Enterprise AI / RAG
Mativyx builds retrieval-augmented generation systems that connect LLMs to documents, databases, knowledge graphs, tickets, contracts, manuals, policies, and operational history with citations and access controls.
RAG Architecture
Reliable RAG needs content engineering, metadata, access control, retrieval evaluation, answer grounding, and continuous quality measurement.
Parse PDFs, scans, contracts, spreadsheets, emails, manuals, and web content into structured, searchable knowledge.
Combine semantic, keyword, metadata, recency, authority, and graph retrieval for better recall and precision.
Connect entities, projects, vendors, contracts, assets, processes, and people so retrieval can use relationships, not only document similarity.
Respect role, department, geography, client, project, and document-level access permissions at query time.
Return answers with cited passages, source documents, confidence signals, and graceful refusal when evidence is weak.
Measure answer relevance, factuality, citation quality, retrieval coverage, latency, and user feedback by use case.
Enterprise Use Cases
Start with a RAG readiness assessment across documents, permissions, search quality, and target workflows.
Plan a RAG System