The Challenge
Enterprises generate massive volumes of sensitive documents every day: loan files, medical records, legal contracts, incident reports, and internal analyses. These are exactly the documents where AI could save the most time, because they are dense, repetitive, and slow to work through by hand.
But sending this data to an external AI service means the original content leaves the environment where it is allowed to live. For regulated teams, that single data path is enough to block the whole project, so the work that would benefit most from AI is the work that never reaches it.
What Enterprise AI on Sensitive Data Requires
Running large language models on sensitive enterprise data requires three capabilities working together:
- Pre-processing substitution. Sensitive elements must be identified and replaced before anything leaves the enterprise, so the original values stay inside the trust boundary from the start.
- Model-agnostic processing. The data layer must work with any LLM, so teams can route requests to the model they already trust instead of rebuilding their workflow around a single vendor.
- Output reconstruction. AI results are reconstructed locally, so the answer comes back in its real, usable form instead of the substituted version the model actually saw.
How LLM Capsule Enables This
LLM Capsule operates as a context-preserving data layer for AI. It sits between your documents and the model, so sensitive workflows can reach an LLM without the original values traveling with them.
Step 1: Sensitive detection. LLM Capsule automatically identifies the sensitive elements in a document, from personal identifiers to internal figures, contract terms, and other enterprise context that generic masking tools do not even see.
Step 2: Local substitution. Detected elements are replaced in place with DP-based, context-preserving substitutes. The structure and meaning of the record stay intact, while the original values stay inside your environment.
Step 3: AI processing. Only the substituted version crosses the boundary to the approved model, so the request runs on the LLM you already use while the original values stay inside.
Step 4: Local reconstruction. AI outputs are reconstructed locally through a protected mapping layer inside your environment, so the result returns in its real, usable form rather than the substituted one.
Key principle: the original values, and the mapping that reverses the substitution, stay inside your trust boundary. Only substituted data crosses to the model, and the usable answer is rebuilt on your side.
Enterprise Use Cases
Banks and insurance companies process loan applications, claims, and underwriting files that mix personal data with internal risk terms, so the document cannot be sent out as-is.
Hospitals and law firms use AI for medical record summarization and contract review, where the value of the output depends on the record staying accurate and structured.
Government agencies and defense organizations work with classified and controlled material that is not allowed to leave a defined environment under any circumstance.
Infrastructure companies analyze vulnerability logs and operational data that reveal how critical systems are built, so any data path out is a risk in itself.
Use AI on Your Sensitive Data with LLM Capsule
Run enterprise AI on real documents while the original values stay inside your environment. Substitute locally, process with any model, and reconstruct usable results.
Run a sample proofView Product
Enterprise AI Enablement by CUBIG
Email : [email protected]
CUBIG LTD (United Kingdom)
Company Number: NI735459
Address: 21 Arthur Street, Belfast, Antrim, United Kingdom, BT1 4GA