Data Infrastructure for Enterprise RAG
Retrieval-Augmented Generation breaks when faced with real-world enterprise file complexity. We provide massive, structurally parsed datasets—covering messy PDFs, multi-tab spreadsheets, and complex legal contracts—designed strictly to benchmark and train Document AI.
Beyond Plain Text: The Structural RAG Challenge
Most foundation models excel at reading clean Markdown, but enterprise RAG systems ingest messy formats: nested tables spanning PDF pages, footnoted legal contracts, and financial spreadsheets. When OCR or parsing fails, the generation step hallucinates.
Our RAG and OCR Datasets are paired files. You receive the raw enterprise artifact (e.g., a native or scanned PDF) alongside a mathematically verified JSON/XML structural extraction. This allows ML teams to benchmark parsing libraries, train visual-language models, and ensure chunking algorithms respect logical document boundaries.
LAYOUT PRESERVATION
Ground-truth spatial coordinates (bounding boxes) for titles, paragraphs, tables, and signatures in document images.
METADATA RICH
Datasets come tagged with document taxonomy (invoice, memo, schematic) and synthetic QA pairs for evaluation.
RAG Target Dataset Categories
| COLLECTION TYPE | FILE FORMATS | RAG / ML APPLICATION |
|---|---|---|
| Financial & Invoice | Scanned PDF, TIFF, JSON | Key-Value extraction, OCR Table Parsing Benchmarks |
| Legal & Contractual | DOCX, Native PDF | Clause retrieval, Semantic Chunking eval, Risk AI |
| Technical Schematics | PDF, CAD outputs, Visio logs | Multi-modal RAG, Diagram understanding |
| Cybersecurity Logs | CSV, TXT, JSONL, YAML | Threat intel RAG, Cloud architecture analysis |
Upgrade Your RAG Evaluation
Contact us to request sample JSON manifests and spatial bounding box overlays.