Why Your RAG Stack Cannot Pass an Audit
RAG retrieves context; audits demand provenance, determinism, and policy versioning. Why retrieval-augmented generation alone fails regulated decision workflows, and the architecture that passes.
Blog
Insights on AI for regulated industries.
Policy-driven agents, document intelligence, and enterprise automation — from the team building it.
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90 articles
GPT-5.6, Fable 5, GLM 5.2: What Model Churn Means for Enterprise AI Budgets
Four frontier model families shipped in six weeks of summer 2026. What quarterly model churn does to enterprise AI budgets, and why model-neutral architecture is the only stable position.
AI Agents for Construction Lending: Draw Reviews Without the Queue
How AI agents automate construction draw reviews: document reconciliation, budget-line policy checks, photo evidence, and the production results at Built Technologies.
The Edit-Distance Standard: How to Know an AI Agent Has Earned Autonomy
Edit distance, how much reviewers change agent output, is the honest metric for expanding AI agent autonomy. How the Audit, Assist, Automate ladder uses it, and why accuracy alone is not enough.
The 60-Day Agent Deployment, Week by Week
What actually happens in a 60-day AI agent deployment: policy encoding, document calibration, integration, audit-mode shadowing, and the evidence gates that precede production.
MCP for the Enterprise: Governed Workflows as Tools
MCP lets enterprise buyers expose governed workflows as reusable agent tools while preserving the same policies, permissions, validation, and audit trails.
Anatomy of a 99% Agent: Production Accuracy
A 99% AI agent combines deterministic execution, evidence-linked extraction, confidence routing, review gates, and closed-loop correction in production.
AI Agent Pricing Models Compared
Compare per-seat, per-token, per-task, and per-outcome AI agent pricing to see how each model allocates risk and clearly reveals the real cost per decision.
Why Voting Does Not Get AI Agents to 99%
AI agent data shows 2-vote and 3-vote majority voting reduces random errors but reaches a costly floor near 10%. Learn why deterministic execution and review perform better.
What Is a Why-Trail? Audit Trails That Explain Decisions
A why-trail connects every AI agent decision to its policy version, source evidence, evaluated data, timestamps, final outcome, human review, and overrides.
AI Agent Speed Economics: What Cycle Time Is Worth
Cycle time is a third AI agent ROI axis alongside labor and technology cost. Learn how lending, insurance, and operations can price faster turnaround.
Structured Outputs for Enterprise LLMs
Structured LLM outputs make enterprise data parseable with enforced schemas, while evidence pointers, deterministic checks, and review routing make it reliable.