Garbage In,
Garbage Out.
Stop feeding your LLM noisy data. Convert messy PDFs, complex tables, and cluttered web docs into clean, structured Markdown ready for RAG.
Page 1 of 42 (Confidential)
Table 1.1 Summary | Q3 Profit Loss
Jan | Feb | Mar (Total)
120.30 .. 140.00 .. 150.25 (410.55)
Footer: 2023 Corporate Audit Services Inc.
## Q3 Financial Summary
| Month | Amount |
| :--- | :--- |
| January | 120.30 |
| February | 140.00 |
| March | 150.25 |
**Total: 410.55**
The Context Window Tax
Messy data doesn't just lower LLM quality—it costs you tokens and developer time.
Broken Tables
Standard PDF parsers treat tables as raw text strings, causing hallucination spikes during retrieval.
Document Pollution
Headers, footers, and legal disclaimers fill your vector database with semantic noise.
Engineer Burnout
Engineers shouldn't spend 60% of their time writing custom regex for specific document formats.
Smart Table Recovery
Our proprietary OCR-to-JSON engine reconstructs multi-page tables with absolute fidelity, maintaining cell relationships for complex analytical queries.
Semantic Noise-Removal
Stripping ads, sidebars, and navigation menus from web docs automatically.
RAG-Ready Chunking
Semantic chunking designed for 32k to 128k context windows with smart overlap.
Developer First
Deep integrations with the tools you already use. Native support for LangChain, LlamaIndex, and any Python project via our high-performance REST API.
Seamless Integration with your Vector Stack