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AI for spare parts startup Intropy raises $11M

A London-based AI for spare parts startup has raised $11m in new funding, as it targets US expansion. Intropy has raised a seed round from lead investor Felix Capital, with participation from Quiet Ca...

AI for spare parts startup Intropy raises $11M

Intropy, an AI-driven spare parts startup based in London, has successfully raised $11 million in a new funding round, with plans to expand its operations into the US market. The seed funding, led by Felix Capital, was joined by participating investors Quiet Capital and previous backers General Catalyst and Firstminute Capital. General Catalyst also led the pre-seed investment on an undisclosed scale.

Founded in 2024 by former researchers from UK AI insurtech Tractable, YihKai Teh and Franziska Kirschner, Intropy specializes in automating inventory, pricing, and decision-making processes for spare parts businesses. Their AI technology streamlines operations by aggregating fragmented structured and unstructured data, automating decisions within a customer's existing Enterprise Resource Planning (ERP) system rather than offering recommendations for manual review.

By doing so, Intropy allows companies to transition from periodic, reactive reviews to proactive, continuously updating decisions.

In the automotive industry alone, it is estimated that over $4 billion in spare parts transactions occur daily. Since its inception, Intropy's technology has processed more than $10 billion in spare parts demand. The startup, co-founded by Teh and Kirschner, intends to utilize the new funding to accelerate product development, expand its workforce, and establish a presence in New York.

Teh, co-founder and Chief Technology Officer, commented, "Every machine made from multiple components will eventually require spare parts, whether it's a car on the road today, an autonomous vehicle of tomorrow, or a robot supporting humanity on Mars. We're building the intelligence layer that understands the extraordinary complexity of spare parts: what fits, how it performs, and when it is needed, so parts businesses can make better decisions."

Written by urgent.news from Tech.eu's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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