A technology studio that advises and builds at the intersection of AI, engineering governance, and regulated-domain assurance — production AI with verification at its core, and the governance discipline that makes it hold where being wrong is expensive.
Engineering-org design and operating models; AI strategy and governance — turning NIST AI RMF / ISO-42001-class frameworks into real controls, not policy decks; platform and systems architecture; technology M&A diligence.
We design and build production AI — multi-agent workflows, skills, and systems — for real operating use, each with a verification layer at its core. Engineering rigor end to end: L100–L300 requirements, TDD/BDD, domain-driven design.
Non-practice strategy and policy advisory — distinct from the affiliated law practice — plus our own standards-development program: simulation credibility (V&V), a companion AI-maturity standard, and a unified readiness model.
In regulated, liability-heavy work, the hard problem with AI isn't capability — it's trust. A system that's right most of the time is unusable when a single unsupported citation or missed authority carries real consequence. [Un]labs is building a next-generation platform for legal case- and citation-management with assurance engineered into its core: the work isn't just produced — it's evidenced, and it stays under human control. The differentiator isn't generation; it's verification — provenance, source-anchoring, and gates built to the same independent-validation discipline that governs safety-critical systems. We're proving it first where the bar is highest — accuracy-critical legal work — because that discipline transfers directly to the other regulated domains (compliance, healthcare, finance) that share the same shape: confidential, liability-bound, and unforgiving of error.