Every AI API call processes your data in plaintext. The model sees the patient record. The provider logs the financial document. The inference server stores the privileged communication. Every single time.
H33 wraps AI inference in Fully Homomorphic Encryption. The model computes on ciphertext and produces ciphertext. It is mathematically incapable of seeing what it processes. H33-74 attestation proves correct execution.
You send plaintext to the model. The model processes it. The provider logs it. The inference server caches it. Your data has now been exposed to every layer of the stack. Compliance says "encrypted in transit." That's TLS. The model still sees everything.
When you call GPT-4, Claude, or any hosted model, your prompt arrives in plaintext at the provider's inference server. TLS protects the wire. It does not protect the endpoint. The model, the logging system, and every middleware between you and the GPU has full access to your data.
Running models on your own infrastructure moves the problem — it doesn't solve it. The model still processes plaintext. Your inference servers become a target. A breach of the GPU cluster exposes every input ever processed. The data surface is the same.
Even if you trust the provider, you cannot prove to an auditor, regulator, or court that the model never accessed your data in plaintext. Trust is not evidence. Compliance requires proof. Today, nobody has it.
Different AI models need different arithmetic. Neural networks need floating-point. Decision trees need exact integers. Classifiers need boolean gates. H33 provides a purpose-built FHE engine for each.
CKKS encodes floating-point vectors into polynomial rings with SIMD slots, enabling parallel computation across thousands of values in a single ciphertext. Neural network layers — matrix multiplications, activations, normalization — execute on encrypted data with controlled precision loss.
BFV operates on exact integers with no approximation error. Credit scoring, risk classification, rule-based decisioning, and threshold comparisons execute with bit-perfect accuracy on encrypted data. When the answer must be exactly right, BFV is the engine.
TFHE evaluates boolean circuits on encrypted bits. Binary classification, pass/fail determinations, flag-or-clear decisions, and bitwise comparisons run at gate level with programmable bootstrapping. Each gate refreshes noise, enabling arbitrary circuit depth.
Agent-Zero classifies documents — contracts, medical records, financial statements, legal filings — without ever seeing the plaintext. The document is FHE-encrypted before it reaches the classification model. The model processes ciphertext, returns an encrypted classification, and the client decrypts locally.
Client encrypts the document using H33's FHE SDK. The plaintext never leaves the client's boundary. The encrypted representation is a lattice ciphertext indistinguishable from random noise.
Agent-Zero's classification model processes the encrypted document. Feature extraction, embedding computation, and classification scoring all execute on ciphertext. The model is mathematically incapable of seeing the document content.
The encrypted classification result returns to the client for local decryption. An H33-74 attestation proves the computation was correct: input hash committed, model version committed, output hash committed, authority signed with Dilithium.
From client encryption to verified result, the plaintext never exists outside the client's boundary.
Your existing AI call, wrapped. FHE encrypts inputs before they touch the model.
from h33 import EncryptedInference # Initialize with your preferred FHE engine engine = EncryptedInference(engine="bfv") # or "ckks", "tfhe" # Your data never leaves your boundary in plaintext encrypted_input = engine.encrypt(patient_record) # Model computes on ciphertext — never sees plaintext encrypted_result = engine.infer( model="classification-v3", input=encrypted_input ) # Decrypt locally — only your key can read the result result = engine.decrypt(encrypted_result) # result.classification — the AI's output # result.attestation — 74-byte H33-74 proof of correct execution # result.model_version — committed model hash # result.verify_url — h33.ai/verify/<proof_id>
TEEs, differential privacy, and federated learning each address a piece of the problem. None of them prevent the model from processing plaintext.
Each approach has legitimate uses. None of them solve the core problem: the model processing plaintext data.
TEEs (Intel SGX, AMD SEV, ARM TrustZone) create hardware enclaves where code runs in isolation. But the data is still plaintext inside the enclave. Spectre, Meltdown, PLATYPUS, and LVI have repeatedly demonstrated that side-channel attacks can extract secrets from enclaves. TEEs protect against software attacks. They do not protect against hardware-level side channels.
Differential privacy adds calibrated noise to outputs to prevent reconstruction of individual inputs. This is a statistical guarantee, not a cryptographic one. The model still processes plaintext data — it just perturbs the output. Accuracy degrades with stronger privacy guarantees. And there is no proof that specific data was never accessed.
Federated learning distributes training across devices without centralizing raw data. But each local model still processes local plaintext data during training. Gradient attacks can reconstruct training inputs. And at inference time, the model processes plaintext regardless — federated learning is a training technique, not an inference protection.
Every industry that uses AI on sensitive data needs encrypted inference. Here's where it matters most.
Medical imaging analysis, diagnostic classification, and treatment recommendation models run on FHE-encrypted patient records. The AI produces results without accessing PHI. HIPAA compliance is cryptographic, not contractual.
Credit models, risk assessments, and fraud detection run on encrypted financial data using BFV exact arithmetic. The model scores applicants without seeing income, debt ratios, or account balances. Results are bit-perfect.
Contract analysis, due diligence, and litigation support AI processes encrypted privileged documents. Attorney-client privilege is maintained because the model is cryptographically incapable of reading the documents it classifies.
Classification models process encrypted intelligence reports. Analysts receive classifications without exposing source material to the AI system. Compartmentalization is enforced by mathematics, not by policy.
Claims adjudication AI processes encrypted policyholder data. The model triages claims, flags anomalies, and recommends actions without accessing personal health information or financial details in plaintext.
Transaction screening against sanctions lists runs on encrypted transaction data. The model returns match/no-match on ciphertext. Wire transfer details, beneficiary names, and account numbers are never exposed to the screening system.
Connect your model endpoint. H33 wraps it in FHE. The model processes encrypted data and returns proven results. No refactoring required.