Multimodal RAG: Documents, Images, Structured Data
Multimodal RAG enterprise systems handle PDFs with tables, scanned images, and database queries. Each modality has its own retrieval pattern. Combine them.
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Tag: NIST AI RMF
Evaluating RAG Quality: Groundedness and Hallucination
Four RAG evaluation metrics drive enterprise AI quality: precision, recall, groundedness, and answer quality. Here is how to measure each one in…
Read ArticlePermission-Aware RAG Architecture for Regulated Firms
Permission-aware RAG enforces identity filtering at retrieval time, not UI render. Where the filter sits, how to model row-level security, and what…
Read ArticleModel Context Protocol (MCP) for Enterprise AI Agents
Model Context Protocol enterprise guide: what MCP replaces, how to secure it under NIST AI RMF and SR 11-7, and which integrations…
Read ArticleMulti-Agent Framework Selection for Regulated Firms
Multi-agent framework selection is a compliance decision first. Score candidates on governance, integration, and operations before developer experience.
Read ArticleAgent Boundaries: Permissions, Thresholds, Escalation
Every enterprise AI agent needs four agent boundaries: data scopes, tool whitelists, confidence thresholds, and escalation rules. Here is how each one…
Read ArticleEnterprise RAG Architecture: The Reference Model
Enterprise RAG architecture adds four layers consumer RAG skips: permission-aware retrieval, multimodal ingestion, groundedness scoring, audit compliance.
Read ArticleAgentic AI for Enterprise: Architecture & Governance
Agentic AI for enterprise works when three layers run together: architecture patterns, agent boundaries, and governance. See how to deploy each layer.
Read ArticleAuditing Agentic AI: Boundaries, Logs, Incident Response
Auditing agentic AI requires permission boundaries per agent, structured tool-call logs, and a rehearsed incident response playbook. Here is each layer.
Read ArticleHuman-in-the-Loop AI Governance: Beyond Rubber Stamps
Human-in-the-loop AI governance fails when reviewers rubber-stamp outputs. Here is the review architecture that makes oversight meaningful under US rules.
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