Run AI models inside hardware-isolated enclaves. Every inference produces a cryptographic receipt proving exactly what happened — which model ran, what data was processed, and that it all happened inside an attested environment.
Standard AI APIs process your prompts and data in plaintext. The cloud operator has full access to inputs, outputs, and model weights.
There is no cryptographic evidence that a specific model processed your data, or that your data was handled according to policy.
Compliance relies on vendor promises and SOC 2 reports. No hardware-level enforcement, no verifiable receipts, no way to prove data handling to auditors.
EphemeralML reduces trust in cloud operators via hardware attestation, policy-gated key release, and cryptographically signed execution receipts.
The client verifies the enclave's identity and code measurements before sending any data. Hardware attestation confirms the exact software running inside the TEE.
Data is encrypted end-to-end. The model runs inside a hardware-isolated enclave. The host forwards ciphertext only — it never sees plaintext prompts or outputs.
Every inference produces an Ed25519-signed receipt covering model identity, input/output hashes, attestation evidence, and session lifecycle events.
Benchmark metrics (overhead, cost, quality) measured on AWS m6i.xlarge. Test and compliance counts from CI.
The receipt is the product. A compliance officer sends data to an endpoint, gets a result back, and receives a verifiable receipt they can show to auditors.
Verifies attestation, holds policy allowlists, establishes encrypted sessions
Networking, storage, API proxy. Forwards ciphertext only.
Decrypts data, loads models, runs inference, signs receipts
EphemeralML targets confidential computing platforms from AWS, GCP, and Azure.
If you run AI in a regulated environment and need verifiable confidentiality, let's talk about a pilot.