NotebookLM for company documents: what it can and can’t do
NotebookLM is an AI notebook from Google: you upload sources – such as PDFs or text documents – and ask questions, which it answers from those sources alone, with references to them. For personal research, it is a strong tool. As a knowledge base for the whole company, however, the approach runs into limits: in teamwork, in data storage and in connecting to existing systems.
In brief
- NotebookLM answers questions strictly from the sources you upload and shows where the information comes from – similar in principle to a document-based knowledge base.
- Its strength is individual research: set up a notebook quickly, upload documents, ask questions, generate summaries.
- Notebooks are collections curated by one person; they are not designed as a centrally maintained body of knowledge that everyone can rely on.
- Data is processed by a US provider; which terms apply depends on the account type and must be checked before you use it with company documents.
- Sources are added by hand for each notebook; ongoing, automatic connections to file storage, email or industry-specific software are not what the tool is built for.
What is NotebookLM and what is it good at?
NotebookLM is a source-based AI tool: unlike a free-form chatbot, it does not answer from its general training data but from the documents you have loaded into a notebook. Its answers include references to the relevant passages in the sources – a principle that makes them noticeably more reliable.
That makes it excellent for getting to grips with a topic: working through a tender, querying a long set of contracts, comparing several reports or summarising documents. It can also produce ready-made formats such as overviews and study guides.
These strengths deserve credit: for one person researching a project with a manageable amount of source material, NotebookLM is one of the best easy-to-use options available today.
Where does it fall short for teams?
A notebook is a collection put together by one person. That is ideal for individual research – but for the company it means every employee builds and maintains their own notebooks, with their own choice of sources and their own version of the facts. Notebooks can be shared, but a centrally maintained body of knowledge with clear responsibilities is a different thing altogether.
In practice, this leads to isolated pockets of knowledge: the project manager has a notebook on the big installation, the service technician has one on repairs – and neither knows which version of the manual is current. The familiar problems of file storage that has grown haphazardly – scattered versions, unclear responsibility – simply reappear at a new level.
A company-wide knowledge base, by contrast, needs a shared document collection, clear access rules and a way of keeping it up to date that does not depend on individuals.
What about data storage and data protection?
NotebookLM is run by Google, a US provider. The commitments that apply to how uploaded content is processed vary with the account type and contract – and these terms change regularly. Before you use it with confidential company documents, you need to check them in detail.
For Austrian SMEs, there is also the fundamental question of where data is stored: if you load customer data, contracts or technical documents into a US tool, you must be able to justify this properly from a data protection point of view. A solution that stores data on EU servers under a data processing agreement (DPA) – the contract that governs how personal data is processed – makes that justification much easier. On top of that comes the commitment that customer documents are not used to train third-party AI models.
To be fair, this is not a criticism of the tool, but a question of the context in which it is used. For non-sensitive research with public material it barely matters – for confidential company documents it becomes central.
How well does NotebookLM fit into everyday business?
Sources are added to a notebook by hand: select, upload, update as needed. For a single project, that is quick. As an ongoing way of managing company knowledge, however, it means someone has to actively add every new manual and every updated report – to every notebook concerned. Outdated versions do not flag themselves.
There is also a limit on the number of sources per notebook, so growing document collections have to be split across several notebooks. Automatic, ongoing import from existing file storage, mailboxes or industry-specific software is not what the tool is built for – NotebookLM is designed as a research notebook, not as connected knowledge infrastructure.
A document-based AI knowledge base works differently: a structured initial load from existing sources, text recognition (OCR) for scans, automatic tagging and a shared collection that the whole team can query. The junova product page shows what this looks like in practice.
How do NotebookLM and an AI knowledge base compare at a glance?
| Criterion | NotebookLM | AI knowledge base |
|---|---|---|
| Core task | Personal research notebook | Central information desk for company knowledge |
| Source material | Assembled by hand for each notebook | Shared, maintained document collection |
| Sources | References to sources | Every answer names the source document |
| Team use | Can be shared, but maintenance stays with one person | Shared collection with clear access rules |
| Updates | Added by hand to each notebook | Continuously added to the central collection |
| Scans and paper | Not a focus | Text recognition (OCR) built in |
| Data storage | US provider, terms depend on account type | EU servers with data processing agreement (DPA) |
The table makes it clear: both tools work from sources – but one is built for the individual, the other for the company.
What is NotebookLM still a good choice for?
To be honest: quite a lot. Getting to grips with a new topic, analysing a lengthy tender, querying a single set of contracts, preparing a training session from public documents – NotebookLM does all of this quickly and with no set-up. As a personal research tool, it has earned its place.
It only becomes a problem when a personal notebook gradually turns into the company’s knowledge base: when colleagues rely on notebooks whose sources nobody is responsible for, and confidential documents are uploaded without any checks on the terms. That is the point at which the company needs a solution built for the job.
The big comparison of document management solutions shows where NotebookLM, Copilot, traditional DMS and dedicated knowledge bases fit into the overall picture.
What is the next step for your company?
If you already use NotebookLM and are running into the limits described here, that is a good sign: there is clearly a real need for company knowledge you can query, and your team has already embraced the idea of AI answers backed by sources.
Moving to a company solution is then not a fresh start but a step up: a shared document collection instead of personal notebooks, EU data storage instead of unclear terms, and a source for every answer for the whole team. The How it works page describes how the introduction works step by step – from reviewing your documents and the initial load to the launch within the team.
Frequently asked questions
Can I upload company documents to NotebookLM?
There is no one-size-fits-all answer, because the terms vary with the account type and contract. Documents containing personal data or confidential content need a data protection review before you use them, because they are processed by a US provider. For non-sensitive, public material the bar is considerably lower.
Does NotebookLM really only answer from my sources?
That is the basic principle of the tool: answers are based on the sources you upload and include references to them. As with any AI, answers can still contain errors – which is exactly why the references are there, so you can check statements against the original. Make sure you actually do.
Can my whole team work from one NotebookLM notebook?
Notebooks can be shared, but they remain collections curated by one person: which sources go in, how they are updated and maintained all depends on whoever created the notebook. A centrally maintained body of knowledge with clear responsibilities and access rules is a different thing altogether – and that is usually where attempts to turn a notebook into a company knowledge base fall down.
What is the biggest difference between NotebookLM and an AI knowledge base?
How they are run: NotebookLM is a personal research tool, with sources assembled by hand for each notebook. An AI knowledge base is a centrally maintained document collection for the whole team – with EU data storage, text recognition (OCR) for scans and a source for every answer. One is a notebook, the other an information desk.
Is NotebookLM still worth using alongside a knowledge base?
Yes – for personal research with non-sensitive material it is still a strong tool, for example to get to grips with a new topic or analyse public documents. The line is crossed when confidential company documents or answers for the whole team come into play. That is the job of the centrally maintained knowledge base.