Unlock AI without exposing sensitive data.
Organizations want AI, LLMs, agents, and enterprise search. But AI needs access to sensitive data. 4Chains lets you build AI applications while data stays encrypted in storage and transit, with only tokenized or de-identified content ever reaching a model.
Run enterprise AI on data you could never expose.
Enterprise AI has to reach customer, financial, and internal knowledge to be useful. The moment it does, that data becomes plaintext to the model and the infrastructure around it. The most valuable use cases stall at the security review.
Put AI to work on your encrypted enterprise data. Data stays encrypted across storage, transit, and retrieval, and the model only ever sees tokenized or de-identified content, so AI reaches the data it was locked out of, without a moment of plaintext exposure.
Key benefits
AI without plaintext exposure
Sensitive values are tokenized or kept in-house, so prompts and outputs are never revealed in the clear to an external model or its host. Analytics like aggregation and filtering run directly on ciphertext.
Enterprise-ready
Sits inside your existing AI and data estate. No re-platforming, no data movement.
Works with your AI stack
Connects to the models and pipelines you already run, encrypted at every hop.
Privacy by default
Sensitive data stays protected by design, so security review accelerates instead of blocking.
A team wants an internal assistant over HR, finance, and customer records. With 4Chains, the assistant answers from that data without exposing it. Sensitive values are tokenized or kept in-house, so nothing confidential is decrypted in the clear to serve a response.
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Ground your LLMs in private knowledge, without exposing a document.
Your internal documents hold the most sensitive knowledge you have. Conventional RAG exposes them during indexing, retrieval, and inference, and routes plaintext to external models.
Build retrieval-augmented generation that never exposes the underlying documents. Knowledge stays encrypted through indexing and inference, and an LLM gateway keeps plaintext from ever reaching external models.
Key benefits
Encrypted knowledge search
Index and retrieve over ciphertext. Documents are never exposed to build the index.
Nothing leaves in the clear
An LLM gateway prevents plaintext from reaching external models, so answers are grounded without leakage.
Confidential by construction
Contracts, records, and IP power AI answers while remaining unreadable to the system.
A firm connects contracts and internal policies to an LLM assistant. Staff get grounded answers, while every source document stays encrypted and unreadable to the model.
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Let AI agents act on sensitive systems, without plaintext access.
AI agents increasingly act across enterprise systems on their own. Granting an autonomous agent plaintext access to sensitive data widens the attack surface and the odds of an incident.
Let agents read, reason, and act without exposing plaintext. Data stays encrypted at rest and in retrieval, sensitive parts are tokenized or handled in-house, and every action stays verifiable and auditable.
Key benefits
Safe AI automation
Agents operate without exposing plaintext: data stays encrypted at rest and in retrieval, and sensitive parts are tokenized or handled in-house.
Enterprise workflows
Compose tools and steps on a privacy-preserving stack built for real operations.
Privacy-preserving agents
Every action is accountable and auditable, meeting enterprise governance.
An operations agent triages and routes sensitive requests across systems. It acts on the data it needs without exposing it, sensitive fields tokenized or handled in-house, and every step it takes is logged and provable.
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Can you use this in your company?
Bring a real workload. We'll show you AI and analytics running on it, without it ever being exposed.