Lettria Knowledge Studio vs Custom GPT: Which Architecture Delivers True Enterprise-Grade Document Intelligence?

Compare Lettria Knowledge Studio’s GraphRAG architecture with a Custom GPT to understand differences in knowledge structuring, scalability, and traceability for enterprise document intelligence.

Assia Khan

Nov 18, 2025

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In regulated industries such as pharma, insurance, and legal, accuracy, auditability, and scalability are not optional. When evaluating AI solutions for document understanding, two main approaches emerge: a Custom GPT (a fine-tuned or prompt-engineered large language model) and Lettria Knowledge Studio, powered by a GraphRAG architecture.

Both can process text and generate answers. The difference lies in how they handle knowledge, context, and traceability.

Why GraphRAG Delivers More Than a Custom GPT

Lettria’s Knowledge Studio combines document intelligence, knowledge graphs, and retrieval-augmented generation. Instead of relying only on textual embeddings, it builds a structured graph of entities and relationships, enabling explicit reasoning and traceable results.

1. Performance

In pharma or legal contexts, Lettria outperforms Custom GPTs on complex queries. The graph structure allows the system to reason over explicit links (for example, molecule X → effect Y) rather than relying only on pattern recognition within text.

2. Scalability

A Custom GPT is limited by its input size. It can only ingest a few documents before reaching token limits.

Lettria’s architecture ingests and connects tens of thousands of scientific or regulatory documents. This makes it suitable for large-scale research, compliance, or knowledge management environments.

3. Explainability

Every answer generated by Lettria can be traced back to the nodes and sources in the graph that supported it. This ensures full transparency and auditability, a critical capability for compliance and internal validation.

Comparative Overview

Criterion	Lettria Knowledge Studio (Document Intelligence + GraphRAG)	Custom GPT
Data Structure	Knowledge graph (entities + relations)	Simple vector index (embeddings)
Reasoning Capability	Explicit reasoning on linked facts (e.g. molecule X → effect Y)	Implicit reasoning based on learned patterns
Factual Accuracy	Very high when the graph is well-constructed (fact-based reasoning)	Depends on model context and memory window
Auditability / Traceability	Excellent: every answer can point to its source nodes	Moderate: limited visibility on how the response was derived
Document Scalability	Optimized for large, heterogeneous corpora	Bounded by context size and inference costs
Domain Customization (e.g. Pharma)	High: explicit modeling of regulatory and molecular concepts	Moderate: depends on prompt quality and fine-tuning
Maintenance / Complexity	Requires data pipeline + ontology + LLM orchestration	Low: simple setup via interface or API

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When to Choose Each Approach

Use Case	Lettria Knowledge Studio	Custom GPT
Small datasets, limited compliance constraints	Not optimal	Suitable for quick deployment
Large-scale document ecosystems	Ideal for ingesting and connecting tens of thousands of documents	Limited by token and context size
Need for explainability or regulatory validation	Full traceability through graph-based reasoning	Low transparency, hard to audit
Rapid prototyping or experimentation	Heavier setup, not intended for quick tests	Fast and easy configuration
Domain-heavy reasoning (pharma, legal, insurance)	Explicit modeling of entities and relations	Limited to what the model has seen or been trained on

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Bottom Line

A Custom GPT is useful for simple, text-based applications such as internal search or chat assistants.

But when document volume, complexity, or compliance matter, GraphRAG-based architectures like Lettria Knowledge Studio become essential. Here is the type of document you can ingest with Lettria :

They turn unstructured text into connected, explainable knowledge, enabling enterprises to trust and scale their AI workflows.

Interested in seeing how this works with your own documents?

👉 Request a demo of Lettria Knowledge Studio

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Frequently Asked Questions

Can Perseus integrate with existing enterprise systems?

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.

How does Perseus accelerate compliance workflows?

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. ‍

What differentiates Lettria Knowledge Studio from other AI compliance tools?

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.

Assia Khan

Assia Khan is a versatile marketing professional currently serving as Head of Marketing at Lettria, leveraging her extensive experience in growth strategies and user acquisition across multiple industries to help companies generate actionable insights from text data.