NotebookLM
Turn your documents into an AI research assistant. We help organisations use NotebookLM to make internal knowledge accessible through conversation.
What NotebookLM does
NotebookLM grounds answers in your uploaded sources, reducing hallucination risk and making internal knowledge easier to access.
Document-grounded responses
Answers come from your uploaded sources, not from the model's general knowledge. Reduces hallucination risk.
Citation support
Responses point to specific passages in your documents. Users can verify claims.
Synthesis across sources
Combines information from multiple documents to answer questions that span your knowledge base.
Audio overview generation
Creates podcast-style summaries of your content for a different way to engage with materials.
Use cases for NotebookLM
Research support
Academics, analysts, or strategists working with large document collections. Query papers, reports, and notes through conversation.
Policy and procedure
Making internal manuals, guidelines, and policies accessible through natural questions rather than keyword search.
Training and onboarding
Helping new staff learn from existing documentation without requiring someone to answer every question.
Client knowledge
Professionals managing information about clients, projects, or cases. Quick access to specifics buried in files.
How we help
From evaluation through to broader AI strategy, we help you get practical value from NotebookLM.
Evaluation
Assess whether NotebookLM fits your use case, or whether a custom RAG system would be more appropriate.
Setup and configuration
Prepare and organise your documents for optimal grounding and response quality.
Use case development
Design workflows around NotebookLM that fit how your teams actually work.
Broader strategy
Position NotebookLM within a wider document AI and knowledge management strategy.
Frequently Asked Questions
What is NotebookLM best for?
Document-grounded Q&A, summarisation, and synthesis where the answer must be tied to a known set of sources.
Is this the same as building RAG?
It is related. NotebookLM is a product that provides a document-grounded experience; RAG is an architectural approach you can build into your own applications.
When should we build our own system instead?
When you need deep integration with internal systems, custom user experiences, or enterprise controls that go beyond what an off-the-shelf tool provides.
How do you reduce incorrect answers?
Use curated sources, define which documents are authoritative, validate outputs with citations, and design workflows that encourage verification for higher-stakes decisions.
What happens after a pilot?
If the use case proves valuable, we help you decide whether to operationalise the approach via tooling, a custom RAG application, or a broader knowledge management programme.
Build with NotebookLM
Whether you are exploring NotebookLM or ready to ground your team's knowledge in AI, we can help with evaluation, setup and strategy.