RAG Systems Development
Retrieval-augmented generation systems that make private company knowledge searchable, answerable, and safe to use with AI.
Give teams reliable AI answers from private company knowledge.
A RAG system retrieves relevant information from approved company sources before an AI model answers. AUTNEX.ai builds RAG platforms with ingestion, chunking, embeddings, vector search, graph retrieval, permissions, citations, evaluation, and monitoring so answers remain grounded in trusted sources.
What this helps you achieve
- Answer questions from private documents with source citations
- Connect SharePoint, Elasticsearch, databases, and file stores
- Respect user-level permissions and sensitive data boundaries
- Evaluate answer quality and retrieval coverage before rollout
Common use cases
- Enterprise knowledge assistants
- Support and internal helpdesk assistants
- Policy and compliance search
- Technical documentation Q&A
- Sales enablement knowledge bases
Technology and implementation patterns
AUTNEX.ai chooses the smallest reliable architecture for the workflow, then adds security, observability, and handover practices required for production use.
Related AUTNEX pages
Questions this page answers
What is a RAG system?
A RAG system, or retrieval-augmented generation system, searches trusted data sources before generating an answer, making AI responses more grounded, auditable, and useful for private company knowledge.
Can a RAG assistant respect document permissions?
Yes. A production RAG assistant should enforce source permissions before retrieval and generation, so users only receive answers based on content they are allowed to access.
Why combine vector search with Elasticsearch or graph retrieval?
Vector search is strong for semantic similarity, Elasticsearch is strong for keyword and filtering precision, and graph retrieval helps follow relationships between entities, documents, teams, and processes.
Want to scope this for your team?
Tell us the workflow, data sources, constraints, and desired outcome. We will map a fixed-scope path to a useful first version.
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