
Maisa AI, a startup based in both Valencia and San Francisco, has secured $25 million in seed funding in a round led by Creandum.
SUMMARY
- Maisa AI, a startup based in both Valencia and San Francisco, has secured $25 million in seed funding in a round led by Creandum.
The company developed Maisa Studio, a platform that enables businesses to build and train digital workers using natural language. The goal is to address common workplace issues with generative AI, including inconsistent results and a lack of transparency.
Additional investors include Forgepoint Capital International, along with returning backers NFX and Village Global.
To support the growing demand and waitlist, Maisa AI plans to expand its team from 35 to 65 employees by early 2026.
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The 95% failure rate of AI pilot projects highlights a broader challenge in how enterprises adopt AI. Research reveals that only 8% of companies are actively practising the core capabilities needed to scale AI effectively, suggesting that most lack the foundational elements for success.
Key obstacles include a lack of clean, usable data, insufficient computing resources, and a shortage of skilled data professionals issues that go far beyond simply selecting the right AI model.
Maisa addresses these core challenges by focusing on structure and accountability with its “chain-of-work” methodology. Rather than chasing flashy tech the company is solving real operational problems within enterprises.
Their growing client base in regulated industries, such as banking and automotive, shows that companies bound by strict compliance standards value reliability and transparency over speed, underscoring Maisa’s strength in building trustworthy AI systems for enterprise use.
About Maisa AI
Founded in 2024 Maisa offers an AI-powered automation platform that handles complex, decision-heavy enterprise processes. Its Digital Workers provide reliable, transparent execution for tasks such as compliance, finance, and onboarding, where accuracy matters. Built for scale, Maisa ensures trusted automation with full visibility resisting errors traditional tools and GenAI often struggle with.
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