Core 1Zero Exposure
Zero Exposure
Sensitive data is encapsulated locally before leaving the environment. Raw data never reaches external AI services. Even if the provider logged or stored the data, no enterprise information would be exposed.
The AI enablement data layer and plugin for enterprise
LLM Capsule enables enterprise AI adoption by keeping raw data inside your environment, preserving document structure and business context during AI processing, and restoring usable outputs through local restoration — so enterprise teams can safely use any LLM on real documents in production workflows.
Most enterprise AI security tools either block AI usage entirely or strip critical context through masking and redaction, producing outputs that cannot be used in real business processes. LLM Capsule takes a different approach: local encapsulation protects sensitive elements before AI processing, structure-preserving processing maintains document integrity for AI comprehension, local restoration auto-restores AI outputs with original enterprise data, and cross-model execution means no vendor lock-in. This enables enterprise AI enablement on document-heavy workflows including contracts, claims, regulatory filings, medical records, and internal reports.
Enterprise AI enablement through five core capabilities
LLM Capsule enables enterprise AI adoption on sensitive data through a 3+2 architecture — three core enablement capabilities plus structure-preserving processing and cross-model execution.
Core 2Restoration
Restoration
AI outputs are auto-restored locally with real data into usable enterprise documents after processing. Restored outputs work directly in reports, claims documents, legal reviews, and internal analysis — no manual reconstruction required.
Core 3Enterprise Context
Enterprise Context
Organizations can define sensitive entities beyond standard PII — project names, internal identifiers, customer-specific confidential markers, and contract references. Context-aware data control adapts to your business.
+1Structure-Preserving
Structure-Preserving
Tables, diagrams, cross-references, and document layouts remain intact during encapsulation. AI receives structurally complete documents that enable accurate extraction and analysis.
+2Cross-Model
Cross-Model Execution
Model-agnostic by design. Use any LLM — ChatGPT, Claude, Gemini, Perplexity, or any API — without vendor lock-in. Protection stays consistent regardless of which model you choose.
Encapsulate before outbound — raw data never leaves
LLM Capsule encapsulates sensitive entities locally before any data leaves the enterprise environment.
LLM Capsule encapsulates sensitive entities locally before any data leaves the enterprise environment. Only the protected capsule is sent for AI processing.
- Local real-time encapsulation — Raw data stays inside the enterprise environment. Sensitive elements are detected and replaced before any outbound transmission.
- Environment-bound processing — Supports controlled enterprise and regulated environments including on-premise, air-gapped, and VPC deployments.
Most enterprise AI risk starts when raw business data is exposed outside the controlled environment. With LLM Capsule, sensitive content is transformed locally so external models never receive the raw original data.
- Protected outbound flow — Only encapsulated representations cross the trust boundary. The AI provider processes useful but opaque data.
- Audit trail — Every encapsulation event is logged with full traceability for enterprise AI governance and compliance reporting.
Beyond text — keep document structure intact for AI
Enterprise workflows do not run on plain text alone. They rely on reports, PDFs, spreadsheets, diagrams, presentations, tables, and mixed-format documents.
Flat masking treats every sensitive value identically, collapsing entity relationships and breaking table schemas. Structure-preserving processing maintains entity consistency across entire documents, preserves table column relationships for accurate extraction, and keeps cross-reference links intact. This is document-aware protection — not flat text anonymization.
PDF & Word Documents
Protected processing with layout, formatting, and section structure preserved. AI receives structurally complete documents.
Spreadsheets & Tables
Tabular data structure maintained through encapsulation and restoration. Column headers, row relationships, and cell references preserved.
Presentations & Reports
Visual and mixed-format documents handled as structured content. Cross-references and entity relationships remain trackable.
Enable AI without breaking enterprise workflows
LLM Capsule does more than hide data. It auto-restores usable output inside the environment after AI processing so enterprise teams can actually use the result in real workflows. This is a restorable workflow — not just protection, but AI enablement with usable output.
Traditional masking protects data by removing meaning. That may reduce risk, but it also reduces output quality and business usability. Restored outputs from LLM Capsule are directly usable in: claims documents with real policyholder data, legal reviews with real party names and clause references, regulatory reports with real customer and account data, and internal analysis with real business metrics.
This is the capability that makes enterprise AI operationally viable. Secure document summarization, AI claims processing, and confidential contract review with AI all depend on the ability to restore results. Without restoration, every AI output requires manual reconstruction — eliminating the efficiency gains AI is supposed to deliver.
Control what matters to your business — beyond generic PII
LLM Capsule lets teams define sensitive entities beyond standard PII categories, including internal identifiers, project names, customer-specific markers, and organization-specific confidential terms.
Enterprise data protection is not limited to names, phone numbers, or IDs. Real workflows often depend on internal project names, contract references, operational code names, and confidential business terms. Context-aware data control enables policy-based sensitivity classification that adapts to document type, department origin, and workflow context — providing enterprise AI governance controls that go far beyond standard PII regex matching.
