LLM integration services for products that have to run in production

LLM integration is not the same thing as a chatbot

A chatbot is a surface. LLM integration is plumbing. The difference matters commercially, because one of them can be bought for a few hundred euros a month and the other is a software project with a budget and a delivery date.

A chatbot sits next to your product and answers questions about it. An integration lives inside the workflow. It reads the ticket, pulls the customer record, drafts the reply in your tone, checks it against policy, and writes the result back to the system your team already uses. Nobody opens a chat window. The work simply happens faster.

If a vendor quotes you a flat monthly fee to add AI to your business, you are buying a surface. That is fine when a surface is what you need. It is the wrong purchase when the value sits in a process that already runs on your own data.

The model call is roughly a tenth of the work

Calling an LLM takes an afternoon. Everything around the call is what takes weeks, and it is also what decides whether the feature is still running in six months.

• Structured outputs, so downstream code receives typed data instead of prose it has to parse and hope about.
• An eval suite that runs in CI, so a prompt edit on a Tuesday cannot quietly degrade quality for every customer.
• Prompt versioning with a one line rollback, because the fix you shipped at 2am will sometimes need undoing at 9am.
• Cost ceilings per request, per session and per customer, so a retry loop cannot turn into a four figure invoice overnight.
• Fallback chains across providers, because rate limits arrive without warning and your feature still has to answer.
• Observability that shows latency, spend and failure rate per feature, not one aggregate number for the whole account.

When you should not integrate an LLM

We turn down this work more often than you might expect, and usually for one of four reasons.

• The task has a correct answer that a rule or a query already produces. A model adds cost, latency and doubt to something deterministic.
• Nobody can describe what a good output looks like. Without that, there is nothing to evaluate against and no way to tell improvement from noise.
• The underlying data is a mess. A model reading bad records produces confident bad answers, which is worse than no answer at all.
• The process is genuinely low volume. If a person handles it twelve times a month, automation will cost more than it saves for years.

Where your data goes, and why EU teams keep asking

This is the first question from most regulated buyers, so here is the direct answer. You choose the boundary, and we build inside it.

Three boundaries are common. Provider APIs under a data processing agreement with zero retention enabled, which is the fastest path and fine for most business content. EU region endpoints on AWS Bedrock, Azure OpenAI or Vertex AI, where the request never leaves European infrastructure. Or open weights models running on hardware you control, in your own VPC or on premise, where no third party ever sees the payload.

The third option costs more to build and more to run. It is the right call when the content is patient data, legal files, source code, or anything where a processor relationship is a problem rather than a formality. We are based in Croatia and work under EU law, so the contract side is not an afterthought either.

What it costs

Published ranges, because a page about a service that hides its numbers wastes everyone's time.

A single LLM feature inside software you already ship runs €10,000 to €25,000. That covers discovery, the integration itself, structured outputs, an eval suite and cost telemetry. Multi feature work with observability and fallback chains sits between €25,000 and €50,000. Running the models on your own hardware is a private AI build, which starts at €100,000.

Three things move the number more than anything else: how clean your data is, how many systems the feature has to touch, and whether the output needs to be right every time or merely right most of the time. Compliance grade accuracy is a different project from a helpful draft, and it is priced that way.