Maisa Raises $25M to Build Accountable Agentic AI

Why Maisa’s seed and studio matter now

A recent MIT NANDA report landed a blunt statistic: roughly 95% of generative AI pilots are failing. Rather than abandoning AI, some teams are shifting to agentic systems — models that can learn, be supervised, and explain their steps. Maisa AI announced a $25 million seed round led by Creandum and launched Maisa Studio, a model-agnostic, self-serve platform for building auditable digital workers trained in natural language.

Maisa’s pitch is surgical: instead of using AI to directly generate answers, they use AI to construct a process — what they call a "chain-of-work" — that leads to the answer. That process orientation is designed to reduce hallucinations and make outcomes traceable for review and compliance.

Two core innovations back this approach. HALP (Human-Augmented LLM Processing) asks users clarifying questions and has digital workers outline each step before execution, like students solving problems at a blackboard. The Knowledge Processing Unit (KPU) is a deterministic layer intended to limit hallucinations by anchoring reasoning to vetted data.

These features have attracted cautious, high-stakes buyers: large banks, automakers, and energy companies already run Maisa in production. Maisa offers secure cloud and on-prem deployments to meet regulatory and operational needs, positioning itself as a next-generation RPA that avoids brittle rules or heavy manual programming.

What this means for enterprises

The surge of "vibe coding" tools promises quick starts, but Maisa’s founders warn quick starts often become long nightmares when you need reliability, auditability and the ability to correct mistakes. For regulated sectors, deterministic units like KPUs and explicit human checkpoints are practical guardrails that make automation acceptable to compliance teams.

Real-world examples where Maisa’s approach helps:

• Banks: automated AML case triage with auditable step logs and human sign-off.
• Automotive: quality assurance workflows that surface decision trails for recalls and supplier disputes.
• Energy: inspection and incident reports where deterministic checks reduce false positives.

How to evaluate agentic automation vendors

Buyers should look beyond demos. Ask for: clear process maps (chain-of-work), deterministic fallbacks (like KPUs), human-in-loop control points, explainability artifacts, and deployment options (cloud vs on-prem). Measure success with concrete KPIs: error rates, time saved, audit cycle time, and remediation latency.

Maisa’s market is still small compared with freemium platforms, but its enterprise-first focus and funding runway position it to scale in regulated industries. The company plans to double staff to meet demand and start onboarding a waiting list later this year.

At QuarkyByte we watch this shift as a practical evolution: the future of useful AI will be less about magic outputs and more about accountable workflows. Organizations ready to move past failed pilots should pilot agentic workers on well-scoped, high-value processes and require vendors to deliver traceable process logs and correction hooks.

If your team is evaluating automation vendors or recovering stalled pilots, consider mapping your chain-of-work, defining deterministic checkpoints, and prioritizing use cases where auditability matters most. That’s the practical path from experimentation to safe, measurable automation.