{"id":2430,"date":"2025-08-28T07:21:49","date_gmt":"2025-08-28T07:21:49","guid":{"rendered":"https:\/\/blog.mogitojournals.org\/?p=2430"},"modified":"2025-08-28T06:56:30","modified_gmt":"2025-08-28T06:56:30","slug":"maisa-ai-raises-25m","status":"publish","type":"post","link":"https:\/\/blog.mogitojournals.org\/fr\/maisa-ai-raises-25m\/","title":{"rendered":"Maisa AI Raises $25M to Tackle Enterprise AI\u2019s 95% Failure Rate"},"content":{"rendered":"<div class=\"wp-block-columns has-ast-global-color-5-background-color has-background is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:100%\">\n<div class=\"wp-block-uagb-container uagb-block-ab8e3be3 default uagb-is-root-container\">\n<div class=\"wp-block-uagb-container uagb-block-153316a4\">\n<p>Maisa AI Raises $25M to Tackle Enterprise AI\u2019s 95% Failure Rate<\/p>\n\n\n\n<p>The promise of generative AI has sparked excitement across industries, but a <strong>recent report from MIT\u2019s NANDA initiative<\/strong> has revealed a sobering reality: <strong>95% of enterprise AI pilots are failing<\/strong>. Many organizations find that their experiments with generative models lead to inconsistent results, unreliable outputs, and costly inefficiencies. Yet instead of abandoning AI altogether, the most forward-thinking companies are pivoting toward a new frontier \u2014 <strong>agentic AI systems designed for accountability and trust<\/strong>.<\/p>\n\n\n\n<p>This is where <strong>Maisa AI<\/strong>, a rapidly growing startup, is making its mark. Founded in 2024, the company has just raised a <strong>$25 million seed funding round<\/strong>, led by European VC firm <strong><a href=\"https:\/\/creandum.com\/\" target=\"_blank\" rel=\"noopener\">Creandum<\/a><\/strong>, to bring its vision of <strong>accountable enterprise automation<\/strong> to life.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Why Enterprise AI Needs a Different Approach<\/h2>\n\n\n\n<p>For enterprises, deploying AI is not about experimentation \u2014 it\u2019s about execution. Rigid RPA (Robotic Process Automation) tools have long been used for automation, but they lack flexibility. Meanwhile, traditional generative AI models often operate as <strong>\u201cblack boxes\u201d<\/strong>, producing answers without transparency.<\/p>\n\n\n\n<p>Maisa AI challenges both models. Instead of generating outputs directly, its system creates <strong>\u201cchains of work\u201d<\/strong> \u2014 structured processes that show exactly how an AI-driven digital worker arrives at an outcome. This makes it possible for businesses to track, audit, and refine AI-driven workflows without losing oversight.<\/p>\n\n\n\n<p>\u201cOur goal is not just to provide answers,\u201d said CEO <strong>David Villal\u00f3n<\/strong>, who co-founded Maisa alongside <strong>Chief Scientific Officer Manuel Romero<\/strong>. \u201cWe\u2019re designing AI that builds the process needed to achieve the right outcome \u2014 with full accountability at every step.\u201d<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">The Technology Behind Maisa AI<\/h2>\n\n\n\n<p>Maisa\u2019s enterprise-first platform, <strong>Maisa Studio<\/strong>, introduces two key innovations that separate it from competitors:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>HALP (Human-Augmented LLM Processing):<\/strong> Instead of letting AI run unchecked, HALP allows users to guide and validate processes in real time. Think of it as a classroom exercise where the \u201cdigital worker\u201d shows its work, and the human can intervene if something looks off.<\/li>\n\n\n\n<li><strong>KPU (Knowledge Processing Unit):<\/strong> A deterministic system that reduces AI \u201challucinations\u201d by enforcing consistent and reliable outputs.<\/li>\n<\/ul>\n\n\n\n<p>These frameworks ensure that <strong>Maisa\u2019s AI agents remain auditable, trustworthy, and scalable<\/strong>. The company has already attracted enterprise clients in banking, automotive manufacturing, and energy \u2014 industries where security, compliance, and trust are non-negotiable.