RAG is an architecture that augments a language model with a retrieval step. Instead of relying on knowledge the model absorbed during training, RAG retrieves relevant documents from a private knowledge base at query time and provides them as context for the model's response. This means answers are grounded in your actual documents rather than the model's general knowledge, which may be outdated, generic, or simply wrong about your specific domain. Lumia AI achieves 98% grounding accuracy because every answer is traceable to a retrieved source.

Hallucination is when an AI model generates confident-sounding but factually incorrect responses, inventing details, misattributing information, or extrapolating beyond what it actually knows. In enterprise contexts, hallucination means wrong answers to questions about internal policies, products, or procedures, which can have serious operational or compliance consequences. RAG prevents hallucination by constraining the model to answer only from retrieved documents: if the information isn't in the knowledge base, the model is designed to say so rather than invent an answer.

A generic AI assistant (like consumer ChatGPT) is trained on public internet data and provides general-purpose responses. An enterprise AI copilot is connected to a company's private knowledge, internal documentation, policies, product information, customer data, and is configured to behave according to the company's specific use cases, personas, and guardrails. It knows what a generic assistant doesn't: your product catalog, your internal processes, your customer history, your compliance requirements.

A Model Registry is a centralized repository that tracks every AI model in production: which version is deployed, on what data it was trained, what its performance metrics are, who approved it, and when it was last validated. Without a registry, organizations lose track of which model is running in which system, can't roll back to a previous version when performance degrades, and can't audit AI decisions against the model that made them. At scale, managing AI without a registry is like managing software without version control.

A general-purpose chatbot is designed to handle any question, producing a response regardless of whether it has accurate information. A knowledge agent is designed for a specific domain and knowledge base, it retrieves from curated sources, stays within its scope, and escalates or declines when asked something outside its knowledge. For enterprise use, this specificity is essential: a knowledge agent that says 'I don't have information on that, please contact HR' is more valuable than a chatbot that invents a plausible-sounding but wrong HR policy.

Governance built-in means the platform natively provides: audit logs of every query and response (who asked what, when, and what answer was given), access controls that determine which users or roles can access which knowledge bases, content filtering that prevents the AI from producing outputs that violate company policy, response attribution that links every answer to its source documents, and monitoring dashboards that track usage, accuracy, and anomalies. These controls can't be added as an afterthought, they must be designed into the platform architecture.

Public AI tools are appropriate for general productivity tasks, drafting, summarizing, brainstorming, using only public information. An internal AI layer becomes necessary when: the use cases require access to proprietary company information, regulatory requirements prohibit sending business data to third-party services, the company needs auditability of AI interactions, consistent behavior governed by company policy is required, or the AI needs to be integrated with internal systems rather than operating as a standalone chat interface.