Two minutes — see a real document encapsulated, sent to an external LLM, and restored back into the originating workflow. No marketing words. The actual product.
For architects and security reviewers — the full zone-based view of how operational data, encapsulation, and how any LLM interacts.
Existing enterprise systems — ERP, CRM, Ticketing, DMS / ECM, Legacy DB, RAG Pipeline — stay in place. Nothing migrates. Capsule reads from them via REST, gRPC, JDBC, or Graph API depending on the source.
The Enhanced Encapsulation Layer detects sensitive elements, replaces them with safe tokens using structure-preserving, differential-privacy-based protection, and hands the capsule to the routing decision. Original values stay behind, retained in the local token map.
Organizational policy, permissions, and domain context decide where the capsule is processed — an approved external LLM (Path A) or an on-prem local model (Path B). The decision is policy-driven per workflow, with full audit retained inside the organization.
The AI response is automatically reconstructed from token to original value inside the organization only. Data that left the trust boundary cannot be reconstructed externally. The restored output is delivered back into the originating workflow.
The same capsule mechanism runs across telecom, healthcare, finance, defense, legal, and OT. Below: a contract review workflow. The raw document never leaves your environment.
LLM Capsule runs inside your environment and reads documents from the systems already there — SharePoint, Jira / ServiceNow, Salesforce, Oracle ERP, internal NOC console, or your own portal. No data migration. No external pipe. No architectural change.
Existing systems invoke Capsule from inside the environment via REST / gRPC / JDBC / Graph API / on-prem API / embedded SDK / Slack App.
Pick from the starter pack — project codes, contract refs, network IDs, mission refs, financial terms, vulnerability labels — or write your own. Filters can be added, removed, and time-shifted tomorrow without redeploying. Every policy version is logged.
Inside the DMZ — Demilitarized Zone (Zone 2 of the four-zone architecture), sensitive elements are replaced with structure-preserving placeholders by the Enhanced Encapsulation Layer. Differential-privacy-based encapsulation (epsilon-DP, Laplace noise, k-anonymity, NER replacement) reduces re-identification risk. Tables, cross-references, and document hierarchy survive intact. See the four-zone architecture
The capsule (only the capsule — never the original) is routed through your approved external LLM (ChatGPT, Claude, Gemini, Perplexity) or to an on-prem local lightweight model for air-gapped workflows. Path is policy-driven per workflow.
The AI's response is auto-restored locally — token map lookup, original value substitution, context re-binding, output validation. Real names, real figures, real references appear in the original ticket. Token map never leaves the enterprise. End user sees a finished, production-ready output.
2,200-character document benchmark. Tested across finance, healthcare, legal, and public sector workflows.
That's 120 milliseconds from raw document to encapsulated capsule — fast enough to plug into real-time NOC alerting, claims intake, and clinical workflows without breaking SLA. Most enterprise AI pilots stall on latency. We don't.
Each card below is a real workflow LLM Capsule runs in production. Click for the full case story.
RCA generation on live ticket data with device IDs, circuit IDs, site references, alarm sequences, SLA-impact references, and subscriber identifiers encapsulated locally. No raw operational data exposure to external LLMs.
AI drafts radiology summaries from real clinical workflows. PHI encapsulated locally; restoration happens inside the hospital network. HIPAA-aligned.
AI-powered claim classification, damage assessment, and fraud detection on real policyholder data. No customer data leaves the insurer's environment.
AI drafts intelligence briefs and operational summaries on classified data. On-prem local execution path — zero external transmission. Full audit trail under command control.
Enterprise AI governance is not a marketing claim. It's a console your audit team logs into.
Every encapsulation, processing, and restoration event lands here. Audit teams can replay any event end-to-end — what was protected, which policy version was active, which model processed it, what was restored.
Yesterday it was network logs. Today it's M&A code names. Next quarter it's a new regulator's spec. Standard PII categories don't move; your business does. LLM Capsule lets your team define, add, remove, and version confidentiality markers as your operations and regulatory landscape evolve — with full audit trail of which marker was protected when.
Project codes, deal terms, internal IDs, contract references, network identifiers, OT asset IDs, mission refs. Your team defines the markers — not a vendor's fixed list.
Add a new marker today, retire one next quarter. Policy versioning + immediate enforcement. Audit log records exactly which marker was active for every encapsulation event.
NOC team, oncology unit, OT operations, M&A — different policies, same governance. RBAC + scoped enforcement + per-policy audit. One LLM Capsule, many policies.
Bring your real workflow. We'll set up Capsule on a sample document in your environment within 30 minutes.
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