01
External LLMs raise enterprise ROI
ChatGPT, Claude, Gemini, and in-region EU models are already good enough to reshape ops workflows, from root-cause analysis to clinical drafting.
LLM Capsule · Context-Preserving Data Layer for AI
Your AI stops at the data it can't touch. Capsule gets it through.
LLM Capsule lets AI work on operational data that can't move raw. Those values become context-preserving stand-in values (substitutes). The AI runs on those. Usable results come back inside your environment, through a protected mapping layer that never leaves.
Available on
AWS Marketplace
The problem
Why enterprise AI projects stall before production
When you put AI to work on sensitive enterprise data, the hardest part isn't the model. It's the data. Each approach breaks at a different layer, and the risk that remains stalls the project right before production.
BUT
02
PII guardrails alone aren't enough
Free-text fields like a CS ticket's Details column mix customer names, contact info, and claim narrative with no structure. Simple PII guardrails miss it. Blanket masking and redaction strip the context AI needs to give a useful answer.
AND
03
Legacy and operational data is complex
Network operations tickets, plant sensor archives, patient records, and mission briefs aren't clean structured data. They cross-reference each other, stay unstructured, and live in production systems that won't migrate.
AND THE RESIDUAL RISK
04
Filtering alone leaves regulated risk standing
Filters that only check each request run at the API edge. They don't process documents end-to-end, don't restore outputs, and don't cover cases where data must stay in-country and sending it outside is not allowed at all.
SO
The fix
LLM Capsule
This is the context-preserving data layer for AI. Operational values that can't move raw become DP-based, context-preserving substitutes with structure intact. You run them on any approved model path, external or on your own servers (on-prem), and reconstruct inside the workflow it came from.
Capabilities
Six reasons Capsule works inside real enterprise workflows
Each capability fixes a specific way conventional approaches break.
CAPABILITY 01
Get real results back
AI outputs auto-restore your original names, figures, and references, ready for reports, legal reviews, and client deliverables. No manual reconstruction.
CAPABILITY 02
Tables, tickets, logs, and runbooks stay readable to AI
Tables, tickets, and document structure stay intact. AI reads the full operational structure instead of broken fragments that produce useless outputs.
CAPABILITY 03
Runs inside the systems you already operate
Air-gapped networks, on-premise servers, custom data systems, ServiceNow / SharePoint / Jira / OT historians. Capsule runs inside your existing environment with one added API call and no architectural change. Run an external LLM or on-prem local under a single governance.
CAPABILITY 04
You define what's sensitive
Customer-defined confidentiality markers beyond standard PII: device IDs, circuit IDs, deal terms, M&A code names, mission references, OT identifiers. Enterprise context, not generic privacy.
CAPABILITY 05
Your workflow runs where your data already lives
Your data stays inside. The model only ever sees protected stand-ins; the originals and the restore map never leave.
CAPABILITY 06
You can change the policy tomorrow
Time-shifting policy: yesterday's policy archived, today's enforced. When new regulations land, you update the markers without rebuilding pipelines.
VersionedScopedAccess-controlledChange-logged
Architecture
The four-zone architecture
Raw operational data stays inside the corporate environment. Only the protected capsule crosses zones, and restored output is reconstructed locally, inside the workflow it came from.
ZONE 01
Corporate Internal Network
The operational systems already live here. Capsule reads your existing systems in place with one API call.
ZONE 02
Data boundary: structure-preserving substitution
Detection finds the values you defined as sensitive. Structure-preserving, DP-based substitution replaces them while keeping structure and context intact, and only the protected working version leaves your environment.
ZONE 03
In-House Team
Governance and routing choose between an approved external LLM (ChatGPT / Claude / Gemini / in-region EU models) and an on-prem local model. Organizational policy and domain context stay intact.
ZONE 04
Local: Auto Reconstruction
Inside the organization, the AI response is auto-restored to original values. Data that left the boundary cannot be reconstructed outside, and business-ready output goes back to the workflow it came from.
Positioning
Built to enable AI work,
Built to enable AI work,
not to police it.
AI gateway
Manages model traffic: routing, auth, fallback, caching, rate limits, cost, observability.
DLP
Detects, classifies, or blocks sensitive content.
Employee AI
Helps workers search, chat, and automate tasks across company apps.
LLM Capsule
It changes what your workflow actually sends to the model. Data becomes a restorable capsule before it goes out, and comes back restored, inside your environment.
Gateways route the call. Capsule changes what crosses the model boundary.
Proof
Validated in production environments
Telecom · Industrial cybersecurity · Healthcare · Finance · Public sector · Legal · Cloud sovereignty
0.12s
Per-page processing
2,200-character document
2,200-character document
Exact
Exact, repeatable reconstruction
inside your environment
inside your environment
98%
Output similarity
vs. processing on raw original · internal benchmark
vs. processing on raw original · internal benchmark
FAQ
Frequently asked questions
A context-preserving data layer for AI lets AI work on operational data that cannot move raw. Sensitive values become DP-based substitutes. Document structure, relationships, and meaning stay intact, so models reason over real business context. Usable outputs reconstruct inside your environment, and the original values and reconstruction mapping never leave your boundary.
LLM Capsule turns sensitive values into DP-based, context-preserving substitutes before the request reaches the model. The model path can be external, but it sees only the protected working version. The original values and the reconstruction mapping stay inside your environment, where the response reconstructs.
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 reconstructs the output inside your environment.
Reconstruction happens only inside your organization. The model path can be external and sees only context-preserving substitutes. The original values and reconstruction mapping never leave your boundary. Reconstruction is a deterministic internal mapping, not a statistical recovery of differential-privacy values.
See LLM Capsule run on your own enterprise documents.
Bring your documents, deployment constraints, and one real workflow. We demonstrate it on your documents, in your environment, in 30 minutes.
Available on
AWS Marketplace