ve shipped large-scale systems and know where they break.