AI glossary for business, in plain English

Most AI jargon is doing one of two jobs: describing something genuinely useful, or making something simple sound expensive. This is the short list of terms you'll actually meet when you look at automating part of your business — defined the way we'd explain them on a call, not the way a vendor's brochure would.

The AI basics

Artificial intelligence (AI)

A broad label for software that does things we used to think needed a human — reading text, answering questions, spotting patterns, making a judgement call. In a business context it almost never means a robot or a "brain"; it means a tool that handles a fuzzy task a rule-based script couldn't.

Large language model (LLM)

The engine behind tools like ChatGPT. An LLM is trained on an enormous amount of text and is very good at predicting what words should come next, which turns out to be enough to draft, summarise, classify and answer. It's the component most "AI" business tools are built on top of.

Prompt

The instruction you give an LLM. The quality of what you get back depends heavily on how you ask, which is why a good prompt library is worth keeping — a small set of tested prompts beats reinventing the wording every time.

Hallucination

When an LLM states something false with total confidence. It isn't lying; it's predicting plausible text and sometimes the plausible answer is wrong. This is the single biggest reason a human reviews anything that matters before it goes out, and why well-built systems keep AI on tasks where a mistake is cheap or caught.

How things get built

AI agent

A system that doesn't just answer once but works towards a goal — it can take a task, decide the next step, use tools, and keep going until it's done. A support agent that reads an email, looks up the order, drafts a reply and files it is doing more than a chatbot. It's the idea behind our AI agents work.

Automation / workflow

A defined sequence of steps that runs without someone doing it by hand — "when a form comes in, create the record, send the confirmation, notify the team." Not every automation needs AI; plenty of the most valuable ones are just reliable plumbing between your tools.

RAG (retrieval-augmented generation)

A way of making an LLM answer from your documents instead of its general training — it retrieves the relevant text first, then writes the answer from that. It's how you get an assistant that knows your policies, products or past matters rather than the whole internet.

Fine-tuning

Further training a model on your own examples so it behaves a particular way. It's powerful but often overkill; for most businesses, a good prompt plus RAG gets you there for a fraction of the cost and effort. We'll tell you when it's actually warranted.

Connecting your tools

API

The doorway one piece of software offers so other software can talk to it — read data, push data, trigger an action. When your CRM, store and accounting tool "integrate," they're using each other's APIs. It's the plumbing that lets a change in one place show up everywhere it should.

Integration

Connecting two or more systems so information moves between them automatically. Done right, it's the thing that kills double-entry — the reason nobody has to copy an order from the store into the spreadsheet into the CRM. It's the core of our API integration work.

Webhook

A way for one system to poke another the instant something happens, rather than the second system having to keep asking. "A payment succeeded — go do the next thing, now." It's what makes automations feel immediate instead of running on a timer.

No-code / low-code

Tools that let you build automations by wiring boxes together instead of writing much code — Make, n8n, Zapier. Great for getting moving and for simple flows; they can get expensive or brittle at scale, which is the trade-off worth understanding before you commit a critical process to one.

Cost and ownership

Per-seat pricing

Software priced per user per month. Fine when a tool fits; painful when you're paying for logins people barely use, which is one of the main reasons a growing team eventually looks at building something it owns outright instead of renting forever.

Token

The unit LLMs are billed in — roughly a few characters of text. It matters because it's how the running cost of an AI feature is measured; a well-scoped task costs cents, and knowing that keeps the "AI is expensive" myth in check.

Vendor lock-in

When leaving a tool is painful because your data, workflows or history are trapped inside it. The antidote is owning the important stuff — your accounts, your database, your code — so switching is a decision, not a hostage negotiation.