Many companies have already tried ChatGPT, Claude, or Gemini on their own and seen the benefit. The next challenge is bringing AI into the systems and processes people actually use every day, while keeping company data safe. This is the point where teams start looking for an AI integration service, yet it is not always clear what such a provider actually does.
This article explains the steps of an AI integration project from the start to after launch, your team's role at each stage, and how to assess a provider before you sign a contract.
What an AI integration service does
Put simply, AI integration means connecting a large language model (LLM) to the applications, data, and workflows you already run. The result might be a chat assistant inside an app, data extraction from invoices and contracts, classification of tickets or emails, summaries of long documents, or search over internal documents using RAG.
Most of the effort usually sits around the model. Data has to be prepared, access rights set, answers tested against real cases, and API costs kept in check. A good provider spends a fair amount of time on this part, because it is where the quality of the final result is decided.
Some work runs in the other direction. A team that already uses Claude or ChatGPT may want the assistant to read sales data or prepare drafts directly in an internal system. For this, providers usually build an MCP (Model Context Protocol) server, an open standard introduced by Anthropic in 2024 for connecting AI assistants to tools and data. Access stays permission-bound, and every call is logged.
The steps from assessment to support
The sequence below is common in AI integration projects. The details vary, but each step has a clear purpose.
- Assessment. The provider studies your business processes, existing systems, data quality, and internal rules on confidentiality. The output is a picture of where AI can help and where the risks are.
- Use-case selection. From a handful of ideas, one or two are chosen with the clearest, most measurable benefit. Examples include speeding up supplier invoice entry at a distributor, or helping staff at a notary office find clauses across archived deeds.
- Data preparation. Documents and data are gathered, cleaned, and given access rights. Personal data such as NIK and NPWP numbers should be masked before it is sent to a model.
- Build. The AI feature is built into your existing system, with input and output rules, model selection per task, and usage limits.
- Evaluation. Answers are tested against a set of real examples prepared with your team. Quality is measured, then prompts, data, or flows are refined until the results are acceptable.
- Rollout. The feature is released to a small group of users first. Risky actions, such as changing records or messaging customers, go through a human approval step.
- Support. After launch, answer quality and cost are monitored. AI vendors keep updating their models, so testing needs to be repeated regularly.
The first project is best kept small and measurable. Once one use case is running and its results are clear, the next one is usually easier to decide, because the data, access rules, and evaluation method are already in place.
Your team's role during the project
An AI integration project cannot be handed over entirely to a vendor. Your own people know best what a correct answer looks like. Finance staff know which fields on an invoice are often misread. A clinic admin knows which patient questions can be answered automatically and which must go to a doctor.
Assign one or two people from the relevant unit to help build test examples and review results. Decide, too, who has the final say on when a feature is ready to use. This involvement does not have to be large, but it needs to be consistent from the start through rollout.
Involve future users early as well. AI features designed without listening to staff often go unused, because they do not fit how people work. A short session to try an early version and collect complaints is usually enough to avoid that.
How to assess an AI integration provider
These questions help separate providers ready for real projects from those who mainly shine in demos:
- How they measure answer quality, and whether you can see that evaluation.
- How company data is handled, including masking of personal data and compliance with Indonesia's Personal Data Protection Law (UU PDP, Law No. 27 of 2022).
- Which model they recommend for each task and why, and whether you can switch models later.
- How API costs are monitored and capped so they stay predictable.
- Whether they have built AI features that real users rely on in a running product.
- What support looks like after launch, especially when a model is updated or answer quality drops.
Notice, too, whether the provider is willing to advise against AI for certain parts of the work. Some problems are better solved by fixing the process or by ordinary automation, and an honest provider will tell you so.
AI integration with Averoa
Averoa builds AI features into its own products, such as the Etalasse AI assistant that manages products, stock, and orders by chat. We bring the same experience to client projects, with guardrails, evaluation, and cost control from the start. You can read about the service on the AI Integration page.
