AI that knows your company

The most common complaint about AI assistants is confident but wrong answers. The reason is simple: a general model cannot know your current price list, your internal procedures or the policy that changed yesterday. RAG solves this: before it answers, the AI checks your own knowledge and answers from there. The result is an assistant that knows your company and does not make things up where it does not know.

What is RAG?

Think of an open-book exam. The AI answers from the book in front of it, not from memory. That book is your company's knowledge: your documents, website, policies, product and service information. For the technical definition, see the RAG entry in our Glossary.

What it does for your company

• Accurate, current answers. Answers come from your current documents, not from the general internet. When a price, procedure or policy changes, updating the document is enough.
• Answers with a source. In the systems we build, every answer shows which document it came from. Your teams trust AI by checking it, not blindly.
• You stay in control. Your company knowledge is not built into the AI; you can change or remove it at any time.
• Everyone sees what they are allowed to see. HR files stay with HR, finance reports with finance; each employee gets answers only from information they are authorised to see.
• An assistant that knows what it does not know. If the answer is not in your company knowledge, the assistant does not guess; it hands the work to the right person. Which work stays with the assistant and which goes to whom is clear from the start.

Where it helps

• Customer service. Questions on WhatsApp or your website are answered with your current information, day and night.
• A knowledge assistant for your people. Leave policy, purchasing steps, product details: people ask instead of hunting for the right document.
• Sales and proposals. Product, pricing and reference information reaches the person writing the proposal.
• Onboarding. Company knowledge stops depending on individuals and can be asked from day one.

The setup makes the difference

In enterprise projects the hard part is not the AI but the order of the knowledge: which document is current, who can see what, who updates it when it changes, who takes over when there is no answer? An assistant built before these are answered only spreads scattered knowledge faster. We build RAG together with these rules and measure the results with you every month.

RAG at Globalmeta

• Claude and OpenAI Enterprise Setup. We connect Claude and ChatGPT to your documents and internal systems, so your teams work with your company's own knowledge.
• AI-Native System Design and Solutions. We design and launch custom assistants and AI products that run on your company knowledge.
• HubAI. Part of the hubApps family, HubAI knows your business and answers your customers from your own knowledge. It works on WhatsApp through helloHub and on your website; we build the connection to your CRM and internal systems for you.

FAQ

Do we need to train the AI on our data?

No. The AI stays as it is; your company knowledge sits apart and is used only when a question comes in. That makes updates fast and easy to undo.

What knowledge does it work with?

Your documents, your website and the business systems you give access to. We choose them with you at the start of the project and build the connections for you.

What if it gives a wrong answer?

Because the assistant answers from your company's knowledge, it is far more accurate than a general AI; where the knowledge has no answer, it hands the work to your team instead of making one up. Every month we review unanswered questions and handed-over work with you and strengthen the knowledge.

Where does our data go?

When a question comes in, only the information relevant to it goes to the AI; on enterprise plans this information is not used to train the model by default. We decide together which cloud and which region your knowledge is hosted in.

The first step towards AI that knows your company is agreeing which knowledge serves which work. Let's map it together in a call.