Where AI learns to work.
Turn any workflow to a structural dataset to train AI models & deploy self training models with repeatable training environments.
Three main stages of model training.
All require deep-tier data to diversify.
Pretraining
Trillions of tokens of text teach language, facts, and reasoning patterns. But public text is finite, within a few years it runs out.
Supervised fine-tuning
Models train on the expert demonstrations and imitate the shown behavior, as a child learns from watching.
Reinforcement learning
Models output a result, get scored on the outcome, and update next time. Every run gets further than the last.
Model needs the actual work, not a description of it.
Volume is easy. Signal is rare. As few as 312 augmented human trajectories produced a 141% relative improvement over the base model - because every one of them was real work.
Synthetic sandboxes
Auto-generated tens of thousands of coding tasks, SQL sandboxes, API workflows, web-tool tasks, synthetic simulations.
Native work environments
Real software, real experts. Pixelnode generates deep-tier data with professional experts for long horizon tasks with verifiable outcomes.
The training layer for real work.
Turn expert knowledge into trained agents.
Data Suite
Record real expert work as replayable, frame-aligned training data: screen, input, voice, files.
Explore the Data Suite >Environment Suite
Boot real computers as RL environments, score every run, and scale to a fleet.
Explore environments >On-Prem Fine-Tuning
Run both suites inside your own data center. Train your own agents on your own work, and nothing leaves.
Explore on-prem >One expert hour becomes a structured training asset.
A task brief goes in. A complete, structured, replayable record of expert work comes out, synchronized to the frame and ready for training.
- Screen frames
- 84,960 @ 30 fps
- Input events
- 4,112 key · pointer · pen
- Think-aloud
- 31:04 transcribed
- File snapshots
- 10 restore points
- Privacy masks
- 3 regions redacted
- Alignment
- frame-locked
Repeatable environments. Verifiable results.
Checkpoint forking
Freeze minute 47 of a real expert session and let agents practice recovering - a thousand times.
Result Verifier API
The automatic referee. Programmable checks against files, app state, and exports.
Prebaked golden images
Versioned machine states. Compose a "level" per occupation in minutes.
Agent gateway
The same channel a human uses. Adapters for every major trainer.
Instant restore & fleet
Byte-identical restore in seconds, not VM-minutes. Parallel episodes, isolation, metering - across all major clouds and regions.
Your experts. Your data. Your agents. The same stack - inside your walls.
Local Capture
Deploy capture on company workspaces and record company-specific workflows.
On-Premise Data Storage
Capture data lives in your own data centers. It never leaves the building.
Custom Trained Agents
Agents do company work - trained on your own company data.
Private beta - now onboarding design partners