CypherAI
CypherAI
FLAGSHIP PRODUCT

Encrypted LLM Inference

Ask Anything. Reveal Nothing.

Deploy GPT-4, Claude, Llama, and custom models on classified and regulated data with zero plaintext exposure. Mathematically-enforced encrypted inference - cloud economics, on-prem security.

The Challenge

Defense and financial institutions cannot use cloud LLMs because prompts expose classified data. Traditional encryption requires decryption before processing - creating an unacceptable security gap.

The Solution

CypherAI enables zero-trust AI inference with mathematically-enforced privacy. Prompts are encrypted client-side, inference runs on encrypted tensors, and outputs are decrypted only by the user.

Core Capabilities

Prompts encrypted client-side
Inference on encrypted tensors
Zero plaintext exposure - mathematical guarantee
Works with existing LLM infrastructure
Compatible with GPT-4, Claude, Llama, and custom models
Similar latency to standard unencrypted inference

Why Not Confidential Computing or VPCs?

Confidential Computing (TEEs)

Data is still decrypted inside the enclave. Side-channel attacks, firmware vulnerabilities, and the cloud provider still has physical access. Trust-based, not math-based.

VPC / Isolated Deployments

Data is decrypted during processing. Insiders, admins, and infrastructure operators can access plaintext. Compliance is contractual, not cryptographic.

Industry Applications

Deploy AI for classified workloadsDefense & Intelligence
Use LLMs for fraud detection without data exposureTier-1 Finance
Apply AI to patient data with HIPAA guaranteesHealthcare
Maintain data sovereignty with cloud AIGovernment
Technical Dossier v4.2

Production-Ready Performance

Independent validation of our 400× speedup and production deployment metrics. 100% exact computation with similar latency to standard unencrypted inference. Post-quantum resilient encryption (TFHE).

Head-to-Head Comparison

Standardized 10 Million Record Query (Exact Matching Scenario).

Solution LibraryLatency (ms)ThroughputArithmetic AccuracyMaturity Status
Microsoft SEAL184,000 ms0.005 ops/sec
100%
RESEARCH
OpenFHE92,000 ms0.01 ops/sec
100%
ACADEMIC
CypherAI Production486 ms2.1 ops/sec
100%
PRODUCTION

Latency Scaling

As database size grows, traditional HE libraries experience exponential overhead. CypherAI maintains sub-second performance even at consumer-scale database sizes.

400× Faster than SEAL at 10M records
Post-Quantum Resilient Encryption (TFHE)

Latency Comparison (Log Scale ms)

Beyond LLM Inference

The same homomorphic encryption engine that powers encrypted LLM inference extends to search, analytics, biometrics, and multi-party collaboration.

Encrypted AI Training & Inference

Models train and run directly on encrypted data. Protect both training datasets and model intellectual property from exposure to infrastructure hosts. Mathematical guarantees throughout the pipeline.

Fraud Detection in Production

Encrypted Data Collaboration

Multiple parties collaborate on encrypted data without revealing raw information. Maintain full cryptographic isolation while deriving joint insights - math-enforced, not policy-enforced.

2026

Proven in Production

Millions

Records - <0.4s query

Government Agency

10M

Records - 0.48s latency

Mobile Manufacturer

5M

Templates - 8 faces/sec

National Infrastructure

Billions

Transactions - Zero PII

Tier-1 National Bank

Ask Anything. Reveal Nothing.

Deploy Encrypted LLM Inference in 30 Days

Schedule a technical deep dive with our cryptographic engineering team to discuss your specific performance requirements.