Vaikora for RAG Pipelines

Retrieval-augmented generation moves sensitive content through three policy-relevant stages: query, retrieval, and generation. Vaikora enforces deterministic policy at each stage and logs every decision to a SHA-256 audit chain.

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Query-stage enforcement

The query is evaluated for prompt injection, jailbreak attempts, leaked credentials, and topic-restricted patterns before any retrieval happens. A blocked query never costs an embedding call, never costs an LLM call, never costs storage write.

Retrieval-stage enforcement

Retrieved chunks are evaluated for PII, regulated content (PHI, PCI), and policy-restricted topics. The semantic risk module classifies chunks against the deployment's policy posture. CONSTRAIN decisions redact specific spans before the chunks reach the generation prompt.

Generation-stage enforcement

The final prompt sent to the LLM passes through the same policy pipeline. Multi-stage decisions are stitched into a single audit receipt so auditors can replay the full RAG path end-to-end.

Adapters

First-class adapters for OpenAI, Anthropic, Google Gemini, OpenRouter, and any LLM accessible through MCP. Vector store integrations through standard RAG frameworks (LangChain, LlamaIndex, custom). Vaikora is store-agnostic.

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