Governed AI Architecture
How to structure AI systems around policy gates, approvals, source evidence, and operational ownership.
Resources
Practical references for governed AI delivery.
Start here for Anubis Labs solution patterns, security posture, delivery process, and source-backed implementation examples.
Knowledge and Retrieval
Patterns for citation-backed search, RBAC-aware retrieval, evidence quality, and source confidence.
Compliance and Review
Rules-first review workflows for policy checks, document comparison, compliance traces, and human judgment.
Operating Notes
What we optimize for
The useful question is rarely "Can AI answer this?" It is "Can this system answer, act, refuse, and explain itself inside the real operating boundary?"
We design for evidence visibility before model fluency.
High-risk actions should pass through deterministic gates or human approval.
Retrieval quality depends on source authority, access control, and document freshness.
Offline, VPC, and managed-cloud deployment models require different threat assumptions.
Field Guides
Search-ready guides for governed AI decisions
Short, structured explainers built around the questions buyers, security reviewers, and AI lookup systems actually ask.
Governed AI ArchitectureGoverned AI SystemsA governed AI system combines model behavior with access control, evidence retrieval, policy checks, approval workflows, monitoring, and operational ownership.Read guide Knowledge RetrievalEnterprise RAG GovernanceEnterprise retrieval needs source authority, permission-aware search, citation quality, document freshness, and refusal behavior when evidence is weak.Read guide Runtime ControlAI Agent SecurityAI agents need constrained tools, least-privilege credentials, approval gates, egress controls, budgets, and replayable run logs before they touch production workflows.Read guide Compliance IntelligenceAI Compliance ChecklistAI compliance work should combine deterministic rules, evidence capture, human review, risk scoring, and a record of why each decision was made.Read guide Restricted EnvironmentsOffline and VPC AI DeploymentOffline, local-first, and VPC AI deployments trade convenience for stronger data control, predictable network boundaries, and tighter operational responsibility.Read guide AI GovernanceWhat Is Governed AI?Governed AI is AI designed with explicit controls for data access, evidence, review, permissions, policy enforcement, monitoring, and accountability.Read guide EvaluationEnterprise RAG EvaluationEnterprise RAG evaluation measures whether retrieval and generation are grounded, permission-aware, useful, current, and able to refuse weak evidence.Read guide AuditabilityAI Audit TrailsAI audit trails preserve the prompts, sources, tool calls, approvals, outputs, model versions, and reviewer decisions behind important AI-assisted work.Read guide Policy ControlAI Policy GatesAI policy gates are deterministic checks and approval rules that decide whether an AI workflow can answer, act, escalate, or stop.Read guide Review WorkflowsHuman-in-the-Loop AIHuman-in-the-loop AI keeps people responsible for high-risk decisions while AI handles retrieval, drafting, comparison, triage, and evidence preparation.Read guide Industrial KnowledgeAI for EPCM WorkflowsAI for EPCM workflows should focus on standards lookup, project evidence retrieval, proposal support, document review, and governed knowledge reuse.Read guide Pursuit IntelligenceAI for Proposal TeamsAI for proposal teams helps turn RFPs, resumes, project sheets, compliance matrices, and past pursuit material into source-backed response intelligence.Read guide Knowledge ArchitectureRAG vs Knowledge GraphRAG retrieves source material for grounded answers, while knowledge graphs model entities, relationships, and structure that make retrieval more precise.Read guide Document ReviewAI Document Review ControlsAI document review needs controls for source comparison, materiality, reviewer approval, version awareness, and traceable change summaries.Read guide PlanningAI Readiness ChecklistAn AI readiness checklist helps teams decide whether a workflow has the data, ownership, controls, evaluation path, and deployment boundary required for a useful pilot.Read guide
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