Enterprise AI Glossary

Coordinating multiple AI agents to work together on complex workflows, each specializing in specific tasks.

Autonomous processes where AI agents perceive, plan, and execute multi-step tasks without human intervention, escalating only exceptions.

Maximum amount of text (in tokens) an LLM can process in a single request.

Numerical representation of text that captures semantic meaning, used for similarity search and RAG.

Data pipeline process for extracting data from sources, transforming it, and loading it into target systems.

Comprehensive AI regulation framework from the European Union categorizing AI systems by risk level.

The ability to understand and articulate why an AI system made a specific decision.

The process of adapting open-weights models (like Llama 3) on proprietary enterprise data to increase accuracy and reduce hallucinations without API leakage.

Process of identifying organizational bottlenecks and time-consuming activities that could be automated with AI.

EU data privacy law requiring explicit consent, right to deletion, and data protection for personal information.

When an AI model generates false or fabricated information that sounds plausible but is factually incorrect.

Workflow design where humans review and approve high-stakes AI decisions before execution.

AI models trained on vast text data to understand and generate human language (GPT-4, Claude, Llama).

Degradation of AI model performance over time as real-world data patterns change from training data.

Personal data such as SSN, email, phone numbers, medical records that must be protected under privacy regulations.

Crafting precise instructions and examples to guide AI models toward desired outputs.

The software kernel that intercepts agent actions and blocks them if they are unsafe or violate governance policies.

Architecture pattern where LLMs query a knowledge base before generating responses, combining retrieval and generation for more accurate, grounded answers.

Ryzolv's architecture for autonomous AI governance - coded into Rflow™ and designed for enterprise compliance. RIF-7 enforces human-in-the-loop gates, logs every decision cryptographically, and blocks unauthorized operations.

Automatic evaluation of an AI decision on a 0-100 scale. High scores trigger human review.

Testing approach where AI systems run in parallel with human operations, making recommendations but not decisions, to validate accuracy.

Architecture where all LLMs, embeddings, and decision logs remain within your corporate firewall or VPC. Zero API calls to external cloud providers (no OpenAI, Azure, or public inference). Equivalent to on-premise AI infrastructure, but cloud-native. All model weights stay under your control.

Unit of text processed by LLMs (roughly 4 characters). Models have token limits and pricing is per-token.

LLM capability to invoke external APIs, databases, or functions as part of task execution.

Ranking AI opportunities from highest-impact to lowest, considering both business value ($$$) and technical complexity.

Specialized database (like Pinecone, Weaviate) that stores embeddings and enables semantic search for RAG pipelines.