Plain-English AI Glossary for Business Owners

AI sales pitches are full of jargon that mostly hides simple ideas. This is our plain-English glossary of the terms a Texas business owner actually runs into while deciding whether AI is worth it, what it costs, and how to keep your data safe. Each definition is a sentence or two, no hype and no fake precision — and where a term deserves a deeper answer, we link to the page that goes into it.

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Owning vs. renting AI

The terms that frame the biggest decision: run AI on hardware you own, or rent it from someone else's cloud every month.

Local LLM (large language model): An AI text model that runs on a computer in your own building instead of a remote company's cloud. It is the heart of the own-it model — see what one actually is on the local LLM server page.
On-premise / on-prem: Hardware and software that physically live at your location, which you own and control, rather than rented online. "On-prem" is just the industry shorthand for AI that stays in your building.
Cloud AI: AI you access over the internet and pay for by the month or by usage. The vendor owns the hardware and often sees your data, which is the trade-off the own-it model is built to avoid.
Subscription / SaaS lock-in: Being dependent on a monthly service you cannot easily leave without losing access to your tool or data. It is the quiet cost of renting — and the reason we build hardware you keep.

Cost and deciding

The words that come up when you weigh whether AI is worth it for your shop, and when owning beats paying monthly.

AI readiness: How prepared your business is to actually use AI: a clear use case, organized data, suitable hardware, and a team willing to adopt it. If you are short on these, the honest answer is often "not yet" — which our readiness audit exists to tell you.
Use case: The specific, real task you want AI to help with — such as sorting incoming documents — as opposed to "AI" in general. Picking the one painful, repeatable task is where every good project starts.
Total cost of ownership (TCO): The full three-ish-year cost of a choice, including purchase, power, support, and upgrades — not just the sticker price. It is the honest way to compare a one-time build against a monthly subscription.
Break-even point: The moment an owned system's total cost drops below what you would have paid in subscription fees over the same period. Where it lands depends on usage, so we run the math with you rather than quoting a single number.

How AI works, in business terms

Enough of the "how" to follow a quote or a roadmap — without a computer-science degree.

Inference: The act of an AI model actually answering a question or processing input, as opposed to training it. Day-to-day, the work your staff do with AI is inference, and it is lighter on hardware than training.
Training (a model): Teaching or tuning an AI model on examples so it performs better on your specific work. Most small businesses never train from scratch — they use a strong existing model and point it at their own data.
Fine-tuning: Lightly adjusting an existing model on your own data so it fits your business, without building one from scratch. It is the practical middle ground between using a model as-is and training one.
RAG (retrieval-augmented generation): A setup where the AI looks things up in your own documents before answering, so replies are grounded in your data rather than guessed. It is how a private assistant answers questions about your files — see RAG for business.

The hardware

The physical parts of an owned AI server, and the tools that run on it — the things you are paying for once.

GPU (graphics processing unit): The specialized chip that does the heavy math for AI; the main hardware that makes a local AI server fast. It is usually the most important and most expensive part of a build — more on the local LLM server page.
ECC RAM: Error-correcting memory that catches and fixes small data errors automatically — important for servers that run continuously. It is one of the things that separates a real server from a repurposed desktop.
Redundant PSU: A second power supply in a server that keeps it running if the first one fails — a reliability feature for always-on hardware. It is the kind of detail that matters when a machine is meant to never stop.
Burn-in testing: Running new hardware hard for a stretch before delivery to catch early failures, so it is stable when it reaches your office. It is how we make sure a machine that passes on the bench keeps passing on your desk.
Ollama: A popular, free tool for running local language models on your own hardware in a simple way. It is one of the common building blocks behind a private, in-house AI setup.

The engagement: audit, roadmap, adoption

The steps of a working AI project, and the people side that decides whether it sticks.

AI readiness audit: A structured on-site review that scores whether your business is ready for AI and recommends what, if anything, to build first. It is the gate that keeps you from buying before you are ready — book the readiness audit.
AI roadmap: A plain, sequenced plan that takes you from deciding to use AI through building, installing, training, and measuring results. We frame it as a path to one real win, not a multi-year program — see the AI implementation roadmap.
Change management: The people side of adopting a new tool: training, communication, and making sure staff actually use what you bought. It is the difference between a tool that pays off and one that sits unused.

Governance and privacy

The rules, frameworks, and Texas law that keep AI use safe — explained at a high level, not as legal advice.

AI governance: The rules and accountability for how your business uses AI safely, including who approves tools and what data is allowed. It does not have to be heavy — for a small shop, a short policy is most of the job.
Acceptable use policy (AUP): A short written rulebook telling staff which AI tools they may use and what data they may and may not put into them. It is one of the highest-value, lowest-cost steps a business can take.
NIST AI RMF: The U.S. National Institute of Standards and Technology's voluntary AI Risk Management Framework — a recognized guide for managing AI risk, built around Govern, Map, Measure, and Manage. It is guidance, not a law, and it scales down to a small shop.
TDPSA: The Texas Data Privacy and Security Act, a state privacy law effective July 1, 2024, governing how businesses handle Texas residents' personal data. Many small businesses are partly exempt, but consult counsel for sensitive data — this is context, not legal advice.
Data residency / data-in-the-building: Keeping your information physically on hardware you control, so it never leaves your premises for a third-party cloud. It is the cleanest way to shrink your privacy risk — the core of private AI infrastructure.

Where to go next

Now that the terms make sense, here is where each one leads when you are ready to act:

Not sure AI is worth it yet? Start with an AI readiness audit for a straight build-now / fix-first / not-yet answer.
Sold on the idea but need a sequence? Follow the AI implementation roadmap to one real win.
Want to know what a "local LLM" really runs on? See the local LLM server page.
Need AI grounded in your own documents? Read RAG for business.
Worried about data and privacy? Look at private AI infrastructure.

We translate the jargon into a straight plan

You don't need to master every term on this page — that's our job. We sit at your table in Houston or Fort Bend, decode what matters for your business, and tell you honestly whether to build, fix first, or wait — then deliver and install it on-site from Katy to Fulshear. Check your town on our Texas service areas.

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