AI capability scales through infrastructure. Governance does not. Ludulluu is building an independent infrastructure layer that makes governance scalable, computable, and continuous.
The Missing Layer
Every layer of the AI stack has infrastructure, except governance.
Transformers became the breakthrough that allowed capability to scale. Ludulluu is building the equivalent layer for governance: infrastructure that makes safety structural, measurable, and able to scale with deployment itself.
When governance becomes computable, it stops being episodic policy review and becomes continuous infrastructure. The result is safety that is structural, comparable, and ultimately priceable.
Read the full argument →Built For
Most AI governance still treats deployment as an afterthought. It encodes a static normative state — what is permitted, contested, and out of bounds — from what is known before launch. It is not built to recompute that state as systems operate.
Working with existing governance frameworks, Ludulluu ingests live signals from structured interactions across people, institutions, and AI systems, then continuously computes whether AI systems are operating within agreed conditions, how close they are to their limits, and which direction risk is trending. It does this independently of the systems under evaluation, at the speed and scale of AI deployment.
The authorization state Ludulluu computes is independently derived, continuously updated, and comparable across deployments and labs — sourced from live ecosystem signals rather than the systems under evaluation.
For Labs
A governance credential that travels across every deployment and sets the benchmark every other lab is measured against.
For Regulators
Continuous, comparable observability of risk and alignment, so oversight is informed and balanced across the ecosystem.
For Insurers and Investors
Standardized signals comparable across labs that make AI deployment risk priceable.
Presented at SXSW 2025: “Can AI Be Trained to Be Ethical, and Will It?”