RAG Knowledge Agents
Grounded, Source-Cited Semantic Search
What is RAG Knowledge Agents?
RAG Knowledge Agents turn your scattered documents — manuals, contracts, wikis, and code repositories — into a single expert your team can simply ask. Unlike a generic chatbot, every answer is grounded in your actual source material and comes with citations, so people can trust it and verify it. Institutional knowledge stops living in someone's head and becomes instantly searchable.
How does RAG Knowledge Agents work?
Your documents are deep-indexed into a private vector store — chunked and embedded so the agent can find the most relevant passages for any question. At query time it uses hybrid search with reranking to retrieve the strongest evidence, then composes an answer that cites the exact source and page. Because answers are grounded in retrieved text, hallucination is sharply reduced — and when the knowledge isn’t there, the agent says so instead of guessing.
What can RAG Knowledge Agents do?
- Deep-indexing of PDF, Markdown, DOCX, and code repositories
- Hybrid semantic search with reranking for precision
- Source-cited answers with page-level references
- Private vector store deployment (Pinecone or Milvus)
- Access controls that respect your existing permissions
Which teams use RAG Knowledge Agents?
Internal knowledge base
Let employees ask questions across policies, manuals, and wikis instantly.
Contract & document review
Surface clauses, terms, and answers from large legal or technical corpora.
Customer-facing answers
Power support and sales with accurate, cited responses from your own docs.
What outcomes can you expect?
- Trustworthy answers backed by verifiable sources
- Less time lost searching for buried information
- Knowledge that scales beyond any single expert
What technology powers RAG Knowledge Agents?
Cohere/Voyage embeddings with hybrid search and reranking over a private, access-controlled vector store.
RAG Knowledge Agents: frequently asked questions
What documents can a RAG Knowledge Agent index?
A RAG Knowledge Agent can deep-index the full range of formats your knowledge actually lives in: PDFs, Markdown, DOCX and other office documents, internal wikis and knowledge bases, and even code repositories. These are chunked and embedded into a private vector store so the agent can retrieve the most relevant passages for any question, rather than relying on a model's general training. Access controls are applied at the retrieval layer and respect your existing permissions, so a given user only ever gets answers drawn from content they are allowed to see. Because each deployment is custom-built, we shape the ingestion pipeline to your specific sources and keep it in sync as your documents change, so answers stay current. The practical upshot is that the agent answers from your real, up-to-date organizational knowledge — the documents your team already maintains — instead of guessing from a generic model that has never seen your content.
How does it avoid hallucinating answers?
The agent avoids hallucination by grounding every answer in passages actually retrieved from your documents, and by citing the exact source and page it used, so you can verify any response at a glance. Rather than generating from the model's general memory, it first retrieves the most relevant content from your private index and then answers strictly from that context — which keeps responses tied to your real material. Crucially, when the knowledge simply is not in your documents, the agent is built to say so plainly instead of inventing a plausible-sounding answer. That honesty is a deliberate design choice: for internal knowledge and customer-facing support alike, a confident wrong answer is far more damaging than an honest 'I don't have that.' We reinforce this with guardrails and evals tuned to your content, so accuracy is measured on your real questions. The combination of retrieval grounding, source citations, and explicit uncertainty is what makes the agent trustworthy for real decisions.
Where is my data stored?
Your content is stored in a private, access-controlled vector store — typically Pinecone or Milvus — provisioned for your deployment, not a shared pool. Embeddings and retrieval run against that private index, with access controls that mirror your existing permissions so users only ever retrieve content they are entitled to see. We never train or fine-tune shared models on your data, we never use one client's data to benefit another, and your data is processed under least-privilege access and deleted on request. Because every build is custom, we can align the storage and hosting choices with your security and data-residency requirements rather than forcing a fixed setup. Every retrieval and answer is logged for auditability, so you have a clear record of what the agent accessed and returned. The guiding principle is simple: your knowledge stays yours, it lives in an isolated store you control, and it is used only to answer your questions — nothing more.
Related capabilities
- Multi-Agent Swarms — Multi-Agent Swarms that plan, execute, and validate complex tasks with built-in peer review for higher accuracy.
- Workflow Agents — Workflow Agents that connect your systems, move and transform data, and run multi-step processes end to end.
- AI Support Agents — AI Support Agents that resolve tickets 24/7 with grounded, cited answers and escalate complex cases to humans.