Maisa AI is a Spanish-American startup building accountable AI digital workers for the enterprise, explicitly positioning itself against the widely cited statistic that the large majority of enterprise AI implementations fail to deliver. Its answer is the Knowledge Processing Unit, or KPU, a proprietary reasoning architecture that sits on top of large language models and converts them into transparent, reliable task executors whose actions can be traced and verified.
Founded in 2024 and based in Valencia, Spain, with a San Francisco presence, Maisa lets business users build agents simply by describing what they want in natural language, without programming expertise or custom training datasets. The emphasis throughout is on trust: agents that explain their reasoning, show their work, and behave predictably enough for enterprises to deploy them on real operational tasks rather than relegating them to experiments.
Maisa raised a $5M pre-seed in December 2024 backed by NFX and Village Global, then closed a $25M seed (about €21.4M) in August 2025 led by early Spotify backer Creandum, with participation from Forgepoint Capital, NFX, and Village Global. The seed was reported as the largest in Spain to date, and the company planned to grow its team substantially while expanding across Europe and North America.
The KPU is central to Maisa's differentiation. By separating reasoning and execution from the underlying model, Maisa argues it can reduce hallucinations and make agent behavior auditable, addressing the reliability and governance concerns that keep many enterprises from putting agents into production. This focus on accountability aligns with the broader market preference in 2025-2026 for agentic products with guardrails and provable behavior.
Maisa competes with other agent-development and digital-worker platforms, but stakes its claim on trustworthiness and ease of authoring. Its pitch to enterprises is straightforward: get the productivity of autonomous digital workers without the unpredictability, with a platform engineered so that non-technical staff can build agents and leaders can trust the results.