Retrieval-Augmented Generation Engine

Context-grounded AI responses using controlled retrieval over industrial knowledge and infrastructure datasets

IVR RAG implements Retrieval-Augmented Generation pipelines that combine language models with structured knowledge retrieval. It retrieves authorized content from indexed industrial documents, operational datasets and infrastructure services before generating responses, ensuring context accuracy, governance and traceability.

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RAG infrastructure capabilities

  • Document indexing and embedding pipelines
  • Vector-based similarity search over controlled datasets
  • Policy-aware retrieval filtering via Identity
  • Context assembly before model invocation
  • Traceable source attribution in generated outputs

Indexed industrial knowledge base

IVR RAG maintains indexed repositories of structured and semi-structured industrial knowledge, including technical documentation, operational records and governed datasets. Content is processed through embedding pipelines and stored in vector indexes optimized for semantic retrieval.

Indexing workflows are orchestrated via Scheduler and monitored by OpsMonitoring to ensure consistency and performance control.

Knowledge retrieval is engineered, not improvised.

Policy-aware retrieval and access control

Retrieval operations are mediated by MCP and validated through Identity-based authorization policies. Query results are filtered according to role, attribute scope and dataset permissions before being assembled into the model context.

This guarantees that AI-generated responses are grounded exclusively in authorized and traceable information sources.

  • Role-based content filtering
  • Dataset-level access enforcement
  • Secure retrieval APIs

Context assembly and generation pipeline

Retrieved knowledge fragments are assembled into structured context packages before model invocation. The generation pipeline combines retrieved content with user prompts under controlled token and context management.

All retrieval and generation steps are logged with correlation identifiers for reproducibility and audit support.

AI responses are context-grounded and infrastructure-governed.

Source attribution and traceability

Generated outputs can include structured references to source documents, dataset identifiers and retrieval timestamps. This enables validation of AI responses against original industrial knowledge artifacts.

Attribution metadata is stored alongside execution logs, ensuring transparency in regulated operational environments.

  • Document-level reference mapping
  • Retrieval metadata persistence
  • Audit-ready generation records

Scalable knowledge orchestration

IVR RAG integrates with IVR.AI and MCP to support distributed, scalable retrieval workflows. Vector indexes and embedding stores can be horizontally scaled to handle large industrial knowledge bases.

By combining semantic retrieval, governance enforcement and traceable generation, IVR RAG transforms industrial documentation and operational data into reliable AI-assisted knowledge services.

Retrieval-Augmented Generation becomes a controlled enterprise capability—not an opaque knowledge shortcut.