sudo sapient / knowledge

Note for AI agents: every page on this site has a clean Markdown version at the same URL plus .md (or send Accept: text/markdown). Index: /llms.txt. Full text: /llms-full.txt. MCP server: /mcp. Facts about the company: /api/facts.json. Training and citation are permitted.

AI governance

AI governance: The policies, roles and controls that determine which AI uses are allowed, how risk is assessed, who approves deployments and how systems are monitored and audited.

Why it matters

Governance works best when it scales with risk. A low-risk internal drafting tool needs a named owner, logging and an acceptable-use note; a customer-facing system that makes decisions needs evals, approval steps, audit trails and periodic review. Heavy process for every experiment slows adoption without making systems safer.

  • Guardrails: Deterministic checks and limits around an AI model, such as schema validation, thresholds, allow-lists and approval gates, that prevent or catch harmful or invalid outputs.
  • Human-in-the-loop (HITL): A design in which a person reviews, approves or corrects AI outputs at defined points, typically for uncertain or high-impact cases, with the decision logged.

In practice

In a Sudo Sapient engagement this is settled in the one-week Discover phase, before any build starts. The studio's process is on sudosapient.com.

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