I’m distilling what I’ve learned from building, and defending, AI systems inside a regulated firm into working notes for compliance and engineering teams: how to architect a compliant AI system, from rule-set design to evals to tracing.
Topics in progress:
- Rule sets that police verdicts, not vocabulary
- Evals as a supervisory control (and when a false-negative rate is a finding)
- The audit trail as the unit of accountability
- Why “deterministic pipeline, probabilistic component” beats “aligned model”
- Supervision by design: staffing a two-person team like a twenty-person one
Want to be notified when these publish? Drop me a note.