EEnhance your AI with accurate, up-to-date information from your own (private/company) knowledge base using state-of-the-art RAG architecture. Our solutions leverage vector embeddings, semantic search, and large language models to deliver precise, context-aware responses. We support multi-hop retrieval and streaming RAG for faster, more comprehensive answers across complex documents. Easily integrate with popular vector databases like Weaviate, Qdrant, pgvector and FAISS—optimized for scale, security, and real-time updates.
Retrieval-Augmented Generation (RAG) is an advanced AI framework that enhances large language models (LLMs) by integrating them with external knowledge retrieval systems. While LLMs excel at generating responses based on general knowledge, RAG extends their capabilities by dynamically accessing and incorporating up-to-date information from external sources. RAG had its peak in 2023 and 2024 but it remains relevant as a cornerstone of a LLM-solution.
Our RAG implementation follows a sophisticated, multi-stage pipeline designed for maximum accuracy and performance
Key advantages of our RAG implementation for enterprise applications
Dramatically reduce hallucinations by grounding responses in your specific knowledge base with verifiable sources.
Keep your AI's knowledge up-to-date by simply updating your document store, no retraining required.
Every response includes source attribution, enabling verification and building trust with users.
Keep sensitive data in-house with private knowledge bases and on-premise deployment options.
Optimized retrieval and generation pipelines for low-latency, high-throughput production deployments.
Tailor the system to your specific domain with custom embeddings, retrieval strategies, and prompts.
Vector databases enable efficient similarity search in high-dimensional spaces, making them ideal for RAG implementations. They store and retrieve vector embeddings generated by transformer models.
IVFFlat, HNSW, and LSH for efficient search
FP16/INT8/INT4 precision for storage optimization
Enable natural language Q&A over internal documentation, policies, and procedures with proper source attribution and version control.
Deploy AI assistants that provide accurate, up-to-date answers by referencing product documentation and support tickets.
Accelerate research by quickly finding and synthesizing information from large document collections and research papers.
Create intelligent tutoring systems that provide personalized learning experiences based on educational content.
Quickly find relevant case law, regulations, and compliance requirements with accurate citations and references.
Have a unique use case? Let's discuss how we can tailor a RAG solution for your specific needs.
We analyze your requirements, data sources, and use cases to design the optimal RAG architecture.
We process and prepare your documents for optimal retrieval and generation.
We build and fine-tune the RAG components for your specific use case.
We deploy the solution to your infrastructure and ensure it scales with your needs.
Schedule a free consultation to discuss how we can implement a custom RAG solution for your organization.
No commitment required • 30-minute consultation • Customized solution