What is LLM integration?
What large language models actually do inside a business, what “grounded in your own data” really means, and how an integration project runs.
The definition.
LLM integration is wiring a large language model into your existing systems and documents so it does real work — summarising, extracting, drafting and answering — grounded in your own data rather than the open internet. The model becomes part of the workflow: input goes in, a checked output comes back.
It is not a chatbot in the corner of your website. Integration means the model is built into the job itself — the report drafts itself from this month's numbers, the contract gets summarised the moment it arrives, the answer comes from your documentation instead of a memory of the internet.
What LLMs do well in a business.
Summarise
Long documents, threads and call notes reduced to the points that matter, in your house style, in seconds.
Extract
The fields you care about pulled cleanly out of invoices, contracts, CVs and forms — into your systems, not another PDF.
Draft
Replies, proposals, reports and product copy drafted from your data and templates, ready for a person to approve.
Answer from your documents
Questions answered from your own policies, manuals and records — with the source shown, so the answer can be checked.
The common thread: language work that used to need a person reading and typing. Where the job is prediction — forecasting, scoring, spotting anomalies — that's machine learning instead; and when the output should trigger real actions across your tools, that's agentic AI.
“Grounded in your own data” — what that means.
Out of the box, a language model answers from what it learned during training — the open internet, roughly. Grounding changes that: before answering, the system retrieves the relevant passages from your approved documents and instructs the model to answer from those, citing what it used.
That single design choice is what turns an impressive demo into a dependable tool. The model's scope narrows to your material, wrong answers drop sharply, and every output can be traced back to a source your team recognises.
On privacy: your data is used under terms where it isn't used to train anyone else's model, and for the strictest cases a private on-premise appliance runs a local model so nothing leaves your premises at all.
Frequently asked questions
Which LLM will we end up using?
The one that fits the job, the budget and your privacy requirements — a frontier cloud model where that's appropriate, or a local model on a private on-premise appliance where data can't leave. The integration is built so the model can be swapped as better ones arrive.
Will our data be used to train someone else's model?
No. Cloud models are used under terms where your text isn't used for training and is only briefly retained. With the on-premise option, nothing leaves your premises at all.
How accurate will it be on our documents?
Grounding keeps the model answering from your material rather than the open internet, and every system is tested against real examples from your work before it goes live. Consequential outputs stay behind a human approval.
What does LLM integration cost?
It depends on scope, so we quote per project after a short call. Most clients start with one job — one document type, one workflow — and grow from there once it's earning its keep.
Do we need LLM integration or machine learning?
If the work is reading and writing language — summarising, extracting, drafting, answering — it's LLM integration. If it's predicting numbers or classifying records, it's machine learning. Many systems end up using both.
Put language intelligence to work.
Tell us the reading-and-writing job that eats the most time. We'll scope what an integration would take — no obligation.