95% of generative AI pilots at companies are failing, prompting exploration of agentic AI systems that can learn and be supervised. Maisa AI is a year-old startup that raised a $25 million seed round led by Creandum and launched Maisa Studio, a model-agnostic self-serve platform to deploy digital workers trainable with natural language. Maisa centers on accountable AI agents and a 'chain-of-work' approach that builds executable processes rather than responses. The company developed HALP (Human-Augmented LLM Processing) to solicit user needs and outline steps, and a deterministic Knowledge Processing Unit (KPU) to limit hallucinations and increase reliability.
That's where Maisa AI comes in. The year-old startup has built its entire approach around the premise that enterprise automation requires accountable AI agents, not opaque black boxes. With a new, $25 million seed round led by European VC firm Creandum, it has now launched Maisa Studio, a model-agnostic self-serve platform that helps users deploy digital workers that can be trained with natural language.
While that might sound familiar - reminiscent of so-called vibe coding platforms like Cursor and the Creandum-backed Lovable - Maisa argues that its approach is fundamentally different. "Instead of using AI to build the responses, we use AI to build the process that needs to be executed to get to the response - what we call 'chain-of-work," Maisa CEO David Villalón told TechCrunch.
The pair isn't skeptical about AI, but they think it won't be feasible for humans to review "three months of work done in five minutes." To address this, Maisa employs a system called HALP, standing for Human-Augmented LLM Processing. This custom method works like students at the blackboard - it asks users about their needs while the digital workers outline each step they will follow.
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