RAG combines large language models with enterprise data sources to deliver accurate, explainable, and up-to-date AI responses. By grounding AI in trusted documents, databases, and APIs, RAG ensures reliable answers while keeping sensitive data secure.
Unlike traditional LLMs that rely on static training data, RAG retrieves verified information in real-time to provide trustworthy AI responses for business-critical applications.
Our RAG systems use a robust and scalable technology stack combining AI, vector databases, and secure integrations.
GPT, Gemini, Claude, and enterprise-grade open-source models for reasoning and generation.
Pinecone, FAISS, Weaviate, Chroma — efficient embeddings storage and fast retrieval.
Semantic & hybrid search, filtering, reranking, and context management.
Connects with internal systems, cloud data, and enterprise documents seamlessly.
Access control, encryption, logging, and compliance guardrails.
Latency tracking, response quality metrics, and continuous performance improvements.
Identify documents, databases, and APIs critical for retrieval tasks.
Transform data into vector embeddings optimized for semantic search.
Combine retrieved context with LLM reasoning to produce accurate answers.
Continuously track quality, improve search pipelines, and refine AI responses.
Deliver accurate, secure, and scalable AI-powered responses across your organization with our RAG systems.