AI Assistant for Company Knowledge: Answers From Your Data

A new employee asks the same experienced colleague for the third time today: where do I find the current price list? How did warranty work for this product? Have we ever made a similar quote for this customer? The answers exist. They are in manuals, quotes, contracts, minutes and old emails. Nobody can find them quickly.

An AI assistant for company knowledge addresses exactly this. This article explains what such an assistant can and cannot do, how it answers only from your own documents, what happens with data protection and access rights, and which use cases are good for getting started.

When knowledge lives in heads and folders

In many small and medium-sized companies, knowledge depends on a few people. That is not a problem until these people are on holiday, fall ill or retire. Typical signs:

• Onboarding takes a long time because new employees learn much of it only by asking.
• Experienced colleagues are constantly interrupted, often with the same questions.
• Documents exist but are scattered: on network drives, in SharePoint, in Google Drive, in email inboxes.
• Search finds file names, not answers. If you do not know what the document is called, you will not find it.

An internal AI assistant changes the way people search: instead of looking for files, employees ask a question in plain language and get an answer with a reference to the right document.

What an internal AI assistant can and cannot do

An AI assistant for company knowledge is a chat program that answers your employees' questions based on your own approved documents. It can:

• understand questions in plain language, even if the terms in the document are different
• find and summarise relevant passages across many documents
• name the source so people can read up on it
• answer in German and English, even if the document is in the other language

What it cannot or should not do:

• invent knowledge that is not in any document. A well-configured assistant then says honestly that it found no answer.
• make binding decisions, for example on goodwill gestures, prices or legal questions
• tell outdated documents from current ones if nobody maintains them. The quality of the answers depends directly on the quality of the documents.

How the AI answers only from your documents

The technology behind it is called RAG (retrieval-augmented generation). Put simply, it works like this:

1. Preparation: your approved documents are split into small sections and prepared so the AI can quickly find passages that match in content.
2. Question: an employee asks a question, for example "Which maintenance intervals apply to machine X?".
3. Search: the system looks for the sections that match the question, and only in the documents this person is allowed to see.
4. Answer: the language model formulates an answer from these sections and names the source, such as "Maintenance manual, chapter 4".

The key point: the AI is not "trained" with your data. For each question it only receives the matching passages as a basis. If you change a document, the new version applies from the next preparation run.

Data protection, access rights and storage location

Company knowledge is often confidential. That is why these questions belong at the start of every project:

• Access rights: the assistant should respect the same rights as your file storage. Anyone who may not open a folder gets no answers from it either. Contracts, HR files or salary data stay protected that way.
• Storage location: the prepared documents can be stored with a cloud provider in the EU or on your own servers. For particularly sensitive data, a language model on your own hardware is also possible.
• Data processing agreement: if the documents contain personal data and you use an external AI service, you need a data processing agreement under Art. 28 GDPR.
• No training of other models: check in the contract terms that your data is not used to train the provider's models. With business plans this can usually be excluded.

On the EU AI Act: since 2 February 2025, Article 4 applies, under which companies must take measures to support their employees' AI literacy. For an internal assistant this mainly means: employees should know how it works, where its limits are and that they check answers against the source for important decisions. Find out more on our page about the EU AI Act.

Good first use cases

The best areas are those where the same questions come up again and again and searching takes longer than answering:

• Technical manuals and maintenance documents in industry, engineering and the trades
• Internal policies, for example on travel expenses, holidays or IT use
• Product and pricing knowledge for sales and customer service
• Previous quotes and project reports, to calculate similar cases faster
• Onboarding new employees, who can ask questions without interrupting anyone

Example: an engineering business makes several hundred pages of manufacturer documentation searchable for its service technicians on their phones. On site, the technician asks about an error code and gets the right passage with the page reference instead of calling the office.

If you already use Microsoft 365, Microsoft 365 Copilot can also be an option that accesses your documents in SharePoint and OneDrive. A custom assistant tends to pay off when your knowledge is spread across several systems, you want full control over storage location and model, or you want to build it into your own applications.

How to start: a pilot in four steps

An assistant for the whole company is not a good first step. A small pilot that quickly shows whether the benefit materialises is better.

1. Collect area and questions: choose an area, such as customer service, and have the team note down for two weeks which questions they ask internally. This list is your benchmark.
2. Tidy up documents: remove outdated versions and decide which documents are valid. This often creates value on its own, regardless of the AI.
3. Test phase with a few users: a small group uses the assistant in day-to-day work and rates the answers. Wrong or missing answers show where documents are missing or unclear.
4. Evaluate and expand: if the assistant answers the collected questions reliably and noticeably saves time, more areas and users are added.

Decide in advance how you will measure success, for example how many of the collected questions are answered correctly and with a source. That way you decide based on your own figures, not on gut feeling.

Costs, funding and getting started

A sensible start is a clearly defined area with a manageable number of documents and a group of test users. At EA Digital Solutions, AI integration starts from €2,500. If several sources such as SharePoint, Google Drive and a CRM are connected, system integration from €4,500 is added (as of October 2026). Running costs for the language model depend on usage.

If you do not yet know which area is best suited, an AI potential analysis for €890 helps. Together we check which documents and questions suit an assistant and what benefit is realistic. Find out more on our page on AI consulting in Graz. Funding for AI projects, for example from aws (Austria Wirtschaftsservice, the federal promotional bank), runs through time-limited calls; you will find an overview under AI funding in Austria.

Your next step

In a free initial consultation we talk about your documents and the questions your team asks most often. Afterwards you know whether an assistant is worth it and what a first step could look like. Feel free to bring a short list of typical questions and tell us where your documents are stored today. Our office is in Graz, and remote collaboration works just as well.