Articles
Reference articles and Aivan analysis on designing, evaluating and governing enterprise AI, focused on technical and management decisions, security, grounded responses and controllable architectures.
Reference articles on enterprise AI
From AI pilot to production
Build an enterprise AI acceptance plan around real baselines, representative questions, operational ownership, stop criteria and a controlled rollout.
Read full textKnowledge freshness and document validity
Separate publication, effective dates and indexing time; assign content owners and measure synchronization lag so enterprise AI does not rely on obsolete policy.
Read full textEnterprise AI vendor evaluation checklist
Compare AI solutions using representative tasks, access-isolation evidence, operational recovery, full task costs and a demonstrated data-exit path.
Read full textWhat are GEO and AEO?
What actually helps content appear in AI search experiences and Google Search.
Read full textWhat is AI red teaming?
Controlled testing of models and agents for misuse, security gaps and risky behavior.
Read full textFrom AI assistant to agent: toward execution
How enterprises move from suggestions and answers to delegated, controlled agent execution.
Read full textControlling AI-agent cost: tokens, tools and Context
Practical controls for Context, tool calls, retries and multi-step agent execution.
Read full textWhy do grounded AI answers matter?
Citation, Grounding, RAG and source evaluation for enterprise answers.
Read full textWhat is AI Governance? Managing AI
AI governance, ownership, access, evaluation, oversight and risk management.
Read full textContext Engineering: beyond Prompt Engineering
Designing Context, knowledge, tools and permissions for more dependable enterprise answers and agents.
Read full textMulti-agent: when are several agents better?
Multi-agent architecture, the orchestrator role, cost, security and real enterprise use.
Read full textConnect AI to your organization’s knowledge and workflows
With Aivan, approved documents, data and systems can power intelligent assistants and workflows.