RSpace™ Infrastructure: Advanced Retrieval with Iris.ai Multi-RAG System

Unlock the Power of Advanced Accurate Retrieval with our RAG-as-a-Service

The world's most accurate RAG system. Revolutionize your AI infrastructure with our highly adaptable, secure multi-RAG solution that leverages advanced embeddings, multiple retrieval methods, and automatic query optimization to deliver precise, contextually relevant information across diverse business applications.

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RSpace™

ADVANCED EMBEDDINGS

MULTIPLE RETRIEVAL METHODS

FULLY SCALABLE

SECURE DEPLOYMENTS

Our Multi-RAG is an advanced system, for advanced end user applications.

FACT 01

HYBRID RETRIEVAL APPROACHES

Iris.ai's Multi-RAG system stands out by offering a more advanced and flexible approach to document retrieval and content generation compared to typical implementations.

A RAG should not be just a vector database using cosine similarity to find similar documents - not if you have a real business case to apply it to. Our Multi-RAG system integrates various retrieval methods, including vector-based retrieval, graph traversal, fingerprinting, and keyword searching. An automatic mechanism selects the best approach based on the query type, optimizing performance for different kinds of questions.

FACT 02

RICH EMBEDDINGS

The token based embeddings native to the LLMs are limited; our more detailed embeddings offer richness, adaptability and far superior answers.

Native LLM embeddings are limited by their token-based nature, creating compressed spaces that lack fine-grained distinctions. They struggle with domain-specific adaptation and rely on less effective similarity metrics like cosine similarity. Our approach creates richer, domain-specific embeddings with finer granularity and better contextual understanding. Optimized through advanced techniques, our embeddings are more accurate and adaptable for specific business cases.

FACT 03

ADVANCED SIMILARITY METRICS

For business applications in very succinct domains, commonly used similarity metrics such as cosine are not going to be sufficient for questions where fine-grained differentiations are needed.

Cosine similarity lacks the granularity needed for domain-specific tasks and struggles with nuanced queries. Our RAG system uses advanced metrics to identify subtle similarities, providing superior accuracy and adaptability. By incorporating multiple retrieval methods and domain-specific embeddings, our system offers better precision and performance compared to cosine-based approaches.

FACT 04

DECISION MAKING SYSTEM

Our RAG system features intelligent, automated processes that seamlessly identify the nature of each query to determine which retrieval method to deploy.

Our RAG system features intelligent, automated processes that seamlessly identify the nature of each query, whether short, long, or entity-based. By dynamically selecting the most effective retrieval method—whether it's keyword search, graph traversal, or another advanced technique—our system ensures every question is answered with precision and accuracy, tailored perfectly to the user's needs. It's the smart, behind-the-scenes "magic" that powers exceptional, context-aware responses.

FACT 05

PRIVACY AND SECURITY

The Iris.ai Multi-RAG system can be deployed both on cloud and on premise with high levels of privacy and security.

Our RAG system is designed to meet the highest standards of security and privacy, offering flexible deployment options including SaaS, dedicated SaaS instances, private cloud, and on-premise solutions. Trusted by clients with stringent compliance needs—such as NATO and leading firms in pharma, engineering, and patent management—we have over nine years of experience handling of sensitive data at scale, and we are well-versed in ensuring data integrity and privacy.

FACT 06

LLM EVALUATION FRAMEWORK

Our RAG solutions can be deployed with the LLM of your choosing and fine-tuning. If you are not sure of what LLM to use for your business case, we have a metric-driven LLM Evaluation Framework to help decide.

Evaluating Large Langugage models for real world business cases - not just generic scoreboards - is a complicated task. We have developed a powerful LLM Evaluation Framework

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Iris.ai's RAG system

RAG IS NOT JUST RAG.

The RAG system stands out by offering a more advanced and flexible approach to document retrieval and content generation compared to typical implementations. A key differentiator lies in the use of rich, domain-specific embeddings that provide better quality and contextual relevance, far surpassing common vector database models that rely on more generic token embeddings. The system also utilizes domain adaptation, allowing it to create tailored embeddings for specific business cases, making it highly adaptable to individual user needs. In addition to enhanced embeddings, the system employs a hybrid retrieval approach, integrating multiple methods for extracting and generating relevant information. This includes traditional vector-based retrieval, graph traversal techniques, fingerprinting, and keyword searching, ensuring comprehensive coverage for various types of user queries. The system is equipped with an automatic mechanism that selects the best retrieval approach based on the type of question being asked, optimizing performance for short and long queries, as well as overview-type questions. Another unique aspect is the use of the RV coefficient instead of the more common cosine similarity. This improves the system's ability to determine the degree of similarity between queries and documents, especially within specific domains where granular differentiation is necessary. Lastly, the RAG system is designed with scalability and security in mind, offering both on-premise and cloud-based deployment options, making it suitable for businesses requiring strict control over data privacy. This combination of rich embeddings, advanced similarity metrics, flexible retrieval methods, and customizable deployment makes the RAG system highly effective across different industries and use cases.

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