Storm Parse

Most RAG systems underperform because upstream parsing produces low-quality, context-stripped data, leading to hallucinations and inaccurate retrieval. STORM Parse solves this data-quality bottleneck at the source. In benchmarks, our solution improved RAG answer accuracy by over 30% versus alternatives, reaching peak precision when paired with STORM's proprietary RAG engine.

• Optimized Multi-Method Parsing: Rather than applying a single technique to every file, STORM Parse automatically selects and applies the parsing methods best suited to each document type, delivering consistently higher accuracy across diverse formats.
• Context-Aware Parsing: STORM Parse goes beyond simple text extraction. It reads the full context of a document, understands what each section actually represents, and parses with that meaning intact, preserving hierarchy and semantic relationships the way a human reader would.
• RAG-Optimized Output: Tables, charts, and visuals are transformed into descriptive natural language that is ideally structured for retrieval, dramatically improving downstream answer quality. Each output chunk also retains its original context, avoiding the common failure where data is separated from its headers mid-table during chunking.

It is built for enterprises and developers building RAG pipelines, AI agents, knowledge-base search, and document-intelligence applications. It is particularly powerful for teams working with high-complexity documents, such as:

• Financial reports (e.g., M&A due diligence files and heavily redacted legal contracts)
• Technical manuals (e.g., Industrial Engineering Schematics & P&ID Manuals)
• Research corpora (e.g., STEM research papers laden with complex formulas and multi-dimensional charts)
• Regulatory filings (e.g., Scanned audited financial statements with nested tables and footnotes)

• Agentic Document Parsing
• Context-Preserving Parsing
• Complex Table & Chart Extraction
• Layout & Structure Recognition
• Visual-to-Text Conversion
• RAG Data Preparation