Why this exists
AI work is only useful if it reduces operational friction without leaking sensitive data. This initiative keeps AI experiments grounded in local-first principles, clear inputs and outputs, and repeatable pipelines that can be audited.
Measures of success
- AI workflows run locally or within approved boundaries.
- Inputs and outputs are documented with clear provenance.
- Retrieval pipelines include guardrails and quality checks.
- AI experiments graduate into reusable patterns when they prove value.
Active projects
Key risks
- Data privacy exposure through uncontrolled ingestion.
- Unreliable outputs without validation and grounding.
- Experiments expand without clear operational value.