AI can plan, write and design — but it can't paint your nails, fix your sink or deliver a parcel. Jobbit Labs captures how real work actually gets done, and turns it into the datasets and models that take AI beyond the screen.
Every task on the Jobbit platform — AI or human — runs through one loop. The data isn't collected after the fact; it's a byproduct of operating.
Our in-house orchestration layer routes each task to the right tool, agent or vetted human.
AI generates what it can; vetted experts handle the real-world work at the end of the chain.
Instructions, photos, video, actions taken, points of uncertainty and final outcomes — captured by default.
Routing, pricing and matching sharpen with every completed task, compounding the dataset's value.
Anyone can bundle tools. Almost no one owns the loop. Because we own both the agents and the freelance network they book, every task runs inside our system and stays there. That closed loop is the moat — and the data engine underneath it is what Jobbit Labs exists to build on.
Frontier labs train on the public internet. There is no public record of how people actually perform real-world tasks — step by step, with context, corrections and outcomes. We produce exactly that.
World models — AI that predicts the outcome of actions in the physical world — are widely regarded as the next major step after language models. Meta, Nvidia and Tesla are building them. The constraint isn't compute. It's data. We're building the models on top of the dataset they're missing.
JEPA-style models that learn physical cause and effect from recorded task trajectories — predicting how real-world actions play out before they happen.
Our in-house reasoning layer already routes every Jobbit task. Each completed task is a labelled training example that makes routing, pricing and matching sharper.
Ground-truth outcomes from real tasks — did the job get done, on time, to spec — give us evaluation signal that no synthetic benchmark can replicate.
We're early — and deliberately so. We're opening conversations with AI labs, robotics teams and enterprises who need data the public internet doesn't contain.
Anonymised, structured real-world task data — licensed for training and evaluation, or collected to your specification through our live platform.
Enterprise licensing of the orchestration layer behind Jobbit — the routing, memory and credit infrastructure we built in-house — plus a developer API for teams building on top of it.
Joint work on world models and human–agent execution with academic and industrial research groups. If you're working on AI that acts in the physical world, we should talk.
The bottleneck is data on how real work gets done. We're producing it every day — let's talk about what you need.