What "AI integration" actually means for a normal company
A large language model (LLM) on its own is just a clever text engine — it writes well, but it knows nothing about your company. Integration is the step that wires it into your world: your documents, your e-shop, CRM, support tickets, internal wiki.
In practice this isn't "drop in a chatbot and we're done." It's connecting the model to sources you already have and placing it inside a tool people actually use — the support window, your internal Slack, the order admin.
The key difference from the public chats you know: we work with your data, under your control. You decide what the model can access, what it may and may not do, and where its answers go. It isn't a toy bolted on top — it becomes part of how the company runs.
So it doesn't invent things: answers from your own documents (RAG)
The biggest worry about AI is a fair one — models sometimes state nonsense with full confidence. The fix is called RAG (retrieval-augmented generation), and it's the backbone of almost every integration we build.
It works simply: before the model answers, the system searches your real documents — manuals, price lists, contracts, procedures — and hands the relevant passages to the model as its source material. The model then answers from what's in front of it, not from memory. It can attach the source too, so a person can check where the answer came from.
That changes trust at the root. Instead of "maybe that applies," you get "this is in your policy, section 4." And when the answer isn't in the documents, a well-tuned assistant says so — it doesn't paper over the gap with a guess.
Where it fits: support, internal "ask the company," drafting
The most common first use is a copilot for customer support. It doesn't have to answer for the agent — it's enough to draft a reply from your knowledge base. The agent reviews it and sends. Handling a ticket drops from minutes to seconds.
The second strong case is an internal "ask the company." Instead of digging through dozens of PDFs, a new hire or your accountant asks in plain language — "what's the process for a claim over 500 euros?" — and gets an answer with its source.
The third area is drafting: quotes, emails, summaries of long documents, meeting notes. Here AI doesn't save the decision, it saves the clicking and the blank page. A person gets a sensible first draft and edits, rather than starting from zero.
Guardrails and accuracy — keeping the model on a leash
Integration isn't just "plug in an API." A large part of the work is setting guardrails so the assistant behaves predictably.
Concretely: we limit what it may talk about (off-topic gets a polite refusal), force it to cite a source, define tone and boundaries (no promises of prices or commitments it can't back), and log conversations so you can see how it behaves. For sensitive actions — a refund, an order change — we leave the last word to a human.
And we test it like software: we build a set of real questions and check whether the answers hold up before it ever reaches customers. Accuracy isn't set once and forgotten — it's something we measure and tune over time, based on where the model trips up.
Where it isn't worth it — and when to wait
We'll be straight with you: AI isn't the answer to everything. If you have a process with exactly five rules and zero ambiguity, plain logic or a form will handle it cheaper, faster and more reliably than a language model. Adding AI there is expensive over-engineering.
It also makes no sense to build an assistant on top of content that doesn't exist or is a mess. If company knowledge lives in people's heads rather than in documents, the source material has to be written down first — otherwise RAG has nothing to draw on and the model is just impressively empty.
And for decisions where a mistake is costly — law, health, finance — we keep AI as a helper, not an authority. It prepares the groundwork, suggests, summarizes. The human decides. That's why we always start with a small, well-bounded case that genuinely saves time, and only expand once it has proven itself.