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Funding, Growth, and Market Position<\/h2>\n\n\n\n<p>Maisa AI\u2019s $25 million seed round follows a <strong>$5 million pre-seed<\/strong> investment secured in late 2024, led by San Francisco-based firms <strong><a href=\"https:\/\/www.nfx.com\/\" target=\"_blank\" rel=\"noopener\">NFX <\/a>and <a href=\"https:\/\/www.villageglobal.vc\/\" data-type=\"link\" data-id=\"https:\/\/www.villageglobal.vc\/\" target=\"_blank\" rel=\"noopener\">Village Global<\/a><\/strong>. The new round includes participation from <strong>Forgepoint Capital International<\/strong>, which partnered with <strong>Banco Santander<\/strong>, signaling strong confidence from investors who specialize in regulated sectors.<\/p>\n\n\n\n<p>The startup maintains <strong>dual headquarters in Valencia and San Francisco<\/strong>, giving it both a European foundation and a foothold in the U.S. market. This positioning is key as enterprises across both regions push to scale AI in high-stakes environments.<\/p>\n\n\n\n<p>Maisa plans to expand its team from <strong>35 to 65 employees by early 2026<\/strong> to meet rising demand. Beginning later this year, it expects rapid growth as it starts onboarding clients from its waiting list.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Competing in the Enterprise AI Landscape<\/h2>\n\n\n\n<p>Maisa AI is not alone in its pursuit of accountable automation. Rivals such as <strong>Crew AI<\/strong> and other workflow automation platforms are also vying for enterprise attention. But Villal\u00f3n argues that Maisa\u2019s unique framework offers something competitors lack: a balance of <strong>flexibility and accountability<\/strong>.<\/p>\n\n\n\n<p>In a recent statement, Villal\u00f3n warned that while many AI frameworks promise \u201cquick starts,\u201d they often lead to \u201clong nightmares\u201d when businesses face reliability issues, lack of transparency, or the inability to fix errors. Maisa\u2019s approach aims to prevent these pitfalls by combining <strong>trust, auditability, and scalability<\/strong> from the ground up.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">A New Era of Enterprise Automation<\/h2>\n\n\n\n<p>For organizations navigating digital transformation, Maisa AI offers more than automation \u2014 it offers <strong>assurance<\/strong>. By deploying AI agents that explain their steps, avoid hallucinations, and adapt to specific workflows, Maisa provides a framework for <strong>enterprise AI adoption that is reliable, secure, and future-proof<\/strong>.<\/p>\n\n\n\n<p>\u201cOur mission is simple,\u201d Villal\u00f3n emphasized. \u201cWe are going to show the market that there is a company delivering what AI has promised \u2014 and proving that it works in real-world, mission-critical environments.\u201d<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Final Thoughts<\/h2>\n\n\n\n<p>The road to enterprise-wide AI adoption has been riddled with failures, but <strong>Maisa AI\u2019s $25 million raise signals a shift in focus<\/strong>. With its emphasis on transparency, accountability, and <strong>enterprise automation at scale<\/strong>, the startup is positioning itself as a serious contender in the next wave of AI-driven business transformation.<\/p>\n\n\n\n<p>As more organizations seek to harness AI for high-value, regulated, and complex tasks, solutions like Maisa Studio may prove essential in turning ambitious pilots into successful, long-term strategies.<\/p>\n\n\n\n<div class=\"wp-block-uagb-container uagb-block-ff5518fe\">\n<div class=\"wp-block-uagb-image uagb-block-accfdbf0 wp-block-uagb-image--layout-default wp-block-uagb-image--effect-static wp-block-uagb-image--align-none\"><figure class=\"wp-block-uagb-image__figure\"><img loading=\"lazy\" decoding=\"async\" srcset=\"https:\/\/blog.mogitojournals.org\/wp-content\/uploads\/2025\/08\/Maisa-AI-Worker-builder.webp ,https:\/\/blog.mogitojournals.org\/wp-content\/uploads\/2025\/08\/Maisa-AI-Worker-builder.webp 780w, https:\/\/blog.mogitojournals.org\/wp-content\/uploads\/2025\/08\/Maisa-AI-Worker-builder.webp 360w\" sizes=\"auto, (max-width: 480px) 150px\" src=\"https:\/\/blog.mogitojournals.org\/wp-content\/uploads\/2025\/08\/Maisa-AI-Worker-builder.webp\" alt=\"Maisa AI\" class=\"uag-image-2432\" width=\"1024\" height=\"683\" title=\"Maisa AI\" role=\"img\"\/><\/figure><\/div>\n\n\n\n<p><a href=\"http:\/\/blog.mogitojournals.org\/fr\/\" data-type=\"link\" data-id=\"blog.mogitojournals.org\">Mogito Journals Blog<\/a><\/p>\n\n\n\n<div class=\"wp-block-uagb-social-share uagb-social-share__outer-wrap uagb-social-share__layout-horizontal