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Moonshot AI

moonshotai.org

Website unreachable since Oct 17, 2026

Moonshot AI develops Hyperlearn, a data search engine that utilizes advanced algorithms to significantly accelerate machine learning model training and predictions, achieving speeds 2000 times faster while consuming 50% less computational resources. This technology addresses the inefficiencies of slow AI predictions, enabling organizations to make timely, data-driven decisions across various applications, including simulations and stock predictions.

Traction 1/10
Sydney, Australia · HQ
Founded 2020300+ followers
  • Artificial Intelligence
  • Data & Analytics
  • Developer Tools
  • Software Only
Updated 3 months ago

Funding

Raised to date

$5BRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.

across 6 rounds

ACMCY+8

$2B · Undisclosed

Announced May 2026 · from article date

Disclosed cumulative: $5B

6/6

Founders

Founder details are not available yet.

Product

Problem

Training and deploying machine learning models can be computationally expensive and time-consuming, hindering the ability of organizations to rapidly iterate and make timely decisions. Large AI models require significant computational resources, leading to long prediction times and increased energy consumption.

Solution

Moonshot AI offers Hyperlearn, a data search engine designed to accelerate machine learning model training and predictions. Hyperlearn utilizes optimized algorithms to achieve faster processing speeds while reducing computational resource consumption. The company's technology enables organizations to make data-driven decisions more efficiently across various applications, including simulations and predictions. Their first product, Unsloth, is intended to make AI faster and more energy efficient.

Target Audience

The primary users are organizations and researchers involved in machine learning, AI, data science, and computational modeling.

Features

  • Optimized algorithms for linear/ridge regression, PCA, and SVD
  • Scikit-learn interface for ease of use and integration
  • Algorithms run faster while using fewer resources
  • Suitable for use on all devices
This profile is AI-generated and may contain inaccuracies.