RAGSys is Crossing Minds new proprietary take on RAG, allowing any team to replace LLM fine-tuning and turning any agents or LLMs into an expert in minutes.
Our platform, built on a cutting-edge stack, delivers real-time, scalable performance for any information retrieval problems.
From Personalization engines tailored to your business KPIs, up to smart agent, our platform is here to help you build, scale and monitor.
Transform the economics of LLM deployment. We focus on LLM agnostic retrieval rather than constant retraining, significantly reducing costs.
Scale seamlessly to handle growing data volumes without proportional resource increases.
Stay cutting-edge with immediate knowledge updates.
Either for real time personaliztion, data enrichment or agent context, we integrate new information in real-time, ensuring your AI models always leverages the latest data.
With our APIs, you gain the flexibility to refine recommendations or deploy your own proprietary algorithms, all supported by an AI-enhanced catalog and a fully managed data pipeline.
RAGSys Value Propositions
Learn about RAGSys’s fast, injection-based in-context learning with the precision of fine-tuning—without the high cost.
RAGSys transcends traditional RAG limitations:
At the heart of RAGSys lies a dynamic, self-improving knowledge base:
RAGSys transcends traditional RAG limitations:
Redefining in-context learning for enterprise LLM deployment:
Increases factually accuracy and stylistic consistency for domain-specific tasks.
Supplements the LLM with task-specific data that are not part of the LLM’s training data.
Some methods are In-Context Learning (ICL), Retrieval Augmented Generation (RAG), and Fine-Tuning.
The table below shows the average NDCG@K for various embedding and reranker models when used to retrieve examples for In-Context Learning for a number of different tasks.
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