Data visualization
verify chart accuracy, axis scaling, and style.
Most codegen models can emit markup. Very few can design. We build the reinforcement-learning environments that teach structure, rhythm, tokens, responsiveness, and accessibility—so your models learn design sense, not just syntax.
developer@enterprise:~$ # Explore the world's only open-source enterprise browser
$ git clone https://github.com/wootzapp/wootz-browser.git
Cloning into 'wootz-browser'...
✓ World's only open-source enterprise browser
✓ Zero vendor lock-in, full transparency
$ cat README.md | grep -i security
🔒 Enterprise-grade DLP built-in
🛡️ Zero-trust architecture ready
🔍 Every line of code auditable
$ run build --production
Building enterprise browser...
✓ Hardened Chromium base
✓ Mobile-first security policies
✓ Ready for production deployment
The world's only open-source enterprise browser. Built on hardened Chromium with enterprise security, mobile-first design, and zero vendor lock-in.
🔓 Open Source
Audit every line
🏢 Enterprise
Production ready
📱 Mobile First
Android native
🛡️ Zero Trust
Built-in DLP
Design students and professional developers use our mobile browser to annotate real interfaces, capture structured specs, and score generated code. We synthesize those judgments into environments that any team can train or evaluate with.
Each environment ships with verifiable reward logic, structured specs, and stable interfaces so your teams can plug it directly into training or evaluation pipelines.
We focus exclusively on layout, tokens, responsiveness, accessibility, and component reuse—everything a designer critiques in code.
WootzApp gives LLM cos a managed, Chromium-based browser with embedded automation—complete with zero-trust isolation, programmable task routing, RL environment generation, and instant global payouts.
Trigger labeling, validation, and review flows natively while contributors stay productive and data quality stays governed.
A programmable rules engine coordinates task routing, consensus validation, and reward triggers so operations run in real time without manual oversight.
Low-latency rendering and offline-aware sync let distributed teams contribute from any device while your infrastructure stays observant and compliant.
Both formats deliver the same spec, reward logic, and validation artifacts. Pick the interface your RL infrastructure already speaks.
| Format | What you get | Where it fits |
|---|---|---|
| RL API (Dockerized) | Self-contained service exposing `/reset` and `/step`. Your agent submits code, the environment runs tests/scorers, then returns reward plus check breakdown. | RL training loops (PPO/GRPO/A2C), batch evaluation jobs, automated regression suites. |
| Verifiers-compatible package | Python environment implementing the Verifiers interfaces: dataset, rubric(s), and interaction protocol (e.g., `MultiTurnEnv`). Loadable via `verifiers.load_environment` and trainable with GRPOTrainer. | Enterprise evaluation stacks, Agent frameworks, or Prime Intellect workflows. verifiers.readthedocs.io |
Verifiers provides first-class primitives for custom interaction protocols, multi-criteria rewards (“rubrics”), and OpenAI-compatible model IO, plus a built-in GRPO trainer. Our packages adhere to these interfaces so you can drop them into existing pipelines without glue code. Learn more.
By running real code inside a real browser, we can turn human judgment into reward signals for more than HTML and CSS.
Because our browser runs the actual code, we can define rewards for scenarios where correctness depends on the rendered result.
Every rendered result becomes a measurable event. That’s how we turn the browser into the universal interface for human feedback in codegen RL.
verify chart accuracy, axis scaling, and style.
confirm DOM interaction, form fills, and task completion.
measure readability, formatting, and structure.
reward interactive correctness and frame behavior.
check plot alignment, execution flow, and output coherence.
Spec locks in grid (`2fr 1fr`), hero ratios, section order, tokens, and policy boundaries. Reward suites check structure, semantics, responsiveness, accessibility, and compliance.
Models practice grids, breakpoints, and spacing with human review loops built in.
Reward functions enforce typography, color, and component tokens your systems rely on.
Specs demand semantic structure, focus states, and motion-safe defaults across viewports.
Environment DSL keeps cards, rails, and promos composable instead of one-off markup.
Run training loops with our HTTP RL API or Verifiers’ GRPOTrainer. Detailed guides cover evaluation flows, scoring, and environment publishing.
Pick the next step that unblocks your team.