ChatGPT or Copilot for Company Knowledge: When Is It Enough?

The honest advice first: if your knowledge is documented, current and easy to find in the connected repositories, an AI assistant with document access is often the right and cheaper choice. Typical cases are manuals, policies, price lists and templates.

In these cases the problem is finding, not the knowledge itself. An assistant that searches and summarizes documents solves that well. These tools are also strong for drafts, translations and research in general knowledge. Anyone starting with Copilot today is doing nothing wrong.

A chat assistant only knows your company once it is allowed to access your repositories. Depending on the product and license, that connection is built in or has to be set up. This belongs at the very start of any selection.

It gets difficult only when the answer is in no document.

Everyday work in the technical Mittelstand, Germany’s industrial SMEs, depends on knowledge that is written down nowhere. Two examples: a foreman hears from the sound that a spindle will be due for replacement in the coming weeks. A field sales rep senses from the buyer’s tone that the deal is not about price. Both are precise, and both are considered obvious, so nobody writes them down.

Standards describe knowledge as a spectrum: from clearly documented to experience-based and never recorded. According to DIN Media, information management processes only take hold once knowledge is in a document. What was never written down falls through that net.

An assistant that reads documents cannot deliver this knowledge because it is not there. That is not a weakness of the tool but a question of the source. hAiner therefore draws it out in conversation, in sessions of 20 to 30 minutes, at the person’s own pace and spread over weeks. You will find more under Securing experiential knowledge.

Even with documented knowledge there is a difference. A search index is a directory that makes documents searchable. A plain document lookup first reads a hit as text. Whether this datasheet belongs to exactly this product, or whether an instruction was replaced long ago, is rarely stated in the text itself.

hAiner does not search documents blindly. Every document is put in relation to everything that makes up your company: customers, equipment, suppliers, orders and employees. On top of that comes the knowledge network that hAiner builds about your operation. The answer therefore rests not on the best text match alone but on the context.

hAiner makes contradictions and gaps visible. In a trial run on the public website of a mid-sized supplier of specialty petrochemical products, one general question was enough. For five products, the datasheets belonged to other products. You can read the case here.

Especially in companies with quality management, the question arises which version of a specification applies and who approved it. A system that only sees the text has few clues for that. If it sees the specification linked to equipment, order and responsible people, it can establish the connection.

How far individual assistants go here today changes quickly. So test it with your own documents, ideally using the checklist at the end of this article.

Source citations alone do not set tools apart, because many assistants display them. What matters is what stands behind the source. For knowledge from conversations, hAiner presents the result to the person for approval. They confirm with one click or correct it in one sentence.

When it matters, this makes the difference. If a successor asks what their predecessor would have done in a given situation, a confirmed answer with evidence is something different from a plausible-sounding text block. Every answer, recommendation and knowledge update in hAiner is documented with timestamp, source and approving person.

With any cloud service, whichever provider, you should clarify three points in writing:

• Location: Where data is stored and processed. When data is transferred outside the EU and EEA, additional conditions apply (Art. 44 ff. GDPR).
• Contract: Whether a data processing agreement is in place, that is, the agreement under which a service provider processes data on your behalf (Art. 28 GDPR).
• Training: Whether your inputs are used to train language models.

hAiner runs in German data centers. On request, we install hAiner on-premise, that is, in your own infrastructure. We do not use your data to train language models, and that also applies to the providers hAiner works with technically. The data processing agreement and the technical and organizational measures are part of every contractual relationship.

This is not legal advice. What is permitted in your case is for your data protection officer and, if necessary, your legal counsel to clarify.

Six steps to judge this yourself in an hour:

1. List the people who will leave in the next three years and check where their knowledge currently lives.
2. Ask an assistant with document access ten questions whose answers you know. Count how many answers are complete and current.
3. Ask a question that only one individual knows the answer to, for example how to fix a nighttime fault on a specific machine. Check whether the assistant finds anything reliable.
4. Ask about a customer and a product at the same time, for example about special terms and the matching datasheet. Check whether the answer connects both correctly.
5. Present two documents that contradict each other. Check whether the assistant reports the contradiction.
6. Clarify location, contract and training in writing with your IT.

If step 1 or 3 reveals a gap, the cause is not the tool but the source. The knowledge still sits in people’s heads, and document access cannot replace it. Copilot and hAiner do not exclude each other: one opens up what was written down, the other secures what lives only in people’s heads.

The Knowledge Loss Check shows how large your risk is: five questions, two minutes, no sign-up. Prefer a conversation? Then book an initial call.