Applied AI Scientist

Omnifold is building purpose-built AI systems to optimize how physical products are manufactured, stored, and distributed throughout the world. The company precisely models the underlying mechanics of supply chain and commercial operations to deliver intelligent, self-improving forecasting algorithms specific to the complexities of each customer's business.

Founded by a team with backgrounds at MIT, Stanford, Google, Palantir, and Snowflake — including a CEO with a prior nine-figure exit to Snowflake and a CTO with deep research experience — Omnifold treats supply chain forecasting as a fundamental computer science problem rather than an application layer problem. The company trains custom AI models for each customer, leveraging advances in deep learning, reinforcement learning, and optimization.

Mission: Every bad forecast has a physical consequence — unnecessary goods manufactured, emergency air freight for misallocated products, workers with nothing to do or working frantic overtime. Omnifold's mission is to eliminate waste and accelerate growth for every company with physical products.

What makes Omnifold unique:

• Not a wrapper around GPT: Omnifold competes in a space where incumbents are not using frontier AI models. The company builds its own proprietary models and conducts its own research.
• Capital efficient: Series B funded ($60M raised) with only 17 people. The company lands six-figure enterprise deals without premature scaling.
• Real customers, real revenue: Deployed with multi-billion dollar corporations and high-growth businesses, each paying six-figure annual contracts.

Backed by Lightspeed Venture Partners and Kleiner Perkins, along with industry leaders including John Thompson (ex-Chairman of Microsoft), Girish Rishi (ex-CEO of Blue Yonder), and Yannis Skoufalos (ex-Chief Supply Chain Officer of P&G).