AI-era governance architecture · LawFirm OS · semantic systems · decision infrastructure
I design governance-first systems for AI-era institutions: semantic substrates, exception-learning loops, decision architectures, and audit-ready orchestration patterns.
My current public work is organized around two connected architecture tracks:
- LawFirm OS — a three-part AI governance/runtime/orchestration architecture for law-firm operations.
- Logos / AIRCA / LAIRCA — decision and theological architecture for making assumptions, authority, and governance explicit.
LawFirm OS is a governance-first architecture for law-firm AI systems. It is designed around a simple principle:
Model outputs are not truth. Runtime observations are evidence. Canonical meaning belongs upstream.
The system is organized into three cooperating repos:
| Repo | Plane | Role |
|---|---|---|
| LawFirm OS Semantic Substrate | Control plane | Owns canonical meaning, schemas, registries, route authority, governance boundaries, and validation contracts. |
| LawFirm OS Exceptions Lake Runtime | Evidence plane | Captures validated exception events, audit records, runtime evidence, and governed learning-loop candidates. |
| LawFirm OS Orchestrator | Execution plane | Coordinates models, tools, agents, approvals, policy gates, run ledgers, and evidence packets without redefining canon. |
Semantic Substrate
↓
defines meaning, schemas, routes, policies, and boundaries
Orchestrator
↓
executes bounded model/tool workflows against those contracts
Exceptions Lake Runtime
↓
stores validated runtime evidence, audit records, and learning signals


