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Learn more about our cookie policyEver wondered what happens after you upload a PDF? Here's how AI·Collab transforms your documents into accurate, context-aware AI answers — with every step hosted in the EU.
Large Language Models are incredibly powerful — but they don't know anything about your private documents. When you upload a 200-page contract or a scientific paper, the AI needs a way to find the right information quickly and accurately. This is where RAG (Retrieval-Augmented Generation) comes in. Instead of feeding entire documents into the AI (which would be slow and expensive), RAG finds only the most relevant passages and gives them to the AI as context. The result: faster, more accurate, and more affordable answers. AI·Collab has significantly upgraded its entire RAG pipeline this week — from OCR to embeddings to retrieval. Let's walk through how it works.
RAG stands for Retrieval-Augmented Generation. It's a technique that combines two things: 1. Retrieval — Finding the most relevant pieces of information from your documents 2. Generation — Having the AI write an answer based on those pieces Without RAG, the AI would have to guess or rely on its general training data. With RAG, it can reference your actual documents and give precise, sourced answers.
Think of it like a librarian:
Imagine asking a librarian a question. They don't read every book in the library — instead, they know exactly which shelf to check, pull out the right pages, and hand you the relevant passages. That's what RAG does for AI.
Here is what happens from the moment you upload a document to when you get an AI-powered answer. The process has two phases: document ingestion (one-time) and query-time retrieval (every time you ask a question).
Let's break down each stage of the pipeline. You don't need to understand the technical details — but knowing what happens will help you get better results from your documents.
When you upload a PDF, AI·Collab uses Mistral OCR — the world's most accurate document extraction engine — to read every page. It understands tables, handwriting, mathematical formulas, images, and even JBIG2-compressed scans. The result is clean, structured text ready for the next steps. Mistral OCR achieves 94.9% accuracy and is hosted entirely within the EU by Mistral AI.
A 200-page document can't be processed all at once. AI·Collab splits the extracted text into smaller "chunks" of about 1,500 tokens each (roughly one page). Each chunk slightly overlaps with the next (100 tokens) so that no important context is lost at boundaries. Think of it like cutting a book into organized index cards.
Each text chunk is transformed into a 1,536-dimensional vector — a mathematical representation that captures its meaning. This is done by Azure OpenAI's text-embedding-3-small model, hosted in Sweden Central (EU). Similar concepts end up close together in this vector space, so when you ask a question, the system can find chunks with similar meaning, even if the exact words differ. Embeddings are included for free — no additional credits are charged.
When you ask a question, AI·Collab uses hybrid search — combining two powerful techniques simultaneously. Vector search finds chunks with similar meaning (semantic), while BM25 keyword search catches exact terms and names. This combination ensures that both conceptual matches and specific terms are found. The system retrieves the top 10 most relevant chunks.
The retrieved chunks are then re-scored by a cross-encoder reranking model (BAAI/bge-reranker-v2-m3). Unlike the initial search, this model reads both your question and each chunk together to judge relevance much more precisely. It runs entirely on local servers within the EU — your data never leaves European infrastructure during this step.
The highest-ranked chunks are passed to the AI model as context alongside your question. The AI can now write an answer grounded in your actual documents, cite specific passages, and avoid hallucination. You get a precise answer with sources — not a generic guess.
This week, AI·Collab completed a major upgrade to its embedding and retrieval pipeline. Here's what changed and why it matters for the quality of your AI answers.
| Metric | Before | Now | Change |
|---|---|---|---|
| Embedding Model | all-MiniLM-L6-v2 | text-embedding-3-small | Upgraded |
| Vector Dimensions | 384 | 1,536 | 4× |
| Quality Score (MTEB) | 0.63 | 0.73 | +16% |
| Multilingual Support | Limited | Excellent | 100+ languages |
| Embedding Cost | Local CPU | Free (included) | Included |
| Search Method | Vector only | Hybrid (Vector + BM25) | Better recall |
Most AI platforms use basic vector search. AI·Collab goes further with a multi-stage pipeline designed for accuracy and privacy.
Combines semantic vector search with keyword-based BM25 search. This catches both conceptual matches and exact terms like names, codes, or specific numbers that pure vector search might miss.
A dedicated reranking model (bge-reranker-v2-m3) re-scores every retrieved chunk by reading it alongside your question. This dramatically improves precision — the AI gets the truly most relevant passages, not just approximately similar ones.
The upgraded text-embedding-3-small model produces 1,536-dimensional vectors (up from 384), capturing meaning with 4× more precision. The MTEB quality score jumped from 0.63 to 0.73 — a 16% improvement in retrieval quality.
Mistral OCR extracts text from any document type — scans, tables, handwriting, math — with 94.9% accuracy. It handles JBIG2 compression, complex layouts, and over 100 languages. This foundation ensures the entire pipeline starts with high-quality data.
Data sovereignty is a top priority for European organizations. AI·Collab's entire RAG pipeline is hosted within the European Union — no data crosses the Atlantic at any stage.
All processing providers operate under zero data retention policies. Your documents are processed and immediately discarded by the API providers — nothing is stored or used for training. Azure OpenAI in Sweden Central operates under Microsoft's EU Data Boundary commitment, and Mistral AI is a French company with EU-first data practices.
Unlike many AI platforms that route data through US servers, AI·Collab ensures that your documents, embeddings, and queries stay within European borders at every stage. This simplifies GDPR compliance, procurement, and audit requirements for European organizations.
The RAG pipeline is designed for speed at every stage. Document ingestion happens once when you upload — after that, every query is answered in seconds.
OCR processing time depends on document length. A 10-page PDF typically completes in 5–10 seconds. Embedding, retrieval, and reranking happen near-instantly for the end user.
AI·Collab's RAG pipeline turns your documents into accurate, instant AI knowledge — while keeping every byte of data in Europe. From world-class OCR to 4× more precise embeddings and cross-encoder reranking, every stage has been engineered for accuracy and privacy. Whether you're a legal team reviewing contracts, a research group analyzing papers, or an enterprise managing internal knowledge — your documents are in good hands.
Key Takeaways:
Transform documents into AI-ready knowledge with Mistral OCR. Process up to 1000 pages at just 4 credits per page with 94.89% accuracy.
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