Problem
Enterprises need to run sensitive documents through large language models for summarization, extraction, translation, and analysis. Sending raw data to an external AI service creates unacceptable exposure. Existing approaches such as masking and redaction strip out the context the model needs, so the output comes back unusable.
That leaves teams with a binary choice: send raw data out to use AI at all, or hold the data back and give up on the result.
Definition
An AI data capsule is a context-preserving data layer that you generate locally, wrapping sensitive enterprise data before it reaches an external AI service. The capsule keeps document structure and context intact while swapping sensitive elements for structure-preserving substitutes. After the model runs, the capsule rebuilds the AI output back into full enterprise context inside your environment, and the original values stay inside.
How It Works
An AI data capsule works as a data layer for AI across three stages, all inside one restorable workflow:
- Local capsule creation. Sensitive elements in enterprise documents such as names, account numbers, dates, medical codes, and contract terms are detected and replaced with structure-preserving substitutes. The mapping between original and substituted values is stored locally, inside your environment.
- External AI processing. The substituted document goes to any external LLM. Because the structure is preserved, the model can summarize, extract, classify, and translate as effectively as it would on the original. The original values stay inside the enterprise environment.
- Local restoration. AI results are rebuilt locally against the original enterprise data. The locally stored mapping reconstructs outputs with real names, amounts, and references, producing results that drop straight into business workflows instead of leaving an analyst to re-match the answer by hand.
How It Differs From Masking and Redaction
Masking and redaction protect a field by removing what is in it, which is exactly the context an AI model needs. A capsule keeps the record usable for the model and brings the real answer back afterward.
| Approach | What the model sees | What comes back |
|---|---|---|
| Masking / redaction | Fields blanked out or removed | Context lost, output often unusable |
| AI data capsule | Structure-preserving substitutes | Results rebuilt into real enterprise context |
Enterprise Example
A bank needs AI to summarize 500 loan applications for a compliance review. Each document holds customer names, social security numbers, account balances, and income data.
With LLM Capsule, the bank builds a capsule for every document locally. The model processes the substituted versions and returns structured summaries. Local restoration rebuilds the real customer names and account details into those summaries, producing compliance-ready output while the original customer values stay inside the bank’s environment.