Infrastructure for trustworthy RAG
Retrieval is not just a feature. It’s infrastructure for trustworthy RAG.
In Web Data Source, Retrieval defines how knowledge is discovered, filtered, and delivered to downstream AI systems — all inside an air-gapped compatible, isolated environment deployment.
Retrieval is the bridge between secure data extraction and accurate AI responses. It ensures your models see the right context, sourced from the right data, under strict Zero-Trust architecture constraints.
What Retrieval enables
- Unified access to full-text search and vector search
- Hybrid retrieval strategies for higher recall and precision
- Predictable, explainable context feeding for RAG pipelines
- Safe operation in private network crawling scenarios
- Full compliance with data sovereignty requirements
- Operation with no internet dependency in a controlled access environment
Designed for enterprise AI. Retrieval in WDS is built to operate where most AI platforms fail:
- Air-gapped environments
- Restricted corporate networks
- Security-first infrastructures
- Regulated industries
It integrates tightly with indexing and enrollment pipelines, ensuring that only approved, curated, and traceable content is ever surfaced to LLMs — preserving enterprise-grade security at every step.
Why it matters for RAG. Better retrieval means: Less noise in context windows Fewer hallucinations More deterministic answers Stronger trust in AI outputs
If your retrieval layer is weak, your RAG system is unreliable — no matter how powerful the model is.
🔗 Full Retrieval API reference
