Some computer vision systems stop at detection.
Real production environments require something more durable: a reliable record of what happened, when it happened, and how the system arrived there.
Sertn is built around making inference verifiable.
Claude agents closing real deals + memory getting stronger→ AI is moving from tools to actors.
The moment AI starts acting, not just responding, verification stops being optional.
At Inference Labs we are building the layer that makes AI actions verifiable.
Falls are one of the leading risks in elderly care, yet detection still often depends on delayed reporting or manual monitoring.
Sertn enables real-time fall detection with a verifiable record of what actually happened. Runs on your own infrastructure, with full control over models and data.
Not just alerts. Evidence.
AI systems are everywhere. Proof is not.
Sertn adds a verifiable record to every inference: model + input + output.
And the best part it is independently checkable. That’s the shift from outputs to trust.
1/ Normally, when a model says “here’s the result”, you have to trust it ran correctly.
With Proof of Inference, the system produces cryptographic proof that the computation actually happened.
Robotic boats are restoring coral reefs with AI-guided precision. Environmental autonomy is rising fast, but ecological robotics must be accountable.
Verifiable inference ensures interventions are transparent and safe.
We’re entering the phase where AI systems don’t just run, they have to be provable.
On Subnet-2, we’re now running JSTprove in production and scaling zk proofs for real ML workloads.
DSperse is now powering ML workloads on Subnet-2.
Slice models → prove parts → scale what used to be impossible.
This is what production zkML infrastructure actually looks like.
1/6 Season 2 of the TruthTensor Crucible is now closed!
The seasonal leaderboard locked.
The bonuses are credited.
The data is being reviewed.
What's been accomplished so far has been extraordinary. 🎉