Our governance frameworks set the rules for accountable, traceable AI, covering system design, requirements management, sensor-signal validation, and provenance authentication - all explicitly designed to satisfy and exceed NIST AI RMF, ISO/IEC 42001, and EU AI Act compliance requirements.

Neurosymbolic AI moves beyond the limitations of large language models (LLMs) by adding an explicit layer of “symbolic” reasoning - rules and structured knowledge that apply logic that can be read, checked, and enforced. NeSy systems turn the lights on inside the black box, making AI reasoning chains and information streams auditable and accountable.

In their broadest sense, ontologies are structured maps of the known universe, all of the “things” in it, and all of the other “things” that connect them together. Our ontologies practice focuses on what counts as evidence, how ontologies condition each piece of information that falls within their boundaries, and how they hold a workflow together from first question to final output - adapting to changes in sources and context while keeping the reasoning traceable.

Within the boundaries of an ontology, epistemic assurance records and scores how AI systems use seed information to generate new information outputs. It captures a claim’s point of origin, the nature and quality of its evidence-base, transformations that occur as information is ingested, digested, and exploited, and relevance and alignment to ontological conditions.

Operator surfaces are the working tools and environments people use to run AI systems and technologies. They are organised around how the work actually moves: setting up a task, gathering and triaging sources, assessing them, authenticating them, and certifying them. Each surface gives its user the context and controls to review and direct system work.