Retrieval-augmented generation connects large language models to your organisation's knowledge — so answers come from your documents, policies, and data, not from the model's general training. CYDATA designs and builds RAG systems that are accurate, auditable, and ready for production.
The difference between a demo and a dependable system is engineering: chunking strategies, retrieval quality, evaluation harnesses, and guardrails. That is where CYDATA delivers.
Pipelines that parse, clean, and chunk your documents — PDFs, wikis, SharePoint, ticketing systems — with metadata and structure preserved for precise retrieval.
Embedding model selection, vector index design, and hybrid search combining semantic and keyword retrieval — tuned for your domain's vocabulary.
Prompt and orchestration design that anchors every answer in retrieved sources, with citations back to the original documents your teams can verify.
Systematic evaluation against curated test sets, hallucination detection, content filters, and access controls that keep responses accurate and safe.
RAG systems live or die by data quality. CYDATA integrates retrieval pipelines directly with the platforms we already deliver — Databricks and Microsoft Fabric — so your AI draws on the same governed, curated data as your analytics. No parallel copies, no ungoverned silos: one source of truth serving both dashboards and AI.
From internal knowledge assistants to customer-facing Q&A, CYDATA delivers RAG systems your organisation can trust with its own knowledge.