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AI integration and AI agents

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Integrating AI into a business means connecting a language model to your data and your tools for a specific, measurable task: answering customers from your documentation, extracting information from documents, qualifying requests or chaining actions in your software. ITEmpire Group designs these solutions, from proof of concept to production.

The problems we solve

  • Teams spending hours copying information from one tool to another.
  • Repetitive customer questions whose answers already exist in your documents.
  • Documents (invoices, purchase orders, CVs, contracts) to read and re-enter.
  • Incoming requests to sort and forward to the right person.
  • An internal knowledge base that is hard to search.

What we build

  • AI assistants on your website, your app or your internal tools that answer from your own content.
  • Document search (RAG): the model first finds the relevant passages in your documents, then writes an answer that cites its sources.
  • Document extraction and classification: reading PDFs and images (OCR), extracting the useful fields, classifying.
  • Process automation with n8n or Make: forms, e-mails, CRM, WordPress, spreadsheets and APIs connected together.
  • AI agents that choose among authorised tools (look up an order, create a ticket, draft a reply) to complete a task.
  • AI integration into your existing web and mobile applications, through APIs.

Chatbot, automated workflow or agent: what is the difference?

SolutionHow it worksWhen to choose it
Conversational assistantAnswers questions; does not act on your systems.Support, FAQs, searching documentation.
Automated workflowFollows fixed steps defined in advance; AI can handle one step (summarise, classify, extract).Repetitive, predictable processes. It is the most reliable and least expensive solution when it is enough.
AI agentChooses the tools and the order of actions itself to reach a goal, within set limits.Variable tasks that require consulting several sources or deciding the next step.

We always recommend the simplest solution that meets the need: a deterministic workflow before an autonomous agent.

Security and control

  • Least privilege: an agent only accesses the tools and data its task requires.
  • Human approval before any sensitive action: sending an e-mail, a payment, a deletion, changing customer data.
  • Prompt injection protection: external content (e-mails, documents, web pages) is treated as data, never as instructions, and the possible actions are restricted upfront.
  • Logging of questions, sources used and actions taken, to check and correct.
  • Confidentiality: model provider and hosting chosen according to data sensitivity; no personal data sent without a legal basis.
  • Execution limits: number of steps, budget and duration capped per task.

From proof of concept to production

  1. ScopingUse case, available data, measurable success criteria and confidentiality constraints.
  2. Proof of conceptA quick test on a real sample of your data to check that the approach works.
  3. PrototypeA first version used by a few people, with a reference set of questions to measure quality.
  4. IndustrialisationIntegration with your tools, error handling, security, monitoring and documentation.
  5. Follow-upMeasuring answer quality, costs and usage; regular improvements.

Costs and timelines

The budget depends on the volume and quality of your data, the number of tools to connect, the level of autonomy you want and the usage volume. AI models are generally billed per use: we estimate this running cost during scoping and monitor it afterwards. Every project is quoted individually.

Further reading

Do you have a process to automate or an AI use case in mind? Describe it in a few lines: we reply within 24 hours with a first approach.

Request a free quote Contact us

Frequently asked questions

How do I integrate artificial intelligence into my business?

Start with a specific, frequent and measurable task (answering recurring questions, processing documents, sorting requests). We check the available data, test the approach on a real sample, then integrate it into your tools with human approval for sensitive actions.

When should you use an AI agent rather than classic automation?

When the task varies from one time to the next and requires choosing between several tools or sources. If the steps are always the same, an automated workflow (for example with n8n) is more reliable, easier to maintain and cheaper.

Is my data sent to an AI provider?

It depends on the solution chosen. We select the provider and hosting according to data sensitivity, limit what is sent to the strict minimum and document the data flows.

How much does an AI project cost?

The cost depends on the data, the integrations, the level of autonomy and the usage volume, plus the per-use billing of the models. We often start with a proof of concept with a narrow scope. Describe your need in the quote form to get an estimate.

How do you prevent AI from making up answers?

By having the model answer from your documents (RAG) and cite its sources, by measuring quality against a reference set of questions and by planning human approval wherever an error would have consequences.

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