LLM Capsule
CUBIG에 의해
LLM Capsule is a context-preserving data layer in enterprise LLM, RAG, and agent workflows.
Enterprises need AI to work on real business documents and records: contracts, financial reports, operational tickets, and internal logs. But these records contain specific context, entity names, deal terms, internal metrics, and org references, which lose value when stripped out before model execution. Remove the details, and the result no longer works as usable operational output. Keep them as-is, and raw business content goes straight to the model.
LLM Capsule sits as a specialized LLM document processing layer between your data and the model layer, carrying data through protected model execution to local output reconstruction. It replaces sensitive or controlled values with context-preserving substitutes before the data goes to the model. Tables, lists, hierarchies, entity relationships, section logic, and domain-specific markers stay intact. The model gets structured input.
After the model responds, LLM Capsule handles local output reconstruction to restore original business context. The result maps back to your environment and fits into the workflow as usable operational output without exposing raw data at any point.
Use LLM Capsule for:
Document summarization where structure and entity relationships must survive the round trip
RAG pipelines that need real business context in AI output without exposing raw data
AI agent workflows that act on contracts, reports, tickets, logs, or operational records
Enterprise AI use cases that need output the business can use without compromising data security
LLM Capsule is not an OCR tool, not a document extraction service, not a ChatGPT gateway. It is a context-preserving layer, optimized for business documents and data moving through Machine Learning and AI agent pipelines.