Stop re-embedding the whole world. Introducing Raptor Data: The "Git" layer for RAG.
We all know the feeling. You build a RAG prototype, it works beautifully, and you deploy it.
Then the "Day 2" reality hits:
The Bill: Your OpenAI/Pinecone costs start creeping up.
The Maintenance: Users update documents. You have to write script after script to handle versions.
The Inefficiency: You realize that when a user fixes a typo in a 500-page contract, your pipeline is re-embedding all 500 pages.
We realized that the AI industry has solved the "Reasoning" layer (LLMs) and the "Storage" layer (Vector DBs), but the Ingestion Layer is still a mess of spaghetti code.
So we built Raptor Data to be the "Stripe for RAG Ingestion."
🦖 What is Raptor Data?
Raptor is a developer-first API that turns unstructured documents (PDF, DOCX) into version-controlled, AI-ready chunks.
We don't just "parse" files. We treat them like code repositories.
✨ The "Git-Like" Magic (How we save you 90%)
When you upload a file to Raptor Data, we use Structure-Aware Chunking and Fuzzy Deduplication.
If you upload Contract_v2.pdf:
We recognize that pages 2-500 are identical to v1.
We detect that Paragraph 1 has a typo fix.
We return a JSON Diff: { added: 0, removed: 0, unchanged: 500 }.
You pay $0 to re-embed that document.
🔗 Auto-Link: Intelligent Version Detection
Forget to pass the parent_id? No problem.
Raptor uses content fingerprinting and fuzzy metadata matching to automatically detect when a new upload is actually a version of an existing document (e.g., recognizing that Q3_Report_Final.pdf is an update to Q3_Report_Draft.pdf). We automatically link them and calculate the diff, keeping your version history clean without you managing complex ID maps.
🛠Built for the Modern Stack (Vercel/Next.js)
We hated dealing with untyped Python scripts, so we built a first-class TypeScript SDK.
Fully typed responses (no any).
Edge Runtime compatible.
One line of code: const result = await raptor.process(file);
🔒 Enterprise-Grade Security (Zero Retention)
We know you are dealing with sensitive data.
Zip Bomb Protection: We inspect compression ratios before processing.
Anti-Virus Protection: We scan all files against the latest security threats.
Zero Retention Policy: We process files in-memory. We hash them, extract text, and return the data to you. We do not store your raw files at all.
🚀 Try it for Free
We want to prove the tech works. We have a generous Free Tier (1,000 pages/month) so you can throw your gnarliest, most broken PDFs at our parser and see the diffing logic in action.
We are launching officially tomorrow, but I wanted to share this here first.
I'm happy to answer any questions about our hashing algorithms, the TypeScript SDK, or how we handle PDF tables (the enemy of all engineers). Let me know what you think!
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