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5 min
Two different visions for document intelligence
AI assistants are multiplying, but few are truly built for enterprise-grade document intelligence.
NotebookLM, launched by Google, targets research and personal productivity. Lettria Knowledge Studio, built in Europe, focuses on regulated industries where governance, accuracy, and explainability are not optional.
This article compares both approaches across technology, scalability, and enterprise readiness.
Two tools, two purposes
NotebookLM is Google’s “notebook-based AI research assistant”. It allows users to upload PDFs, web pages, or notes, then ask questions, summarise, and find insights. The platform is designed for individuals and teams looking to collect and explore knowledge interactively.
Lettria Knowledge Studio addresses a different need. It is a no-code platform that helps enterprises transform unstructured documents into structured, traceable knowledge.
Instead of focusing on productivity, it aims to solve the governance and compliance problems created by large volumes of text in regulated environments (contracts, reports, regulatory filings, or claims documentation).
Two architectures: RAG vs GraphRAG
NotebookLM operates on a standard RAG (Retrieval-Augmented Generation) model: content is embedded, stored, and queried through a vector database.
This enables relevant context retrieval but limits reasoning when documents contain implicit relationships or domain-specific hierarchies.
Lettria’s GraphRAG architecture goes further. It combines vector retrieval with a knowledge graph that models entities, relations, and hierarchies.
This additional layer allows the system to reason across connected concepts, offering better accuracy on complex domain queries. For example, linking clauses, parties, and obligations across long legal or insurance documents.
In practice, this means Lettria can answer “Which clause applies to this policyholder profile?” instead of “Summarise this PDF”.
Scaling and governance
NotebookLM is cloud-native and well-integrated into Google’s ecosystem. It can handle multi-file projects and collaborative notebooks, making it a good fit for general research or educational teams.
Lettria Knowledge Studio is designed for enterprise-scale ingestion.
It can process thousands of documents per batch, automatically identify entities, and enrich them through ontologies built with its Ontology Toolkit.
This ensures consistent terminology across systems, which is critical for compliance and auditability.
Governance is another differentiator. Lettria guarantees data sovereignty, private cloud options, and full control over model behavior.
NotebookLM, even in its enterprise version, remains bound to Google Cloud infrastructure and global data-processing policies.
Performance and transparency
When evaluated on complex questions within specialised domains (for instance, pharmaceutical documentation or insurance clauses), GraphRAG typically yields more consistent and explainable outputs.
The reason is structural: graph-based retrieval preserves semantic links, reducing hallucinations and improving reasoning chains.
NotebookLM has introduced citation tracking to show which source supports each response, a valuable step for transparency.
However, it still operates on unstructured embeddings, which can blur meaning in domain-specific contexts.
Comparison table
In summary
NotebookLM democratizes AI-driven research and personal note-taking.
Lettria Knowledge Studio industrializes it, with a layer of governance, explainability, and scale required by regulated sectors. Here is the type of document you can ingest with Lettria :
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In insurance, finance, or legal contexts, this difference defines what is viable in production.
When accuracy, traceability, and compliance matter, an enterprise-grade GraphRAG platform offers a measurable advantage over general-purpose AI note tools.
Frequently Asked Questions
Yes. Lettria’s platform including Perseus is API-first, so we support over 50 native connectors and workflow automation tools (like Power Automate, web hooks etc,). We provide the speedy embedding of document intelligence into current compliance, audit, and risk management systems without disrupting existing processes or requiring extensive IT overhaul.
It dramatically reduces time spent on manual document parsing and risk identification by automating ontology building and semantic reasoning across large document sets. It can process an entire RFP answer in a few seconds, highlighting all compliant and non-compliant sections against one or multiple regulations, guidelines, or policies. This helps you quickly identify risks and ensure full compliance without manual review delays.
Lettria focuses on document intelligence for compliance, one of the hardest and most complex untapped challenges in the field. To tackle this, Lettria uses a unique graph-based text-to-graph generation model that is 30% more accurate and runs 400x faster than popular LLMs for parsing complex, multimodal compliance documents. It preserves document layout features like tables and diagrams as well as semantic relationships, enabling precise extraction and understanding of compliance content.
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