Context-aware AI grounded in your live data
Agentic RAG combines LLMs, autonomous agents and dynamic retrieval to deliver fact-based, real-time answers.
Built on open-source LLMs, tuned to your business.Traditional knowledge systems struggle with stale data, hallucinations and limited context. Agentic RAG answers from retrieved, current sources, so decisions rest on facts.
How Agentic RAG works
- 01
Retrieve
Pull live context from databases, document stores and APIs, with pgvector or OpenSearch for fast similarity search.
- 02
Augment
Agents enrich and cross-reference what was retrieved, filtering for relevance before it reaches the model.
- 03
Generate
Answers are grounded in the retrieved sources, so they stay fact-based and traceable.
- 04
Refine
Feedback from real use tunes retrieval and ranking, so accuracy improves over time.
Key features
Search that understands intent
Semantic, embedding-based search surfaces the most relevant context first, even across large unstructured repositories.
Multi-source retrieval
Databases, object storage, document systems and APIs in a single pass, combined into one grounded answer.
Answers anchored to sources
Every response is tied to retrieved material, minimizing hallucinations and giving teams answers they can verify.
Enterprise-ready
Deploys into your AWS environment with access controls and audit trails suited to regulated workflows.
Learns from use
Human-in-the-loop feedback refines retrieval and agent behavior the more your teams use it.
Open-source LLMs, tuned to you
Built on open-source models and tuned to your business, running in your own AWS account with no vendor lock-in.
Bring grounded AI to your business
Tell us about your data and workflows. We will show you what Agentic RAG can do with them.