Retrieve
Pull live context from your databases, document stores and APIs — Aurora PostgreSQL + pgvector or OpenSearch for fast similarity search.
Real-time, accurate insights with Agentic RAG
Traditional AI knowledge systems struggle with stale data, hallucinations and limited context. Agentic RAG combines advanced LLMs, autonomous AI agents and dynamic knowledge retrieval to deliver fact-based, real-time responses for businesses that demand accuracy, efficiency and adaptability.
Agentic RAG is designed to meet unique business needs by delivering accurate, efficient and intelligent AI-powered retrieval and response. Enhance decision-making, streamline workflows and drive innovation.
Pull live context from your databases, document stores and APIs — Aurora PostgreSQL + pgvector or OpenSearch for fast similarity search.
Autonomous agents enrich and cross-reference the retrieved context, filtering for relevance before anything reaches the model.
Responses are grounded in the retrieved sources, so answers stay fact-based and traceable rather than hallucinated.
Feedback from real interactions tunes retrieval and ranking over time, so accuracy improves with use.
Semantic, embedding-based search understands intent — not just keywords — so the most relevant context surfaces first, even across large, unstructured repositories.
Query databases, object storage, document management systems and APIs in a single pass, combining structured and unstructured data into one grounded answer.
Every response is anchored to retrieved source material, minimizing hallucinations and giving teams answers they can verify and trust.
Deploys into your AWS environment and integrates with existing systems, with access controls and audit trails suited to regulated workflows.
Human-in-the-loop feedback continuously refines retrieval weights and agent behavior, so the system gets sharper the more your teams use it.
Tell us about your data and workflows, and we’ll show you what Agentic RAG can do.
Request a demo