Overshoot

Enable developers to build and run AI vision applications

Winter 2026

Computer Vision

Infrastructure

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.

Founders

Younes El hjouji

Founder

Ex-Intel Computer Vision AI Frameworks Engineer. CTO of Overshoot. Building the platform for realtime vision powered by LLMs.

Zakaria El hjouji

Founder

Co-founder and CEO of Overshoot. Prior to this, Zakaria spent 7 years building pricing algorithms at Uber and writing GPU kernels at Meta AI. Won 3 prominent AI hackathons. Built and sold a software product while in grad school at MIT. He graduated top of his class at the London School of Economics. Likes to nerd out on anything inference and VLM / video understanding.

AI Research Report

Problem & Solution

Problem and Solution Analysis

Overshoot addresses a critical bottleneck in the deployment of artificial intelligence: the difficulty of running Vision Language Models (VLMs) on live video streams in real-time. While AI has reached a point where it can 'see' and understand the physical world, existing platforms often make it prohibitively difficult for developers to build real-time applications. Common issues include high latency, limited model availability, and infrastructure that breaks when attempting to scale. These technical hurdles prevent the widespread adoption of 'video agents' in fields like physical security, robotics, and consumer products, where immediate response times are essential.

The solution offered by Overshoot is a high-performance SDK and API that allows developers to connect live video feeds to a vast collection of Vision Language Models with minimal effort. The company claims that developers can integrate their technology with just three lines of code, effectively removing the 'infrastructure headache' typically associated with computer vision. By optimizing the entire pipeline—including codecs, streaming protocols, and inference engines—Overshoot achieves response times of less than 200ms. This is significantly faster than existing inference platforms and, in many cases, faster than human reaction time.

The value proposition of Overshoot lies in its simplicity and speed. Users can point the SDK at a video source (such as a camera, file, or screen), describe what they want the AI to look for in plain English, and receive continuous results in real-time. For example, a developer could prompt the system to 'alert me when a person enters the restricted area' or 'track the movement of the pet,' and the system will provide a continuous stream of data as the video plays. By providing a production-ready, low-latency environment, Overshoot enables a new generation of real-time video applications that were previously technically unfeasible.

Market & Competitors

Market and Competitive Landscape

Overshoot operates in a highly competitive but rapidly growing market for AI infrastructure and video analytics. The target audience includes developers and enterprises building applications for physical security, gaming, robotics, and smart consumer devices. The market is currently characterized by a shift toward edge computing, where processing occurs closer to the data source to reduce latency and bandwidth costs. This trend is a significant tailwind for Overshoot, as its platform is specifically designed for the low-latency requirements of real-time video streaming.

The competitive landscape is composed of three primary groups. First are the large cloud and hardware incumbents, such as NVIDIA, AWS, Google, and Microsoft. These companies provide the underlying hardware (GPUs) and broad AI inference stacks. While powerful, these general-purpose platforms often lack the specialized, end-to-end optimization for video modalities that Overshoot provides. Second are specialized video analytics vendors and startups like Viso.ai, which offer platforms for computer vision applications. Third are open-source frameworks and libraries that developers use to build custom solutions, though these often require significant infrastructure management.

Overshoot's primary competitive advantage is its singular focus on image and video modalities. Unlike general-purpose AI platforms, Overshoot has optimized its entire stack—from video codecs to inference engines—specifically for real-time video. This focus allows them to achieve sub-200ms latency, which they claim is 10x faster than existing inference platforms. Additionally, their developer-centric approach, featuring a TypeScript SDK and a '3 lines of code' integration, lowers the barrier to entry for teams that do not have deep expertise in AI infrastructure. As of early 2026, the company has already seen adoption by over 1,000 developers, indicating strong initial product-market fit in the developer community.

Total Addressable Market

Quantitative Market Analysis and TAM

Overshoot operates at the intersection of several rapidly expanding markets: Computer Vision, Video Analytics, and Edge AI Software. As of 2026, the global computer vision market is valued at approximately USD 23.62 billion, with projections suggesting it will reach USD 58.29 billion by 2030, representing a CAGR of 19.8%. Within this broader category, the video analytics segment is a critical driver, projected to grow from USD 8.3 billion in 2023 to USD 22.6 billion by 2028. This growth is fueled by the increasing accessibility of edge computing devices that allow for real-time analysis without the latency of centralized cloud processing.

The Total Addressable Market (TAM) for Overshoot can be viewed through two primary lenses. A conservative estimate focuses on the 'developer platform' slice of the video analytics and edge AI software markets. With the edge AI software market projected to reach USD 8.89 billion by 2031 and the edge AI platform segment being the fastest-growing category, the immediate addressable market for a developer-first inference platform in 2026 is estimated to be between USD 2 billion and USD 10 billion. This figure accounts for the specific portion of the market that prioritizes modular architectures and standardized development environments over end-to-end hardware/software bundles.

A broader strategic TAM considers the full potential of real-time vision across all industries, including gaming, robotics, and security. By 2026, the global edge AI market is valued at USD 47.59 billion and is expected to skyrocket to USD 385.89 billion by 2034. For a company like Overshoot, which enables low-latency vision at scale, the long-term market opportunity encompasses a significant portion of this edge AI spend. The methodology for these estimates relies on identifying the overlapping growth in real-time inferencing needs and the shift toward decentralized, edge-based AI processing, where Overshoot's low-latency SDK provides a distinct competitive advantage.

Founder Analysis

Founders and Professional Background

Overshoot was co-founded by cousins Zakaria El Hjouji and Younes El Hjouji, both of whom possess deep technical expertise in high-throughput systems and computer vision. Zakaria El Hjouji, serving as CEO, has a distinguished academic and professional record. He graduated at the top of his class from the London School of Economics (LSE) and later attended MIT for graduate studies. Before founding Overshoot, Zakaria built low-latency, high-throughput pricing algorithms (specifically surge pricing) at Uber and worked on GPU inference engines at Meta. His background also includes successfully building and selling a previous software product and winning several prominent AI hackathons.

Younes El Hjouji, the company's CTO, brings extensive experience in computer vision and AI frameworks. He was a founding engineer at Cosmonio, a startup that was later acquired by Intel. During his tenure there and subsequently at Intel, Younes built a comprehensive training and serving platform for computer vision from the ground up. His expertise lies in the architectural side of AI, specifically in how models are served and scaled in production environments.

Together, the founders combine experience from some of the world's leading technology firms with a specific focus on the intersection of low-latency systems and machine learning. Their combined history at Uber, Meta, and Intel provides them with the necessary technical foundation to address the complex infrastructure challenges associated with real-time video inference. This leadership team is uniquely positioned to bridge the gap between advanced Vision Language Models and the practical, high-performance requirements of real-world video applications.

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