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Antelligent

Antelligent builds AI models that can run sustainably and privately on edge devices, enabling applications in remote or offline environments such as rural clinics or field operations. Their flagship offering includes a compressed version of the DINOv2 vision model that runs fully offline at 50 MB while retaining about 80 % of top‑5 accuracy, dramatically reducing compute and energy requirements compared to cloud‑bound alternatives.

Updated 15 days ago

Funding

Funding not disclosed

Funding rounds are not available yet.

Founders

Founder details are not available yet.

Product

Problem

Many AI applications rely on large, cloud‑based models that demand high computational resources, constant internet connectivity, and expose user data to external servers, limiting their use in remote, low‑bandwidth, or privacy‑sensitive environments.

Solution

Antelligent delivers AI models and toolkits optimized for on‑device, offline operation. Their flagship offering compresses the DINOv2 vision model to a 50 MB footprint while preserving roughly 80 % of top‑5 accuracy, enabling deployment on edge hardware without internet access. By reducing model size and computational demand, the solution lowers energy consumption and hardware costs, supporting sustainable AI practices. The offline architecture ensures that data never leaves the device, enhancing privacy for users in sensitive contexts such as rural clinics or field operations. Antelligent also provides a suite of development tools to help partners integrate these models into diverse hardware platforms.

Target Audience

Primary customers include hardware manufacturers, enterprise developers, and organizations deploying AI in remote, low‑connectivity, or privacy‑critical settings such as rural healthcare facilities, field operations, and edge‑focused IoT solutions.

Features

  • 50 MB compressed DINOv2 vision model that runs fully offline on edge devices
  • Retains ~80 % of top‑5 accuracy compared to the original cloud‑based version
  • Designed for low‑resource hardware, reducing power and compute requirements
  • End‑to‑end privacy: all inference occurs on‑device with no data transmission
  • Development toolkit for easy integration of compressed models into custom hardware and software stacks
  • Emphasis on sustainable AI through reduced energy consumption and smaller carbon footprint
This profile is AI-generated and may contain inaccuracies.