Run AI on the sensitive operational data that used to be off-limits,
and get results back in a form your workflow can use right away.
The original values stay inside your environment.
Original values & reconstruction mapping stay inside
The model is rarely the hard part.
The data it needs can't move raw,
and the results have to come back in a form the business can use.
Inside the company, teams want AI for root-cause analysis, claim review, and clinical drafting. In products, AI companies want to ship features that run on each customer's data. External and on-prem models are ready for both.
Tickets, contracts, patient records, and sensor logs hold names, codes, and terms that your policy keeps from leaving as they are. The workflow stops before the model ever sees the data.
Blanking or deleting sensitive fields keeps the data in, but a record full of gaps loses the structure, relationships, and meaning the task depends on. An answer comes back, but it isn't usable.
Even when the model answers, teams still re-map names, codes, and figures by hand before results can go into reports, systems, or customer deliverables.
This is the context-preserving data layer for AI. Operational values that can't move raw become context-preserving substitutes with structure intact. You run them on any approved model path, external or on your own servers (on-prem), and Business-Ready Reconstruction restores the results inside the workflow they came from. When substitution alone can't keep data both protected and useful, embedded DTS reconstructs the data the task needs.
What changes when AI can work on data that can't move raw.
Business-Ready Reconstruction restores your original names, figures, and references in the AI output, ready for reports, legal reviews, and client deliverables. Teams spend far less time re-mapping results by hand.
Sensitive terms are substituted consistently, so tables, tickets, logs, and the links between them stay readable. AI works on the full operational record, not broken fragments.
Substitution and restoration run inside the execution flow, at about 0.12 seconds per page on a 2,200-character document (self-tested, excluding answer generation), so data handling adds little wait to AI steps.
Customer-defined markers go beyond standard PII: device IDs, circuit IDs, deal terms, M&A code names, OT identifiers. Policies are versioned, so when rules change you update the markers instead of rebuilding pipelines.
Whether your systems sit on an air-gapped network, on-premise servers, or inside your own product, LLM Capsule connects through an API, and a single governance policy covers external and on-prem models.
Some values, like amounts, timestamps, frequencies, ages, or test results, lose their use when simply swapped. For that data, embedded DTS reconstructs what the task needs, so protection and usefulness hold together.
Raw operational data stays inside the corporate environment.
Only the protected working version leaves your environment,
and output is restored locally, inside the workflow it came from.
The operational systems already live here. LLM Capsule connects to your existing systems through an API and reads them in place.
Korean and English named-entity detection, plus rules you set per workspace, finds the values you defined as sensitive. Substitution replaces them while keeping structure and context intact, and only the protected working version leaves your environment.
Governance and routing choose between an approved external LLM (for example ChatGPT / Claude / Gemini) and an on-prem local model. Retrieval for RAG combines embedding and keyword search. Organizational policy and domain context stay intact.
Inside the organization, the AI response is auto-restored to original values. The reconstruction mapping stays inside, so restoration happens only in your environment, and business-ready output goes back to the workflow it came from. Each run leaves an audit log and usage record.
Manages model traffic: routing, auth, fallback, caching, rate limits, cost, observability.
Detects, classifies, or blocks sensitive content.
Helps workers search, chat, and automate tasks across company apps.
It changes what your workflow actually sends to the model. Sensitive values are substituted before the request goes out, and results come back restored inside your environment.
Gateways route the call. LLM Capsule changes what crosses the model boundary.
Finance · Public sector · Industrial cybersecurity
Self-tested figures. Processing time excludes answer generation.
Original values are restored through the reconstruction mapping in your environment.
A context-preserving data layer for AI lets AI work on operational data that cannot move raw. Sensitive values are consistently substituted. Document structure, relationships, and meaning stay intact, so models reason over real business context. Usable outputs are restored inside your environment, and the original values and reconstruction mapping stay there.
LLM Capsule turns sensitive values into context-preserving substitutes before the request reaches the model. The model path can be external, but the model sees only the protected working version. The original values and the reconstruction mapping stay inside your environment, where the response is restored.
Yes. LLM Capsule substitutes the sensitive elements in your RAG sources and agent context while keeping the structure that retrieval and reasoning depend on. So RAG and agent workflows run on operational data you could not send to an external model before.
Masking and redaction destroy the meaning a model needs, and a record full of blanks is unusable. LLM Capsule keeps the format, relationships, and document structure intact with context-preserving substitutes, so the model still understands the task and teams get answers they can act on, with real values restored internally.
An AI gateway routes and manages model traffic. DLP detects and blocks sensitive content. LLM Capsule changes what crosses the model boundary: it swaps operational values for context-preserving substitutes before model execution, then restores the output inside your environment.
Reconstruction happens only inside your organization. The model path can be external, and the model sees only context-preserving substitutes. The original values and reconstruction mapping stay inside your environment. Business-Ready Reconstruction restores values through your internal mapping, not through statistical recovery.
Bring your documents, deployment constraints, and one real workflow.
We show it live in a 30-minute session, on sample or shared documents.