NotebookLM for company documents: possibilities and limits
NotebookLM is an AI notebook from Google: you upload sources – such as PDF files or text documents – and ask questions that are answered on the basis of exactly these sources, with references to the sources. For personal research, this is a strong tool. As a knowledge base for the whole company, however, the approach reaches its limits: when working as a team, in data storage and in connecting to existing systems.
In brief
- NotebookLM answers questions strictly on the basis of the uploaded sources and shows where the information comes from – the basic principle resembles a document-based knowledge base.
- Its strength lies in individual research: quickly create a notebook, upload documents, ask questions, generate summaries.
- Notebooks are personally curated collections; a centrally maintained body of knowledge that is binding for everyone is not the concept.
- Processing takes place at a US provider; which terms apply depends on the account type and must be checked before use with company documents.
- Sources are assembled manually per notebook; an ongoing, automatic connection to file storage, email or specialist systems is not the core of the tool.
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 knowledge but from the documents you have loaded into a notebook. With its answers, it shows references to the corresponding passages in the sources – a principle that noticeably increases reliability.
This makes it excellent for getting to grips with a topic: working through a call for tenders, querying a long set of contracts, comparing several reports or generating a summary from documents. Prepared formats such as overviews and study aids are also part of its repertoire.
These strengths deserve recognition: for research by a single person, project-related and with a manageable amount of source material, NotebookLM is among the best low-threshold options currently available.
Where are the limits when used by a team?
A notebook is a personally assembled collection. For individual research this is ideal – for the company it means: every employee builds and maintains their own notebooks, with their own choice of sources and their own state of knowledge. Notebooks can be shared, but a centrally maintained body of knowledge with clear responsibilities is a different concept.
In practice, this creates isolated solutions: the project manager has a notebook on the big installation, the service technician one on repairs – and neither knows which version of the manual is current. The problems of file storage that has grown over time, scattered versions and unclear responsibility, repeat themselves on a new level.
A company-wide knowledge base, by contrast, needs a shared document collection, clear access rules and a maintenance logic that does not depend on individual people.
What about data storage and data protection?
NotebookLM is operated by Google, a US provider. Which commitments apply to the processing of uploaded content differs depending on the account type and contractual framework – and these terms keep changing. Before using it with confidential company documents, this needs to be checked in detail.
For Austrian SMEs, there is also the fundamental question of data storage: if you load customer data, contracts or technical documents into a US tool, you must be able to justify the data protection side properly. A solution with data storage on EU servers and a data processing agreement (DPA) – the data protection agreement on the processing of personal data – makes this justification considerably easier. Added to this is the commitment that customer documents are not used to train third-party AI models.
Important for fairness: this is not a criticism of the tool, but a question of the framework in which it is used. For non-critical research with public material it hardly matters – for confidential company documents it becomes central.
How well does NotebookLM fit into everyday business?
Sources are added to the notebook by hand: select, upload, update if needed. For a project, this is done quickly. As an ongoing operation for company knowledge, however, it means: every new manual and every updated record must be actively added by someone – in every notebook concerned. Outdated versions do not stand out on their own.
The number of sources per notebook is also limited, which forces growing document collections to be split across several notebooks. Automatic, ongoing import from existing file storage, mailboxes or specialist applications is not the core of the tool – NotebookLM is designed as a research notebook, not as connected knowledge infrastructure.
A document-based AI knowledge base takes a different approach here: structured initial loading from existing sources, text recognition (OCR) for scans, automatic tagging and a shared collection that the whole team queries. What this looks like in concrete terms is shown on the junova product page.
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 base | Assembled manually per notebook | Shared, maintained document collection |
| Sources | References to sources | Every answer names the source document |
| Team use | Sharing possible, maintenance stays personal | Shared collection with clear access rules |
| Updates | Manual additions per notebook | Ongoing additions 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 shows: both tools work on a source basis – but one is built for the person, the other for the company.
What does NotebookLM remain a good choice for?
Honestly answered: for a lot. Getting to grips with a new topic, analysing an extensive call for tenders, querying a single set of contracts, preparing a training session from public documents – NotebookLM does all of this quickly and without setup. As a personal research tool it has earned its place.
It only becomes problematic when the personal notebook gradually turns into the company’s knowledge base: when colleagues rely on notebooks whose sources no one is responsible for, and confidential documents are uploaded without a checked framework. That is the point at which the company needs a solution built for the purpose.
How NotebookLM, Copilot, classic DMS and dedicated knowledge bases fit into the overall picture is shown in the big comparison of document management solutions.
What is the next step for your company?
If you already use NotebookLM and are running into the limits described, that is a good sign: the need for company knowledge you can query is clearly real, and your team has already taken on board the principle of source-based AI answers.
The step to a company solution is then not a fresh start but a professionalisation: a shared document collection instead of personal notebooks, EU data storage instead of an unclear framework, a source for every answer for the whole team. How this introduction works step by step – from reviewing the documents and initial loading to the start in the team – is described on the Process page.
Frequently asked questions
May I upload company documents to NotebookLM?
There is no blanket answer, because the terms differ depending on the account type and contractual framework. For documents containing personal data or confidential content, a data protection review is needed before use, because processing takes place at a US provider. For non-critical, public material the hurdle is considerably lower.
Does NotebookLM really answer questions only from my sources?
That is the basic principle of the tool: answers are based on the uploaded sources and show references to the sources. As with any AI, answers can still contain errors – the references are there precisely so you can check statements against the original. You should actually make use of this option to check.
Can my whole team work with one NotebookLM notebook?
Notebooks can be shared, but they remain personally curated collections: the choice of sources, updates and maintenance depend on the person who created the notebook. A centrally maintained body of knowledge with clear responsibilities and access rules is a different concept. This is usually exactly where expanding it into a company knowledge base fails.
What is the biggest difference between NotebookLM and an AI knowledge base?
The operating mode: NotebookLM is a personal research tool with manually assembled sources per 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-critical material it remains a strong tool – for example to get to grips with a new topic or to analyse public documents. The line is drawn where confidential company documents or team-wide answers come into play. That is the job of the centrally maintained knowledge base.