Connect your documents to a secure knowledge graph and vector index. Deploy on bare VMs, Kubernetes, or dedicated managed instances.
Self-hosted or dedicated managed environments. Easy setup.
The Business Case
Standard Vector RAG loses the connections between your data chunks. That means run-away token bills from long prompts, incorrect logic, and flat-out hallucinations.
Splitting files into random text blocks and searching by similarity is easy to build. But when you ask complex business questions, standard RAG fails:
Ragyn extracts structured facts and embeddings from your files, writing them into Neo4j with full vector coverage and automated query routing:
The Pipeline
You don't need a team of data scientists. Ragyn processes your files and builds your knowledge graph automatically.
Point Ragyn at S3, Azure Blob, GCS, SharePoint, ServiceNow, or local files. It scans documents, maps their content, and structures domain schema templates dynamically.
It generates nodes, relationships, and vector embeddings inside Neo4j in a single transaction. Ingestion is append-only—no slow, costly rebuilds.
Queries are dynamically routed to context-matching domain schemas, matched via vector search, and resolved via Cypher with full traceability.
Deployment
Run Ragyn on a standard Ubuntu or Debian virtual machine using Docker Compose. Perfect for simpler, low-overhead deployments with minor maintenance.
Deploy using standard Helm charts or manifests. Auto-scale worker pods to exactly zero when idle via KEDA integration to keep cluster compute costs at a minimum.
Need a fully managed deployment? We provision and manage a dedicated, secure Ragyn environment for your team. Contact us to discuss your requirements.
Why Teams Choose Ragyn
We focus on predictability, compliance, and infrastructure efficiency—features that matter to growing businesses.
Every answer includes clear citations tracing back to specific database nodes. No loose assertions—if the relationship isn't explicitly documented in the graph, Ragyn won't invent it.
Know exactly what you are paying for. Ragyn tracks extraction and query token usage down to the individual document block, giving you clear cost audit trails.
Minimize cloud infrastructure bills. Background ingestion workers scale down to exactly zero replicas when there are no jobs in the queue, eliminating idle compute waste.
Full observability from day one. Ragyn exports standard OTEL traces for ingestion tasks and chat pathways, making it easy to monitor performance in Datadog or Phoenix.
Why choose between semantic search and relational logic? Ragyn merges vector similarity search with structured graph traversals for complete contextual coverage.
Keep prompts small and context laser-focused. Ragyn analyzes query intent at runtime and automatically routes searches to relevant domain schemas (like HR or Finance).
Connectors
No complex migrations. Point Ragyn at your active document libraries and start querying.
AWS S3
PDF, DOCX, TXT, MD, ImagesAzure Blob
PDF, DOCX, TXT, MD, ImagesGoogle Cloud (GCS)
PDF, DOCX, TXT, MD, ImagesSharePoint
Document LibrariesServiceNow
Incidents, Changes, RCAsLocal Directory
PVC mounts, local filesSchedule a detailed use-case walkthrough with our engineers. Deploy self-hosted on bare VMs or Kubernetes, or contact us to discuss a dedicated managed option.
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