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German AI startup mika raises €6M to simplify accounting and tax for SMEs

Berlin-basedvertical AI startup mika has raised €6 million in a seed round led bypan-European investor Smedvig Ventures. Basel-based family office Wecken &Cie. (Care4 AG) also participated, alongs...

German AI startup mika raises €6M to simplify accounting and tax for SMEs

German AI startup mika has secured €6 million in seed funding to streamline accounting and tax processes for small and medium-sized enterprises (SMEs). Investors include pan-European firm Smedvig Ventures, family office Wecken & Cie., as well as notable founders Dennis Bemmann, Michael Brehm, Johannes Ditterich, and Martina Pfeifer. Existing investors Keen Venture Partners and Dutch Founders Fund also bolstered their stakes.

With nearly one million small incorporated businesses in Germany, many rely on tax advisers due to the complexity of tax rules and potential consequences of errors. mika seeks to simplify these processes through an AI-native financial operating system specifically designed for SMEs. The platform, built on a German-specific general ledger, reads receipts and payments, categorizes transactions, records them, and explains individual entries to users.

Trained accountants at mika review results, intervening when human intervention is necessary. Users can interact with the platform through mika or the Model Context Protocol (MCP) via assistants like ChatGPT or Claude. The system explains correspondence from tax authorities and answers questions about company accounts and finances, though individual tax advice is not offered.

mika's CEO Agnieszka Walorska emphasizes that the platform aims to break down the barrier that often prevents founders from starting their own companies. Currently, around 400 GmbHs and UGs use mika, up from over 750 at the beginning of the year. The funding will be used to expand the company and further develop its platform for growth.

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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