AI Automation Glossary: Plain-English Definitions for Business Owners
AI automation comes wrapped in jargon — RAG, embeddings, vector databases, agents, MCP — that mostly hides simple ideas. This is our plain-English glossary of the terms that actually come up when you automate work with private AI you own, with no hype and no fake precision. Each definition is one or two sentences, and where a term has a deeper answer we link to the guide that explains it.
Automation and workflow terms
The words for the plumbing that wires your tools together and runs the work — start here. Our business AI automation page puts these to work.
- Workflow automation
- Wiring software steps together so a trigger — a new email, a form submit — sets off a chain of actions without a person clicking through each one. It's the foundation everything else on this page builds on; see business AI automation.
- n8n
- An open-source, self-hostable workflow automation platform. You can run it on your own server so your data and logic stay in your building — which is why it's the orchestration engine we reach for first.
- Orchestration
- Coordinating multiple steps, models, or agents so they run in the right order, pass data between each other, and handle errors. It's the difference between a one-off script and a reliable workflow.
- Webhook
- A way for one app to instantly notify another when something happens. It's the most common way to trigger an automation the moment a lead, order, or message arrives.
- Human-in-the-loop
- An automation design where a person reviews or approves the AI's output before it takes a consequential action. It's how you get the speed of automation without handing over judgment on the calls that matter.
Agents and how AI takes action
The terms that separate a chatbot that talks from an agent that does — covered in depth on AI agents for business.
- AI agent
- An AI system that doesn't just answer, it acts: it can decide to use tools — search a database, send an email, call an API — to finish a task with little or no hand-holding. See AI agents for business.
- Agentic workflow
- A process where one or more AI agents carry out multi-step work, choosing what to do next rather than following a fixed script. It trades some predictability for the ability to handle messier, branching tasks.
- Tool calling (function calling)
- A model's ability to call a predefined function or tool, like a database query, and use the result in its answer — the core of turning a chatbot into an agent. Our MCP and tool calling guide goes deeper.
- MCP (Model Context Protocol)
- An emerging open standard for connecting AI agents to tools and data sources in a vendor-neutral way, so you can swap frameworks without rewriting integrations. See MCP and tool calling.
Knowledge and RAG terms
How AI answers from your documents instead of guessing. This is the knowledge layer behind a private AI chatbot; RAG for business is the full explainer.
- RAG (retrieval-augmented generation)
- A technique where the AI first retrieves the most relevant chunks of your documents and then writes its answer from them, so responses are grounded in your data and can be cited. It's the heart of a private assistant — see RAG for business.
- Embedding
- A list of numbers that captures the meaning of a piece of text, so a computer can find other text with similar meaning even if the words differ. It's what makes search by meaning possible — more in vector databases and embeddings.
- Vector database
- A database built to store embeddings and search them by meaning rather than by exact keyword; the memory behind a RAG system. See vector databases and embeddings.
- pgvector
- An extension that adds vector search to PostgreSQL, letting you keep one database for both your app data and your AI search. It's a common starting point when you're already on Postgres.
- Qdrant
- A dedicated open-source vector database known for strong price-performance and scaling, with quantization to keep large indexes on affordable hardware. It's the step up when a dataset outgrows a general-purpose database.
- Chroma
- A lightweight, developer-friendly vector database that's easy to start with for prototypes and smaller datasets. It gets a project moving quickly before you commit to a heavier store.
- Chunking
- Splitting documents into smaller passages before embedding them, so retrieval returns focused, relevant pieces. Good chunking is quietly one of the biggest factors in answer quality.
- Reranking
- A second pass that re-scores the retrieved chunks for relevance, so the best material reaches the model first. It sharpens answers when the first retrieval pass is close but not perfectly ordered.
- Hybrid search
- Combining keyword search and meaning-based (vector) search to catch both exact terms and related concepts. It covers the cases where one method alone would miss the right passage.
Models and where they run
The terms for running the AI itself on hardware you own — the whole point of a private stack. The servers pillar covers the hardware it runs on.
- Local LLM
- A large language model that runs on hardware you control, so prompts and data never leave your network. It's the privacy foundation of everything in this pillar.
- Ollama
- A tool that downloads, manages, and runs open large language models locally with one command, handling GPU and memory details for you. It's the simplest way to stand up a local LLM on your own box.
- Quantization
- Compressing a model or embeddings to use less memory and run on smaller hardware, usually with a small, manageable quality trade-off. It's how a capable model fits on affordable hardware.
- Open WebUI
- A self-hosted chat interface for local models, with built-in RAG and source citations, often used as the front end for a private assistant. It's a common face for a private AI chatbot.
- AnythingLLM
- A self-hosted application for chatting privately with your documents, organizing them into separate workspaces with hybrid search and citations. It's an alternative front end when you want workspace separation.
Document processing terms
How AI reads invoices, forms, and contracts and pulls out what you need — see document automation.
- OCR (optical character recognition)
- Turning an image or scanned page into machine-readable text so it can be processed automatically. It's the first step before any document workflow can act on a scan.
- IDP (intelligent document processing)
- OCR plus AI that not only reads a document but understands its structure to pull out the specific fields you need — invoice total, due date, vendor. It's what turns a scanned pile into clean data; see document automation.
Where to go next
Now that the terms make sense, these guides put them to work:
- Start with business AI automation — how we map your busywork and automate it on a server you own.
- Want AI to answer from your own documents? Read RAG for business, then the technical companion on vector databases and embeddings.
- Curious how AI agents actually do things? See AI agents for business and MCP and tool calling.
- Need a chat assistant grounded in your files? Browse the private AI chatbot page.
- Wondering about hardware or privacy? The servers pillar covers the box it runs on, and the security pillar covers keeping it private.
We turn the jargon into automation you own
You don't need to master every term on this page — that's our job. Tell us the busywork, and we'll map the workflow, pick the model and the stack, and build it on a server you own, installed on-site across Houston, Sugar Land, Richmond and the Fort Bend area. See our Texas service areas.
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