Your data is decrypted every time it's processed.
That's where breaches happen.

Every database query, every AI inference, every API call exposes plaintext. H33 eliminates the plaintext window with fully homomorphic encryption. One API call. No infrastructure changes.

Start Free — 1,000 Auths → See It Work

✓ HIPAA Ready ✓ HIPAA Compliant ✓ No infrastructure changes ✓ Production in weeks

YOUR DATABASE — PLAINTEXT

NAME	SSN	BALANCE
Sarah Chen	428-91-7735	$47,832
Marcus Johnson	551-43-2298	$12,401
Elena Rodriguez	672-18-4490	$156,220
David Kim	339-67-1184	$8,945

The problem

Encryption fails during processing.

Database queries require plaintext — a breach exposes everything
AI models process data in GPU memory — unencrypted
API calls transmit plaintext payloads
Compliance audits can't prove data was never exposed

What your database looks like today

NAME	SSN	BALANCE
Sarah Chen	428-91-7735	$47,832
Marcus Johnson	551-43-2298	$12,401
Elena Rodriguez	672-18-4490	$156,220
David Kim	339-67-1184	$8,945

Every field readable by anyone with access.

The solution

Process data without decrypting it.

H33 wraps your existing infrastructure with post-quantum FHE. Your databases, AI models, and APIs operate on encrypted data. The plaintext never exists on your servers.

Databases store and query ciphertext
AI models run inference on encrypted data
ZK-STARK proofs verify without revealing
One REST API call — no crypto expertise needed

Start Free Now →

Before H33

Sarah Chen 428-91-7735 $47,832

Marcus Johnson 551-43-2298 $12,401

Elena Rodriguez 672-18-4490 $156,220

After H33 — same database, same queries

a7f2c9e1d3... 3b8e1c4a72... f19d42b8c1...

d6c3e1f9a2... 7a9c3d6e18... c3d7a2f9e1...

b1f4e8c2d9... 4f2a7e1c83... 6e9b2d4a71...

Nothing readable. Nothing to steal.

The definition

What "encrypt data in use" means.

Encrypting data in use means processing it while it stays encrypted. Data at rest and in transit is already routinely encrypted — but the moment a program needs to read a value, that data has traditionally been decrypted into memory. H33 closes that window with fully homomorphic encryption (FHE): the server runs computation over encrypted data (ciphertext) without decrypting it, so the plaintext never exists on the machine doing the work. This capability is a supporting expression of the FHE Platform, which computes on encrypted data.

Why this exists: the riskiest moment for sensitive data is while it is being processed. A breach of a running database, an AI model's GPU memory, or an API payload exposes plaintext even when storage and transport are locked down. FHE removes the plaintext step from processing entirely.

One boundary to keep straight: FHE runs computation over encrypted data (ciphertext) without decrypting; the ZK Platform proves a statement is true without revealing the underlying data. Encrypting data in use is the FHE job, not the ZK job.

When to use it

Choose FHE when the data can't be decrypted to be used.

USE FHE WHEN

Databases must query regulated fields without exposing them
AI models run inference on PHI, PII, or financial data
Two or more parties analyze data neither should see in the clear
You need to prove data was never exposed during processing

DON'T REACH FOR FHE WHEN

You only need to prove a fact about data — use zero-knowledge proofs
The data can safely be decrypted inside a trusted environment you control
You need device or endpoint protection — that is a different security layer
The workload never touches sensitive data in the first place

Live demo

Watch a database get encrypted with one API call.

This is the actual transformation. Every cell. Every row. One request.

YOUR DATABASE — PLAINTEXT

NAME	SSN	ACCOUNT	BALANCE
Sarah Chen	428-91-7735	7291038456	$47,832
Marcus Johnson	551-43-2298	3847201956	$12,401
Elena Rodriguez	672-18-4490	9182736401	$156,220
David Kim	339-67-1184	5019284673	$8,945

Live demo — AI compliance

The AI diagnosed the patient. It never saw the data.

PATIENT RECORD

Name: Jane Doe
DOB: 1985-03-14
Rx: Metformin 500mg
Dx: Type 2 Diabetes

→

AI MODEL

Name: a7f2c9...
DOB: 3b8e1c...
Rx: f19d42...
Dx: 8c2a7d...

Processing on ciphertext...

→

RESULT

Live demo — encrypted data sharing

Two banks detect fraud together. Neither sees the other's data.

