amber Raises €7M Series A to Power Europe’s Enterprise AI Agents

amber Raises €7M Series A to Power Europe’s Enterprise AI Agents
EuropeFunding
WorkNation
August 17, 2026

amber Raises €7M Series A to Power Europe’s Enterprise AI

German AI startup amber has raised €7 million in Series A funding co-led by Ventech and NRW.Venture to expand its enterprise AI platform across Europe.

Founded in 2021 by Philipp Reißel, Bastian Maiworm and Igli Manaj, the Aachen-based company is building AI agents that can move beyond answering questions to taking autonomous actions within enterprise workflows.

The company has more than 400 active customers and employs around 60 people.

Building an AI Data Layer

amber's platform creates a data layer beneath its search, chat and AI agent tools.

It brings together company documents, images, presentations and records from customer and resource planning systems into an organised knowledge layer before AI models access the information.

The company says this approach can reduce AI token costs by up to 60%, depending on usage.

amber says the system is particularly useful for engineering and construction companies that need to quickly retrieve information from years of previous projects.

A European AI Sovereignty Play

amber is also positioning itself around European data sovereignty.

The company operates on German cloud infrastructure and says it has no American shareholders, allowing it to avoid exposure to the US Cloud Act.

The approach gives amber a differentiated position against larger enterprise AI platforms such as Glean and Microsoft Copilot.

Growing Across Europe

amber previously received €2.1 million from Ventech in March 2025, with the investor following up in the new Series A.

The startup counts companies including Ritter Sport and Scheidt & Bachmann among its customers.

amber plans to use the new funding to expand into the Benelux region before moving into the Nordic markets.

The company is betting that enterprise AI adoption will increasingly depend not only on the quality of AI models, but also on the quality, accessibility and sovereignty of the data those models can use.