Published
Pick one clear use case
The projects that work start small: answer customer questions, summarise documents, extract data from invoices or route support tickets. Choose a task that is repetitive, measurable and already costs your team time.
Chatbots grounded in your own data
A general chatbot invents answers. A chatbot that uses retrieval-augmented generation, usually called RAG, searches your own documents first and answers from them. This is the usual way to build an assistant for support, internal knowledge or product questions.
Connect AI to the tools you already use
The value comes when AI is wired into your CRM, helpdesk, database or ERP, so it reads real data and writes results back. An assistant that lives in a separate window is rarely used for long.
Check your data first
AI quality depends on data quality. Before building, review where your documents and records live, who can access them and whether they are accurate. Clean, well-organised data shortens the project more than any model choice.
Plan for privacy and oversight
Decide what data may leave your systems, where it is processed and how people review the output. For European businesses this includes GDPR and the transparency expectations around AI. Keep a person in the loop for decisions that matter.
Measure and improve
Define a success metric before launch, such as time saved or tickets resolved without a human. Review real conversations, fix gaps in the knowledge base and improve prompts. A first version that ships in a few weeks teaches you more than a long design phase.