When an agent executes a trade, settles a payment, or votes in governance based on a model's output, "the model said so" is not good enough. On-chain, every other actor needs to be able to check the work without re-running it and without trusting the operator who ran it. Vitwit operates the compute layer that closes that gap.
AI is heavy and probabilistic. Blockchains are light and deterministic. Computation has to happen off-chain, which immediately raises the question every on-chain system has to answer: how does anyone know it was done correctly?
Centralized compute has no answer to the verification question. It just asks you to trust the operator — exactly the assumption decentralized applications exist to remove.
Inference designed so its results can be checked rather than trusted. ZK-friendly means the compute can be paired with zero-knowledge proofs — a cryptographic attestation that a specific model ran on specific inputs and returned a specific output, verifiable by anyone including a smart contract, without re-running the model or seeing the weights.
Deploy your own models on our infrastructure rather than being boxed into a fixed menu of hosted APIs. You control the model, the weights, and the version; we run the GPUs underneath. Fine-tuned, proprietary, and open-weight models all deploy the same way.
We operate across the decentralized compute and machine-intelligence networks the ecosystem already runs on. Akash gives you a permissionless GPU marketplace instead of a single cloud vendor's lock-in; Bittensor connects you to a decentralized network of machine intelligence. Years of production Akash experience, not a fresh integration.
On-chain AI is rarely just a model. The applications that need verifiable inference also need somewhere to settle results, relay messages, and often a chain of their own to run on. Splitting that across an AI vendor, an RPC vendor, and a validator vendor means three reliability stories and three teams pointing at each other when something breaks.
An agentic application can run its inference, broadcast through our endpoints, settle on a chain we help operate, and relay across IBC — all under one SLA and one on-call rotation. For teams building at the intersection of AI agents and on-chain payments, that vertical integration is the difference between a demo and something that survives contact with production.
Talk to the infra team →The hard part of on-chain AI is not getting a model to return an answer. It is making that answer trustworthy to a system that trusts no one by default, and keeping the GPUs underneath it running with the same reliability you'd expect from a validator that has never been slashed. If you're building on-chain AI and need compute that produces proofs, talk to our infra team and we'll scope what your application actually requires.