Internal code names
Project names and operational identifiers
Customer identifiers
Customer-specific account codes and references
Contract references
Deal terms, agreement numbers, clause identifiers
Financial terms
Pricing models, valuation ranges, internal metrics
Vulnerability labels
Security findings, CVE references, risk assessments
Strategic data
M&A targets, competitive intelligence, board-level data
Custom markers
Business-specific confidential markers defined by your team
3+2Core Architecture
ZeroRaw Data Exposure
100%Local Restoration
AnyLLM Model Support
Operational control for enterprise AI governance
Enterprise deployment requires more than transformation logic. Teams need policy control, access control, activity visibility, and auditability.
Enterprise AI governance requires evidence of data protection at every stage — what data was processed, how it was protected, which models interacted with it, and who authorized the workflow. LLM Capsule's admin capabilities provide this auditability across all AI interactions.
RBAC
Role-based access control for teams and workflows. Define who can configure policies, process documents, and view audit records.
Policy Management
Define and enforce encapsulation policies per team, data type, document classification, or workflow context.
Audit Logs
Full traceability of every encapsulation, AI processing, and restoration event. Supports compliance reporting and regulatory review.
Token Usage Visibility
Monitor token consumption and cost across all AI model interactions. Optimize usage and track spending by team or workflow.
Detection Logs
Visibility into what was detected as sensitive, how it was classified, and how the protection policy was applied.
Operational Monitoring
Compare and monitor processing across multiple AI models. Centralized visibility into system health and throughput.
Model-agnostic — use any LLM with no vendor lock-in
Enterprise teams do not always standardize on a single AI model.
Enterprise teams do not always standardize on a single AI model. Evaluation, governance, and operational workflows may span multiple providers and multiple model choices over time. LLM Capsule fits this reality as an AI enablement data layer for cross-model enterprise AI deployment.
Because LLM Capsule operates at the data layer — not the model layer — protection and enablement remain stable even when model vendors change. ChatGPT, Claude, Gemini, Perplexity, or any LLM API can be used interchangeably without reconfiguring the pipeline. This is cross-model execution — enterprise AI enablement independent of any specific AI provider, eliminating vendor lock-in.
Built to fit existing enterprise systems
LLM Capsule works as a deployable component through API and SDK integration patterns, making it practical to embed into existing products, portals, and internal workflows.
The API provides LLM API enablement at the data layer — wrap any existing AI integration with encapsulation and restoration without rebuilding the application.
Internal enterprise portals
Embed protection into existing employee-facing AI tools and knowledge systems.
Partner backends
Integrate into partner platforms and B2B workflows with API-based encapsulation.
Secure document workflows
Add protection to existing document processing, review, and approval pipelines.
AI-assisted analysis tools
Wrap analysis and extraction tools with data protection at the API layer.
Customer-facing AI features
Enable customer-facing AI capabilities without exposing internal data to external models.
Enterprise AI deployment — ready for any controlled environment
Enterprise teams need deployment flexibility without giving up control.
Enterprise teams need deployment flexibility without giving up control. LLM Capsule supports on-premise deployment, air-gapped environments, cloud deployment including AWS Marketplace, hybrid configurations, and embedded integration. The same product logic runs across all deployment models while keeping local protection and local restoration at the center.
On-premise
Fully inside customer-controlled infrastructure. The encapsulation engine runs within the enterprise data center.
Air-gapped environments
Restricted and isolated environments. Encapsulation on the isolated network, controlled transfer for AI processing.
Cloud & AWS Marketplace
AWS Marketplace deployment for streamlined procurement. Runs within the enterprise's cloud account or VPC.
Hybrid & Embedded
Different sensitivity levels route through different deployment modes. Embeddable into existing applications and partner products.
How LLM Capsule differs from traditional approaches
Not all protection approaches are designed for usable enterprise AI workflows. Traditional masking protects data by reducing usability. LLM Capsule protects data while preserving enterprise workflow value.
| Dimension | Traditional Masking / Redaction | Prompt Security Gateways | LLM Capsule |
|---|---|---|---|
| AI enablement layer | Pre-processing data removal | API-level prompt filtering | Data-layer encapsulation |
| Local processing | Often does not preserve full workflow boundary | Cloud-based filtering, not local | Sensitive entities encapsulated locally before outbound |
| Restoration | One-way, no restored usability | No output restoration | Outputs auto-restored locally for usable workflows |
| Business-specific entity control | Generic PII categories only | Pattern-based PII detection | Enterprise context control beyond PII |
| Structure preservation | Optimized for flat text only | N/A — operates on prompts | Tables, diagrams, layouts preserved |
| RAG pipeline support | Partial | Limited — only sees final prompt | Full data pipeline protection |
| Deployment flexibility | Varies | Cloud / SaaS only | On-premise, air-gapped, cloud, hybrid, embedded |
| Workflow usability | Protects data while reducing output value | Blocks or passes, no transformation | Built for usable AI outputs |
| Audit & governance | Limited traceability | Prompt-level logging | Complete audit trail |
Common Questions about LLM Capsule
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See how LLM Capsule fits your environment, documents, and controls
Bring your documents, deployment constraints, and evaluation questions. We demonstrate enterprise AI enablement on your actual data, in your environment, against your compliance requirements.
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