Ragie

Ellis is a high-tech immigration law firm for businesses and highly skilled individuals. For Ellis, speed and accuracy are essential to building trust with clients. Previously, drafting legal briefs took their team at least a day. By integrating Ragie’s Google Drive Connector, they’ve accelerated the process by 10x, all while maintaining human oversight. “Honestly, it just works. The API is super easy to use, and even the less technical folks on our team find the dashboard fantastic.” – Sampei Omichi, Founder & CEO, Ellis. Read the full story to see why forward-thinking companies like Ellis love Ragie ?? https://lnkd.in/dWRrqCJP?

?? From Data Collection to AI Innovation: Our VC Evolution In an industry that champions innovation but often resists change, we're taking a different approach to venture capital. This week, we're pulling back the curtain on how Nido is reimagining VC operations through AI and automation. Our analysis by Ana Carolina Mexia Ponce reveals the impact: processing 150+ deals quarterly and supporting portfolios through 60+ hours of calls, all enhanced by our AI-powered institutional memory. From simple meeting recordings to a Slack bot powered by Ragie we explore how embracing technology is reshaping our investment decisions and founder support. As always, you're reading ConteNIDO, your go-to place for all things Mexico/US, AI, and innovation. Make sure to subscribe to receive our up-to-date analysis ?? Maria Gutierrez Pe?aloza Renata Solana Carvajal José Luis S. Johan Petersmann Martin A. Mexia Ponce Javier de la Madrid Prieto Renato Picard

In our latest blog by Mohammed, we explore how to deliver fast and contextually relevant results using Hybrid Search. This feature is essential for ?? search-centric applications like knowledge bases and customer support portals. ?? Learn more about the key use cases, technical implementation, and how Ragie makes it easy for developers to integrate Hybrid Search into any application: https://lnkd.in/dTSe7qfH

We're hosting an office hours for developers next friday! Bring your questions or simply drop in to meet with the Ragie engineering team. We're excited to hear about what you're building ?? Register on Discord: https://lnkd.in/dWbxbXKq

As more companies continue to integrate AI, one of the biggest challenges for developers is to ensure that AI-generated content is accurate. This is where Retrieval Augmented Generation (RAG) comes in. By combining LLMs with real-time data retrieval, RAG helps make AI apps more reliable, context-aware, and accurate—all while reducing hallucinations ?? In our latest blog, we explore: * What is RAG, and why it’s a crucial ingredient for AI development * How RAG can improve response accuracy and reduce hallucinations * The benefits of using Ragie, our fully managed RAG-as-a-Service platform, to simplify the process If you're building AI apps and want to ensure they deliver smarter, more accurate content, check out the blog: https://lnkd.in/gDWJuaqY

One of the questions we’ve been getting lately is on how to do citations with Ragie, so we decided to create a cookbook for that. Our new guide walks you through the process of setting up responses with source-backed citations. Now, with just a few lines of code, you can: - Retrieve document chunks relevant to a user query - Generate responses with clear source attributions - Provide clickable citations to specific documents for easy verification ?? Explore the Cookbook: https://lnkd.in/d8jfpp7Z

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We evaluated Ragie's RAG pipeline against FinanceBench - an open source benchmark for financial question answering. On the harder shared vector store configuration, we outperformed the benchmark by 42%!! We picked FinanceBench for its real world applicability. Its a collection of 360 financial documents such as 10-K filings and earning reports from public companies. It consists of 50,000+ pages of dense financial information and include a mixture of structured data like tables and charts with unstructured text, making it a challenge for RAG systems to ingest, retrieve, and generate accurate answers. The evaluation was comprised of answering 150 complex real-world financial questions like "Did AMD report customer concentration in FY22?" Our outperformance of the benchmark by 42% showcases the strengths of our RAG pipeline. - An advanced multi-step document ingestion pipeline which uses a combination of text extraction, OCR and Vision LLMs - A highly sophisticated indexing and retrieval system which uses a combination of semantic, keyword and hierarchical indexes. - A scalable, performant platform which was able to ingest 50,000+ pages of pdfs in a few hrs in the highest extraction mode. If you are looking to use RAG for real world use cases and want to learn more, we wrote a blog detailing our evaluation.

As more companies continue to integrate AI, one of the biggest challenges for developers is to ensure that AI-generated content is accurate. This is where Retrieval Augmented Generation (RAG) comes in. By combining LLMs with real-time data retrieval, RAG helps make AI apps more reliable, context-aware, and accurate—all while reducing hallucinations ?? In our latest blog, we explore: * What is RAG, and why it’s a crucial ingredient for AI development * How RAG can improve response accuracy and reduce hallucinations * The benefits of using Ragie, our fully managed RAG-as-a-Service platform, to simplify the process If you're building AI apps and want to ensure they deliver smarter, more accurate content, check out the blog: https://lnkd.in/gDWJuaqY

We’ve just launched our latest integration that makes it easier than ever to add RAG to your LangChain-powered apps. If you're looking to enhance your AI applications with smarter, more accurate data retrieval, this update is for you! Try it out —> https://lnkd.in/dycjHyBB