Overview
Synthetic data platforms generate artificial datasets that mimic the statistical properties of real data. Teams use them for model training, testing, and analytics where real data cannot be used. LLM Capsule solves a different problem: it is a context-preserving data layer for AI that runs AI on real enterprise documents while keeping sensitive values inside your environment and returning usable outputs.
How Synthetic Data Platforms Work
Synthetic data platforms analyze real datasets and generate new, artificial data that preserves statistical distributions, correlations, and patterns. The synthetic data contains no real individuals or entities. Teams use it for model training, development environments, and analytics workloads.
Limitations
- Not suitable for document processing. Synthetic data platforms generate tabular data, not documents. They cannot create synthetic versions of contracts, medical records, or legal filings that retain their specific content and meaning.
- Loss of specificity. Synthetic data preserves statistical patterns but not specific enterprise content. You cannot summarize a synthetic contract: it does not contain real terms, real parties, or real obligations.
- No real-world output. AI outputs based on synthetic data describe synthetic scenarios, not real enterprise situations. There is no mechanism to map synthetic outputs back to real enterprise context.
How LLM Capsule Differs
LLM Capsule does not replace real data with synthetic data. It keeps real documents intact, preserving their specific content, structure, and relationships, and replaces only sensitive elements with structure-preserving substitutes. AI processes real enterprise content and produces real enterprise outputs, which are restored through a protected mapping layer inside your environment. Because that mapping stays inside your trust boundary, the original values stay inside your environment rather than being sent to the model in their original form.
AI results come back restored inside your environment.
Comparison
| Capability | Synthetic Data Platforms | LLM Capsule |
|---|---|---|
| Input data | Artificially generated | Real enterprise documents |
| Document support | Tabular data only | All document types |
| Content specificity | Statistical patterns only | Actual enterprise content |
| Output usability | Synthetic context | Real enterprise context |
| Output restoration | ✗ N/A | ✓ Restored inside your environment |
| Use case | Model training, testing | Production AI workflows |
Enterprise Workflow Example
A compliance team needs AI to identify risk indicators in 1,000 real audit reports. Synthetic data cannot help. Synthetic audit reports do not contain the real findings, real entity references, and real risk patterns the team needs to analyze.
With LLM Capsule, AI works on the real audit reports with sensitive elements replaced by structure-preserving substitutes, identifies the risk patterns, and the protected mapping layer returns an actionable risk assessment with real entity names and findings linked to real reports.