Make sensitive workflows run with AI.

A context-preserving data layer for AI sits between your systems and approved model paths. It substitutes sensitive values while keeping document structure, relationships, and meaning intact, so models can reason over real business context and outputs restore to usable form inside your environment.

LLM Capsule transforms sensitive fields into restorable capsules before the request reaches the model. The model sees structure-preserving placeholders instead of raw values, and the response is restored to the originals only inside your environment, so raw data never leaves.

Yes. LLM Capsule encapsulates sensitive elements in your RAG sources and agent context while preserving the structure that retrieval and reasoning depend on, so RAG and agent workflows run on documents you previously could not send to an external model.

Context-preserving tokenization replaces sensitive values with placeholders that keep their format, relationships, and document structure, unlike redaction or masking that destroy meaning. The model still understands the task, and tokens are mapped back to original values during local restoration.

An AI gateway routes and manages model traffic; DLP detects and blocks sensitive content. LLM Capsule changes what crosses the model boundary: it turns controlled enterprise context into restorable capsules before model execution, then restores the output inside your environment.

Reconstruction happens only inside your organization. External models see safe placeholders, and original values are reconstructed locally into business-ready output, so raw enterprise data stays in your environment.