Building World's Most Accurate AI-Powered Document Retrieval System: From IBM Watson to Level

As co-founder of il, I've spent 15 years building AI-powered applications, and after four years developing solutions to tackle rag's accuracy limitations, we're proud to introduce the world's most accurate and scalable rag platform using no-code tools and APIs.

1. Name is Ben, co-founder of il, building AI-powered applications for 15 years.
2. Experience at IBM Research, IBM Watson, The Weather Channel, and now at il.
3. Il has built the world's most accurate and scalable RAG (Retrieval-augmented Generation) platform using no-code tools and APIs.
4. Users can upload documents and receive accurate retrievals in minutes.
5. Il was an early user of GPT-3 beta program, using it for RAG applications.
6. High error or hallucination rates (up to 35%) common with RAG, especially when dealing with complicated enterprise documents.
7. Errors are usually due to the quality and relevance of retrieved content, not language models or prompts.
8. Problems typically fall into three categories: bad text extraction, missing information, or non-extracted visual elements.
9. Difficult data engineering problems require hundreds of hours to solve; il has built these solutions into their ingestion pipeline.
10. Users can build accurate RAG applications in just minutes with il's platform.
11. Customers like Air France and Dartmouth have achieved 95% accuracy or higher using il's platform.
12. Il's platform recently outperformed popular solutions by up to 120% on complicated real-world documents.
13. Il's approach focuses on semantic objects and multi-field search across attributes, not vector databases.
14. Semantic objects contain original text and autogenerated metadata for context preservation.
15. Text is rewritten into ideal formats for search and completion purposes.
16. Il's ingestion pipeline includes vision models, multimodal processing pipelines, and dedicated text extraction methods.
17. Context is often lost in chunking during RAG; il's semantic objects maintain the necessary context.
18. Example: Air France uses il's platform for a chatbot co-pilot to understand their 100,000+ documents filled with tables, figures, and text.
19. Il's fine-tuned search models rank results to improve accuracy.
20. Nine or more models are fine-tuned in il's ingestion and search processes for high accuracy.
21. Il's platform enables users to build enterprise-quality, production-ready applications in minutes, not months.
22. Invitation to try il's LLAMA (LLM as a service) Xray for RAG applications.

Source: AI Engineer via YouTube

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