Grounded AI systems
Retrieval-augmented generation development
Zactra builds RAG systems that retrieve relevant information from approved sources before a model generates an answer or completes a task.
Direct answer
What you should know
RAG combines search and generative AI. Zactra designs ingestion, chunking, metadata, embeddings, retrieval, reranking, permissions, citations and evaluation so responses are more grounded in the organization’s current knowledge.
RAG architecture
- Content connectors and ingestion pipelines.
- Cleaning, chunking and metadata design.
- Embeddings, vector or hybrid search.
- Access-aware retrieval and reranking.
- Prompt assembly, citations and response controls.
- Evaluation, monitoring and refresh workflows.
Knowledge sources
- Policies, manuals and internal documentation.
- Support articles and product knowledge.
- Contracts, reports and structured records.
- Approved databases and APIs.
- Research libraries and technical content.
Quality controls
RAG quality is measured across retrieval relevance, answer groundedness, citation accuracy, access control, coverage, latency and cost. The system should also decline when approved evidence is insufficient.
Frequently asked questions
RAG retrieves relevant information from approved sources and supplies it to a generative model so the response can be grounded in current evidence.
No. It can materially improve grounding, but retrieval quality, prompt design, citations, evaluation and refusal behavior still matter.
Yes. Retrieval can filter content using the user’s identity, role and source-system permissions before information reaches the model.
Refresh timing can range from scheduled batches to event-driven updates depending on the source systems and freshness requirements.