AI CRM and ERP integration services for the systems your business already runs on

AI inside the CRM beats AI beside the CRM

The distinction sounds small and decides the whole return on the project.

AI beside the system is a separate tool. Someone has to leave their workflow, open it, paste context in, read the answer, then go back and retype the result. Each of those steps is a place where a busy person gives up, which is why adoption for these tools tends to collapse after the first fortnight.

AI inside the system meets people where they already are. The summary is on the account page. The draft is in the reply box. The extracted fields are already in the form, waiting to be confirmed. Nobody has to remember a new habit, and that is usually the difference between a tool that gets used and one that quietly dies.

Three integration patterns, and when each is right

Almost every engagement we run is one of these three, or a staged path through all of them.

• Read and answer. The model reads records and documents, then answers questions or writes a summary. Nothing changes in the source system. Lowest risk, fastest to ship, and often enough on its own.
• Draft and approve. The model produces something a person would otherwise write, and a person signs it off before it is saved or sent. This is where most of the measurable time saving lives.
• Act within limits. The model calls real actions against scoped credentials, with per-action caps, dry-run testing and an audit log. Worth it for high volume, well understood workflows. Not worth it for anything a regulator will ask you to explain later.

What actually breaks these projects

Rarely the model. Almost always one of these four, so we look for them during discovery rather than during delivery.

• Data quality. Duplicate accounts, free-text fields holding four different conventions, and a status field that three departments interpret differently. A model reading that produces confident nonsense.
• The permission model. If the AI can see records the user cannot, you have built a data leak with a friendly interface. Retrieval has to respect row level access, which means the integration needs to run as the user, not as an admin service account.
• Platform limits. API call ceilings, bulk job windows and governor limits shape the architecture more than any AI decision does. Finding that out late means rebuilding the pipeline.
• No owner. If nobody inside the business owns the workflow being automated, there is nobody to define correct, nobody to review the queue, and nobody to notice when quality drifts.

Where the records sit, and who can see them

Customer records are usually the most regulated data a company holds, so this question comes before the model question.

Three setups cover almost everyone. Provider APIs under a data processing agreement with zero retention, which is quick and acceptable for most commercial data. EU region endpoints on Azure OpenAI, AWS Bedrock or Vertex AI, where requests stay on European infrastructure. Or open weights models on hardware you control, where the records never leave your own network at all.

We are based in Croatia and operate under EU law, so residency and the processing agreement are part of the scope from the first call rather than something legal raises at the end. If your sector has rules beyond GDPR, bring them to discovery week and we will design around them.

What it costs

Ranges, published, because the first thing most people want to know is whether this is a ten thousand euro project or a hundred thousand euro one.

A single read-only workflow against one system runs €12,000 to €30,000. Draft and approve across a CRM and an ERP, with document intake and a review queue, sits between €30,000 and €80,000. Anything involving an on-premise model, a legacy system without a usable API, or strict audit requirements starts higher and is scoped individually.

What moves the price: how many systems have to talk to each other, how clean the data is, whether write-back is in scope, and how exact the output must be. An assistant that drafts is a cheaper build than a pipeline that has to be right every single time, and both are legitimate projects.