Turn documents into
AI-ready data.
Convert PDFs and documents into clean Markdown, structured JSON, RAG-ready chunks and schema-validated extraction, built to drop straight into ChatGPT, Claude, Gemini and your vector database. Encrypted, never trained on.
Structure your models can actually use
A raw text dump gives a model noise. We keep the document’s meaning intact, so what lands in your pipeline is structured, chunked and ready to embed.
- Clean Markdown with real headings
- Structured JSON, schema-validated
- Layout-aware RAG chunks
- Tables extracted as real data
- Semantic fields, not just text
- Reading order and hierarchy kept
- Multilingual documents supported
- Encrypted, never trained on
Built for the pipeline
Output that drops straight into ChatGPT, Claude, Gemini and vector stores like Pinecone or pgvector. No cleanup step, no brittle regex, and your source files are never used to train any model.
The parts a text dump loses
Tables, document structure and the specific fields you need come through as data, not a wall of characters.
Tables become data
Real rows and columns extracted to JSON or CSV, so numbers stay computable instead of collapsing into text.
Semantic extraction
Invoices, receipts, resumes and contracts become schema-validated JSON, the exact fields and line items you asked for.
RAG-ready chunks
Split on real structure, not arbitrary character counts, so each chunk is a coherent, embeddable unit of meaning.
Every AI-ready format
Clean Markdown, structured JSON, tables to CSV, and RAG chunks, one document, whichever shape your pipeline expects.
Schema-validated extraction
Invoices, receipts, resumes and contracts to typed JSON, with the fields, line items and clauses you define.
Drop into any stack
Built for ChatGPT, Claude and Gemini, and for vector stores like Pinecone and pgvector. No cleanup, no lock-in.
AI conversions
8 ways to turn documents into AI-ready data. Metered in pages, private every time.