BANK A — ENCRYPTED SIGNAL

Card: e4b917a2f3...
Velocity: 9b1e4c8d32...
Location: 3c7a8f2e14...

BANK B — ENCRYPTED SIGNAL

Card: f8c3a7d1e9...
Velocity: 2e8f1b4a73...
Location: b4e1d9a3c7...

What happens next

Three steps. Zero risk.

You sign up free

60 seconds, no credit card

You get your API key instantly

Instant, no credit card

You're running encrypted compute

Full post-quantum stack from day one

Start Free Now →

Enterprise readiness

Production numbers. Not projections.

2.21M

Auth/sec sustained

35.25µs

Per authentication

±0.71%

Variance at scale

114

Patent claims pending

✓ HIPAA-ready architecture ✓ HIPAA-aligned ✓ Post-quantum secure

Start encrypting. Right now. Free.

Enter your work email. We'll send a free API key instantly. No sales call unless you want one.

✓ Free to start ✓ No credit card ✓ Live in 60 seconds

Company

Use Case

Select your primary use case

Healthcare / PHI Processing

Financial Services / Fraud

AI / ML on Sensitive Data

Biometric Authentication

Cross-Organization Data Sharing

Compliance / Audit

Other

Infrastructure Scale

Select scale (optional)

Under 10,000 auth/day

10,000 - 100,000 auth/day

100,000 - 1M auth/day

1M+ auth/day

Additional Context

Compatibility

Works with your existing stack

No rip-and-replace. No new infrastructure. One API call wraps what you already run.

Python

Node.js

Rust

REST API

AWS

GCP

Azure

PostgreSQL

FHE, precisely

Five questions about the encryption underneath.

What does FHE compute?

It runs computation directly over encrypted data (ciphertext) — queries, scoring, inference — and returns an encrypted result without ever decrypting. This is the primitive owned by the FHE Platform; the capability on this page is a supporting expression of it.

What does it not do?

It does not prove a statement without revealing data (that is zero-knowledge), and it does not decrypt during computation. The plaintext is never exposed on the server.

How is it different from ZK?

FHE runs computation over encrypted data without decrypting; the ZK Platform proves a statement is true without revealing the underlying data. Complementary primitives, not substitutes.

How does H33-74 fit in?

FHE produces the encrypted computation; H33-74 anchors the evidence of it as a portable post-quantum attestation. Neither owns the other — each remains verifiable on its own.

When should you choose FHE?

When data must stay encrypted while it is processed — encrypted database queries, private ML inference, cross-party analytics. Not when you only need to prove a fact (use ZK) or when data can safely be decrypted in a trusted environment.

FAQ

Frequently asked questions.

What does it mean to encrypt data in use?

Encrypting data in use means processing it while it stays encrypted. Data at rest and in transit is routinely encrypted, but data in use has traditionally been decrypted into memory so a program can read it. H33 uses fully homomorphic encryption to close that window: the server runs computation over encrypted data (ciphertext) without decrypting it, so the plaintext never exists on the machine doing the work. FHE is the primitive owned by the FHE Platform, which computes on encrypted data.

Is this the same as a zero-knowledge proof?

No. FHE runs computation over encrypted data (ciphertext) without decrypting it — the server produces an encrypted result and never sees plaintext. A zero-knowledge proof instead proves a statement is true without revealing the underlying data. Encrypting data in use is an FHE capability. H33 also offers ZK, but the two solve different problems: FHE computes on encrypted data, ZK proves a fact without exposing it.

When should I use FHE to encrypt data in use — and when not?

Use FHE when sensitive data must stay encrypted while it is processed: database queries over regulated fields, AI inference on PHI or PII, and cross-organization analytics where no party should see another's raw data. Do not reach for FHE when you only need to prove a fact about data without exposing it (use zero-knowledge proofs) or when the data can safely be decrypted inside a trusted environment you fully control. FHE protects the processing step itself; it is not a replacement for access control, endpoint security, or transport encryption.

Does H33 change my database or infrastructure?

No rip-and-replace. H33 wraps your existing databases, AI models, and APIs with post-quantum FHE through a single REST API call. Your systems store and query ciphertext and run inference on encrypted data; the plaintext never exists on your servers. FHE handles the encrypted computation, and H33-74 can anchor the evidence of that computation as a portable post-quantum attestation — the two are independent and neither owns the other.