Before the journey,
the vocabulary.
Four short primers for the visitor who arrived before the journey, the buying guide, and the compliance pages made sense. What generative AI actually is. What workload patterns most deployments fit into. What a proof-of-concept is for. And the glossary you'll hear thrown around in any serious deployment conversation.
Plain English. No hype. Read in any order.
Most AI projects stall on the words.
Reading the deployment journey or the infrastructure buying guide assumes you already know what an LLM is, what fine-tuning means, and what an agentic workflow actually does. A lot of visitors don't yet - and a lot of AI projects that fail in their first six months fail because the people specifying them weren't fluent in this vocabulary.
These four primers fix that. Each one is short. Each one is concrete. None of them sells you anything - the whole point of the foundations track is that you finish it able to talk to the right partner about the right thing without anyone in the room exploiting an ambiguity in your understanding.
Once you have the vocabulary, the rest of the AI Solutions cluster reads cleanly. If you already have the vocabulary, skip this track and use the cluster map below to jump where you actually need to go.
Four pages, in any order.
Each primer is independent. The glossary is the one to keep open while you read the others. If you only have time for one, read the workload-patterns page - it's the vocabulary that scopes every later conversation.
Primer 01
What generative AI actually is
A neutral primer on large language models - what they do, what they don't do, where they are reliable, where they are not. Open-weight versus proprietary, local inference versus API. Vendor-agnostic; safe to read before you talk to anyone selling you anything.
For the visitor who
"You've heard the terminology and want it demystified before the first internal meeting."
Primer 02
AI workload patterns
The five patterns most enterprise AI deployments fit into - retrieval-augmented generation, chat / assistants, agentic workflows, fine-tuning, and classification - with the infrastructure footprint each pattern implies. The vocabulary you need to describe what you actually want.
For the visitor who
"You know AI can help, you don't yet know what shape the deployment should take."
Primer 03
From PoC to production
The validation gate that most enterprise AI projects stall at. What a proof-of-concept is for, what it cannot tell you, why a successful PoC does not always graduate, and what has to be true before the gate opens. Honest about the failure rate.
For the visitor who
"You're considering a PoC, or you're past one and trying to figure out whether to keep going."
Primer 04
Glossary
Fifty-plus terms you will hear in any serious AI deployment conversation, defined in plain English. LLM, RAG, fine-tuning, LoRA, quantisation, embeddings, agentic, MCP, MLOps, drift, evaluation, prompt injection. Stable anchors so partners can deep-link.
For the visitor who
"You want one tab open while you read everything else."
Foundations come before the journey.
The AI Solutions cluster has four content layers. Foundations is the bottom; the rest assume it. Every primer cross-links to the next layer once it has done its job.
You are here
Foundations
/ai-solutions/foundations
You are here. The vocabulary.
Layer 02
The journey
/ai-solutions/deploying-ai-in-your-business
Six phases from discovery to production.
Layer 03
The buying guide
/ai-solutions/ai-infrastructure-buying-guide
Five stages of investment without throwing early spend away.
Layer 04
Private AI for sensitive data
/ai-solutions/private-ai-for-sensitive-data
When sovereign deployment is non-negotiable.
LM TEK is not your AI consultancy.
These pages exist because we are tired of watching organisations specify AI deployments without the vocabulary to do it well. They are not the start of a sales process for our hardware. They are not an attempt to replace the AI consultancy you eventually need.
We build the engineered hardware foundation that sovereign AI deployments sit on. Everything above the hardware - model selection, fine-tuning, evaluation, ongoing operations - is partner work, and we will recommend the right partner for your situation when you reach the routing CTA at the end of any of these pages.
The reason the foundations track exists at all is that customers who arrive at our partners' tables fluent in this vocabulary get better outcomes. Our partners say so. So do their customers.
Ready when you are.
Read one primer or all four. When you're ready to talk to a real partner about a real deployment, the routing form on the AI Solutions hub takes your situation and comes back with a partner shortlist that fits.