Company / Origin
Where AI learns to work. Built by the team behind Vagon.
Pixelnode grew out of Vagon — the cloud computer platform 250K+ professionals run their daily work on. We capture that work, consent-first, and turn it into the training data and RL environments frontier AI needs.
Origin
We watched experts work for years.Then AI needed to.
Vagon started with a simple product: powerful cloud computers professionals could run their heaviest software on. Over six years it grew into a platform where a quarter-million designers, engineers, artists, and analysts do real, deliverable-grade work every day — on machines we operate.
When AI's frontier shifted from benchmarks to occupations, we realized what we were sitting on: the exact work models now need to learn, happening on infrastructure built to observe it. Pixelnode is that realization made into a product — capture the work consent-first, rebuild the machines as practice environments, and close the loop from imitation to verified skill.
Why this, why now. Three facts that picked the company.
T.1
Models ran out of internet text
The frontier has moved from exams to occupations. What's scarce now isn't compute or text — it's demonstrations of real professional work, with context, rationale, and results.
T.2
Real work happens in real software
Expert judgment lives inside SolidWorks, Ableton, Revit, and Excel — not in transcripts of it. Teaching models real work means capturing and rehearsing it in the software where it actually happens.
T.3
The expert network already exists
Vagon's platform hosts 250K+ professionals doing daily work on cloud computers. The supply side of the training-data problem was built before the problem was named.
How we work. Written down, because it constrains us.
V.1
Consent before capture
Nothing is recorded without explicit, revocable, session-level consent. The recording layer only works if the people being recorded trust it completely.
V.2
Real over synthetic
We capture genuine deliverable-grade work from working professionals. No synthetic imitations of expertise, no crowdsourced approximations of craft.
V.3
Instruments, not demos
We ship precise, reliable tools that experts and labs bet real work on — and we measure ourselves the way a verifier would: on outcomes.
V.4
Experts are partners
The professionals who teach the models are paid, credited in aggregate, and in control of their sessions. They are the supply side of this industry, not its raw material.
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