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Trusted by Defense, Intelligence, and Financial Institutions
Mathematically-enforced encrypted inference deployed in the most demanding security environments. Zero-trust architecture validated by independent security agencies.
Encrypted OSINT Platform
The Challenge
“Analysts needed to query classified data without exposing what they're searching for. Pattern recognition by the cloud host was a non-starter. This is a math problem, not a policy problem.”
Traditional database encryption required decryption for querying, creating massive insider threat and OPSEC vulnerabilities. Confidential computing still decrypts data inside enclaves - not sufficient for classified workloads.
The Results
Impact & Achievement
- Enabled cloud deployment of intelligence workload previously restricted to on-premises
- 60% cost reduction vs. isolated on-premises systems - cloud economics, on-prem security
- Zero operational security incidents since deployment - zero breach liability
- Validated by independent national security agency - mathematical guarantees, not contractual promises
- CKKS-based encrypted biometric analysis - 5M records, 8 inferences/sec
- Foundation for national defense deployments
Impact & Achievement
- Proved encrypted inference viability at consumer scale (hundreds of millions of users)
- Validated mobile device performance with mathematically-enforced privacy (no server-side decryption)
- Demonstrated ARM64 optimization effectiveness for encrypted compute
- Provided reference architecture for encrypted search at scale
10M Record Encrypted Mobile Search
The Challenge
“Prove encrypted inference works at consumer scale with sub-second response on standard mobile hardware.”
A leading global mobile device manufacturer required an encrypted search mechanism with mathematically-enforced privacy that could scale to hundreds of millions of users without sacrificing performance.
Encrypted Biometric Matching at Scale
The Challenge
“We had a gallery of 5 million biometric templates too sensitive to upload to the cloud. Our on-premises environment lacked the massive compute power for real-time matching across street camera feeds. We were effectively grounded until CypherAI enabled computation on ciphertext - with mathematically-enforced privacy, not just contractual promises.”
- Head of Biometric Security, National Infrastructure Authority
Facial recognition at scale requires high-compute cloud instances, but data privacy mandates forbade storing plaintext biometric templates in any untrusted cloud environment. Confidential computing still decrypts data inside enclaves - not sufficient for biometric data at this classification level. CypherAI's Sentinel platform enabled real-time encrypted similarity search across the entire gallery using homomorphic encryption, with zero plaintext exposure at any stage.
The Results
Impact & Achievement
- Real-time identification of up to 8 faces per second across a 5M encrypted template gallery
- End-to-end encryption of biometric data - gallery remains encrypted throughout matching
- Cloud-scale compute with mathematically-enforced privacy - not contractual promises
- Encryption keys never leave customer control - zero plaintext exposure to cloud provider
- Supports up to 3 million encrypted queries per day at production scale
- Deployed on AWS public cloud within customer's sovereign perimeter
Encrypted Fraud Detection
“Banks must run fraud checks on every transaction in milliseconds, but cannot expose customer PII to cloud infrastructure or AI model providers. Contractual compliance does not prevent insider threats or breach liability.”
Traditional fraud detection requires sending transaction data to cloud-based ML models, creating compliance risks under current regulations. CypherAI enables encrypted fraud detection with mathematical privacy guarantees - not contractual promises.
Technical Achievement
- Billions of transactions queried without decryption - zero plaintext exposure
- Real-time encrypted inference pipeline (<100ms target)
- Full PII protection throughout the entire model inference path - mathematical guarantee
- Cloud economics with zero data exposure - cryptographic compliance by design
Validation & Partnerships
Validated by 2 Independent Security Agencies
Post-Quantum Resilient Encryption (TFHE)
Foundation for National Defense Deployments
What Our Customers Say
“CypherAI's mathematical guarantee was the only approach that met our requirements. Confidential computing still decrypts inside enclaves - not sufficient for our classified workloads.”
- Lead Intelligence Architect, Government Agency
“We had 5 million biometric templates too sensitive to upload to the cloud. CypherAI enabled computation on ciphertext with mathematically-enforced privacy - not just contractual promises.”
- Head of Biometric Security, National Infrastructure Authority
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Deploy Encrypted LLM Inference in 30 Days
Schedule a technical deep dive with our cryptographic engineering team to discuss your security architecture requirements.