Proof, not promises.
I build the missing layers between AI capability and institutional trust: context, authority, evals, observability, workflows, and receipts.
AI agents are crossing from answers into actions. Once systems act on behalf of people and organizations, the market needs more than capability. It needs context, authority, boundaries, evals, auditability, and proof.
governable ai
Agent assurance for delegated machine action.
proves governance primitives, proof language, authority envelopes, action lineage, revocation, buyer-grade trust artifacts.
helaix
Context infrastructure for human-AI collaboration.
proves context planes, stable records, typed links, permissioned views, tools, receipts, policies, gates, sync.
singulariki
Work / task / skill ontology for the AI era.
proves labor-market mapping, source-backed public intelligence, role/task/tool taxonomy, AI exposure sensemaking.
money pipelines
Federal money intelligence.
proves public-record data product thinking, source-backed analytics, recompete timing, contractor exposure, opportunity graphs.
agentic-u
AI-native operator education.
proves adoption, curriculum design, operator graph thinking, human-AI collaboration training.
grown-up rules
Writing and diagnosis.
proves category language, technical narrative, AI constraint diagnosis, founder-level communication.
- → agent monitoring / observability
- → agent governance / control planes
- → AI identity and authority infrastructure
- → LLM evals and trace analysis
- → context layers / enterprise graphs
- → forward-deployed AI systems
- → AI-native product / category strategy
- → regulated enterprise AI
- → federal / public-data AI products
- ▸ Name the missing primitives before anyone else does.
- ▸ Turn ambiguous agent behavior into product surfaces.
- ▸ Connect buyer trust, technical architecture, and proof.
- ▸ Move from category language to a working system.
- ▸ Ship artifacts people actually use: evals, maps, interfaces, proof packets, workflows, pages, specs.
I'm looking for a full-time, founding, advisory, or fractional role with a team building the infrastructure layer beneath production AI agents.