uagb-block-32385409\">\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-8c1e8d2d\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/www.facebook.com\/sharer.php?u=\" tabindex=\"0\" role=\"button\" aria-label=\"facebook\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg 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Many organizations find that their experiments with generative models lead to inconsistent results, unreliable [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":2433,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","footnotes":""},"categories":[1,15,13,18],"tags":[39,37,48,44,41,50,49,47,36,40,38,42,45,43,35,51,46],"class_list":["post-2430","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","category-ai-artificial-intelligence","category-tech","category-tech-business-news","tag-agentic-ai-systems","tag-ai-accountability-platform","tag-ai-agents-for-banking","tag-ai-digital-workers","tag-ai-workflow-auditing","tag-and-energy","tag-automotive","tag-enterprise-ai-adoption-strategies","tag-enterprise-ai-solutions","tag-enterprise-automation-ai","tag-generative-ai-failure-rate","tag-halp-ai-framework","tag-how-maisa-ai-reduces-enterprise-ai-failures","tag-knowledge-processing-unit-ai","tag-maisa-ai","tag-scalable-and-auditable-ai-for-enterprises","tag-transparent-ai-solutions-for-businesses"],"uagb_featured_image_src":{"full":["https:\/\/blog.mogitojournals.org\/wp-content\/uploads\/2025\/08\/Maisa-founders-David-Villalon-and-Manuel-Romera-1.webp",1200,800,false],"thumbnail":["https:\/\/blog.mogitojournals.org\/wp-content\/uploads\/2025\/08\/Maisa-founders-David-Villalon-and-Manuel-Romera-1-150x150.webp",150,150,true],"medium":["https:\/\/blog.mogitojournals.org\/wp-content\/uploads\/2025\/08\/Maisa-founders-David-Villalon-and-Manuel-Romera-1-300x200.webp",300,200,true],"medium_large":["https:\/\/blog.mogitojournals.org\/wp-content\/uploads\/2025\/08\/Maisa-founders-David-Villalon-and-Manuel-Romera-1-768x512.webp",640,427,true],"large":["https:\/\/blog.mogitojournals.org\/wp-content\/uploads\/2025\/08\/Maisa-founders-David-Villalon-and-Manuel-Romera-1-1024x683.webp",640,427,true],"1536x1536":["https:\/\/blog.mogitojournals.org\/wp-content\/uploads\/2025\/08\/Maisa-founders-David-Villalon-and-Manuel-Romera-1.webp",1200,800,false],"2048x2048":["https:\/\/blog.mogitojournals.org\/wp-content\/uploads\/2025\/08\/Maisa-founders-David-Villalon-and-Manuel-Romera-1.webp",1200,800,false],"trp-custom-language-flag":["https:\/\/blog.mogitojournals.org\/wp-content\/uploads\/2025\/08\/Maisa-founders-David-Villalon-and-Manuel-Romera-1.webp",18,12,false]},"uagb_author_info":{"display_name":"Mogito Journals","author_link":"https:\/\/blog.mogitojournals.org\/fr\/author\/gospeljournals0\/"},"uagb_comment_info":0,"uagb_excerpt":"Maisa AI Raises $25M to Tackle Enterprise AI\u2019s 95% Failure Rate The promise of generative AI has sparked excitement across industries, but a recent report from MIT\u2019s NANDA initiative has revealed a sobering reality: 95% of enterprise AI pilots are failing. Many organizations find that their experiments with generative models lead to inconsistent results, unreliable\u2026","_links":{"self":[{"href":"https:\/\/blog.mogitojournals.org\/fr\/wp-json\/wp\/v2\/posts\/2430","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.mogitojournals.org\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.mogitojournals.org\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.mogitojournals.org\/fr\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.mogitojournals.org\/fr\/wp-json\/wp\/v2\/comments?post=2430"}],"version-history":[{"count":5,"href":"https:\/\/blog.mogitojournals.org\/fr\/wp-json\/wp\/v2\/posts\/2430\/revisions"}],"predecessor-version":[{"id":2441,"href":"https:\/\/blog.mogitojournals.org\/fr\/wp-json\/wp\/v2\/posts\/2430\/revisions\/2441"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blog.mogitojournals.org\/fr\/wp-json\/wp\/v2\/media\/2433"}],"wp:attachment":[{"href":"https:\/\/blog.mogitojournals.org\/fr\/wp-json\/wp\/v2\/media?parent=2430"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.mogitojournals.org\/fr\/wp-json\/wp\/v2\/categories?post=2430"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.mogitojournals.org\/fr\/wp-json\/wp\/v2\/tags?post=2430"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}