A recent report published by MIT’s NANDA initiative reveals a staggering 95% of generative AI pilots at companies are failing. However, instead of abandoning the technology, the most advanced organizations are now experimenting with agentic AI systems that can learn and be supervised.
This is 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 this 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. The company uses AI to build the process that needs to be executed to get to a response, a concept they call “chain-of-work,” instead of using AI to build the responses directly.
The principal architect behind this process is Maisa’s co-founder and Chief Scientific Officer, Manuel Romero, who had previously worked with CEO David Villalón at Spanish AI startup Clibrain. In 2024, the duo teamed up to build a solution to AI hallucinations after seeing firsthand that you could not fully rely on AI.
The pair is not skeptical about AI, but they believe it will not be feasible for humans to review three months of work done in five minutes. To address this, Maisa employs a system called HALP, which stands for Human-Augmented LLM Processing. This custom method works by having digital workers outline each step they will follow while asking users about their needs, much like students at a blackboard.
The startup also developed the Knowledge Processing Unit, or KPU, a deterministic system designed to limit hallucinations. While Maisa started from this technical challenge rather than a specific use case, it soon found that its bet on trustworthiness and accountability resonated with companies hoping to apply AI to critical tasks. Current clients using Maisa in production include a large bank, as well as companies in the car manufacturing and energy sectors.
By serving these enterprise clients, Maisa hopes to position itself as a more advanced form of robotic process automation that unlocks productivity gains without requiring companies to rely on rigid predefined rules or extensive manual programming. To meet their needs, the startup offers deployment in its secure cloud or through on-premise installation.
This enterprise-first approach means Maisa’s customer base is still very small compared to the millions flocking to freemium vibe-coding platforms. But as those platforms explore how to win enterprise customers, Maisa is moving in the opposite direction with Maisa Studio, which is designed to grow its customer funnel and ease adoption.
The startup also plans to expand with existing customers that have operations in multiple countries. With dual headquarters in Valencia and San Francisco, Maisa already has a foothold in the U.S. Its $5 million pre-seed round last December was led by the San Francisco-based venture firms NFX and Village Global. U.S. firm Forgepoint Capital International also participated in this new round via its European joint venture with Spanish bank Banco Santander, highlighting its appeal for regulated sectors.
Focusing on complex use cases demanding accountability from non-technical users could be a differentiator for Maisa, whose competitors include CrewAI and many other AI-powered, business-focused workflow automation products. The CEO has highlighted an ongoing AI framework gold rush, warning that a quick start can become a long nightmare when you need reliability, auditability, or the ability to fix what went wrong.
Doubling down on its goal to help AI scale, Maisa plans to use its funding to grow from 35 to as many as 65 people by the first quarter of 2026 to meet demand. Starting in the last quarter of this year, the startup anticipates rapid growth as it begins serving its waiting list. The company aims to show the market that there is a company delivering on what has been promised and that it is working.