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Parse documents into data
Extract structured fields from complex files in minutes. Reduce manual review and speed up downstream workflows.
See how
Lettria outperforms competing tools
Read a detailed analysis of the performance of Lettria's Text Cleaning API vs. that of unstructured.io. See how Lettria compares and outperforms.
Connect with all your sources
GraphRAG's document parser efficiently extracts text from various formats, including PDFs and images, ensuring that all relevant information is captured accurately.
To ensure privacy, your data is safeguarded in a secure environment only you have access to. This data can be conveniently reused across all of your current and future word processing projects.
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Create and manage your datasets
Our dataset manager empowers you to oversee your sources, facilitating seamless collaboration between teams and repurposing data for additional GraphRAG projects across your organization. Transform your datasets into valuable data assets with ease.
Automated Data Structuring
Manually structuring data can be tedious. Lettria automates this task, offering precise parsing tailored to each data type, making it easy to manage complex documents, such as legal contracts or financial reports.
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Do this again and again, at scale
The majority of GraphRAG projects fail before they even get started. By leveraging Lettria's Document Parsing API, you can efficiently convert unstructured data into structured formats, paving the way for deeper analysis and informed decision-making.
Frequently Asked Questions
To explore the platform, you can request a personalized demo through the website. A team member will guide you through the features and help determine how the parser can support your specific use case.
Yes, the parser includes advanced cleaning features, such as:
- Removing HTML tags and headers/footers
- Fixing speech-to-text and formatting errors
- Reconstructing tables and lists
- Replacing special characters and trimming whitespace
These features help ensure your data is as clean and consistent as possible for downstream use.
Lettria’s parser works seamlessly with tools like GraphRAG by transforming raw documents into clean, structured knowledge that can be queried, visualized, or enriched with AI. It's a foundational step in the full document intelligence pipeline.
Yes. Lettria provides a dataset manager that allows you to organize parsed documents, collaborate across teams, and reuse data for different use cases. It turns isolated documents into a consistent, centralized knowledge base.
After processing, the parser returns:
- The full extracted text
- A list of document sections or "chunks"
- Metadata depending on the type of file
This structured output is optimized for follow-up tasks like classification, tagging, or feeding into AI models.
Yes. The parser is built to manage detailed, information-rich content. It automatically extracts key elements from dense documents, making it ideal for use cases like compliance reviews, contract analysis, or financial data extraction.
Your documents are processed in a secure, private environment that’s only accessible to your team. Lettria doesn’t retain or repurpose your data without your consent.
Lettria supports a broad range of formats:
- Text:
.txt,.docx,.odt,.html - Spreadsheets:
.csv,.xls,.xlsx - PDF documents
- Images:
.jpg,.png,.webp - Audio:
.mp3 - Structured data:
.json
This flexibility ensures teams can work with data from nearly any source.
Lettria's parser extracts and structures content from various document formats using a combination of linguistic intelligence and knowledge graph technology. This makes it easier to analyze and repurpose textual data, no matter how unstructured the source may be.
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Patrick Duvaut
Head of Innovation
Patrick Duvaut
Head of Innovation
Patrick Duvaut
Head of Innovation
Start to accelerate your AI adoption today.
Boost RAG accuracy by 30 percent and watch your documents explain themselves.
More accurate answers, fewer hallucinations
Combine graph retrieval with vector search to improve reasoning for complex questions and keep answers grounded in your data.
GraphRAG brings structure to retrieval for answers you can trust.
GraphRAG combines vector search with knowledge graphs to reason over entities and relationships within your data, improving accuracy, grounding, and transparency for complex questions.
Higher accuracy on complex queries
Uses entity relationships and graph structure to retrieve the most relevant context.
Fewer hallucinations, better grounding
Answers are anchored in your knowledge graph, not inferred from loosely connected content.
Explainable reasoning, not black-box retrieval
You can trace how information is connected and understand why an answer was produced.
Trusted by leading teams managing large-scale document knowledge.
