Build fast. Keep privacy in the stack
While you ship, Stoffel keeps the sensitive part inside the computation boundary
Usual stack
1
Encrypt data at rest on the device
2
Encrypt it in transit to your server.
3
Decrypt to run app logic.(Sensitive data is back in your stack)
4
Your app becomes the place users have to trust.
Stoffel stack
1
Encrypt data at rest on the device
2
Encrypt it in transit to your server.
3
Compute over shares.(Your logic runs across private shares)
4
Less sensitive user data sitting in your app.
Give your product clear privacy boundaries
Turn sensitive user context into shares. Open only the output your product needs
Compute the Result
StoffelVM runs on encrypted inputs. Nobody sees the raw data. Not you. Not us.


Ship trusted features with clear privacy boundaries
Compute over shares. Open only the result. Keep private details out of the center

AI / ML
Train together without pooling user data.
Compute model updates over shares while raw gradients stay distributed.
Key management
Generate keys without one holder.
Create and use key shares across teams or partners without appointing one dealer.

Questions teams ask before they build
Bring us the sensitive workflow. We’ll help you map the privacy boundary
Can't we just anonymize the data?
You already know the answer to this. The moment you join datasets—or someone subpoenas your logs—'anonymized' becomes 're-identifiable.' Anonymization is statistical hope, not cryptographic proof.
Won't this slow us down?
Compared to what? The three-month security review you're avoiding right now? The slowest part of building privacy-sensitive features is the meetings where you argue about whether you should.
Isn't this massive overkill for our use case?
Depends. Do you prefer 'Yes, but we promise to protect it' or 'No, our architecture makes storing toxic data impossible'?
This sounds like research-lab complexity
That's what we thought too. Then we actually tried it. Stoffel is production-ready infrastructure, not an academic prototype. You write logic in a domain-specific language that feels like defining a function signature. The MPC stuff happens under the hood. You ship like a normal developer.
What's the catch?
Performance overhead for encrypted computation (we're transparent about benchmarks). Learning curve for Stoffel-Lang (not Python, but not assembly either). And you have to actually care about privacy—if you just want legal cover, this isn't it.
