AI prepares the record. The human authority decides. And the record proves it.
The market is moving in two directions — AI that decides for itself, or human sign-offs bolted onto one-off systems. HUMID filed the third: one domain-blind architecture in which the human decision is structural, not ceremonial, and which transfers to any domain without rebuilding.
The architecture
HUMID's work concerns adjudication itself — the act of evaluating a record against codified criteria and reaching a determination that a person and an institution must stand behind. Clearances, licenses, benefits, permits, and reviews of every kind share this shape, and they are all now being offered AI systems that promise speed.
In the HUMID architecture, AI assembles and organizes the evidentiary record and emits a structured finding against the governing criteria. The determination itself is reserved to an accountable human authority: the system is structurally incapable of advancing a matter on its own. Every step — what was found, what was decided, by whom, against which criteria — is committed to a tamper-evident, auditable record, so the decision can be inspected long after it was made.
Because the engine is blind to its domain, the criteria change while the architecture does not. The same structure that evaluates one kind of record against one body of rules evaluates any other against any other — without retraining and without rebuilding.
Research questions
What levels of transparency and disclosure does an AI-assisted review require before its conclusions deserve institutional trust?
How are accountability and authorship defined — and proven afterward — when an AI system contributes to a determination a human must own?
What makes an audit record sufficient: not merely present, but capable of carrying the evidentiary burden a disputed decision places on it?
Can one adjudication architecture transfer across domains — from one body of criteria to another — without retraining, and how is that fidelity measured?
Standing
HUMID Technologies LLC, a Delaware company founded in 2026.
Inventor on four U.S. provisional patent applications (2026) covering this architecture. Patent pending.
Registered in SAM.gov for federal financial assistance awards and in the SBA Small Business Registry.
About the founder
Keith Nigro is an independent inventor and researcher and the founder of HUMID Technologies. His background is 25+ years in public service, including a decade administering enterprise information-governance platforms for a state court system and prior work training professionals through institutional change; he holds an M.S. in Higher Education from Drexel University.
The questions above are the questions his work addresses in structural rather than policy terms: systems where AI assembles and organizes the evidentiary record while the determination remains reserved to an accountable human authority, with an auditable record of how each conclusion was reached.
Contact
Research inquiries, standards collaboration, and partnership questions: research@humidtechnologies.com