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ROBOTICS

Robotics & Embodied AI

Scale simulation-to-real training pipelines with elastic GPU capacity and reproducible, sandboxed environments.

FIG. 01 / CAPACITY

1–256

GPUs on demand, per job

FIG. 02 / PROVIDERS

50+

Datacenters bidding on reserved capacity

FIG. 03 / REWRITES

0

Changes to your simulation stack

Environment Hub

Access and contribute to 2,500+ open-source RL environments and a community of researchers and developers.

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Verifiers

A library of modular components for creating RL environments and training LLM agents.

Prime-RL

A framework for asynchronous reinforcement learning (RL) at scale.

Sandboxes

For secure code execution optimized for large-scale reinforcement learning.

Embodied AI needs millions of rollouts. Run massively parallel simulation environments on elastic GPU capacity, and scale policy training without rewriting your stack.

Deterministic sandboxes make every rollout reproducible, so regressions in the policy are traceable to changes in the environment — not to infrastructure noise.

Export trained policies with full lineage — environment version, reward spec, and evaluation results — ready for deployment on real hardware.

Platform

Everything this solution needs. None of the overhead.

1

Elastic Simulation

Massively parallel simulation on elastic GPU capacity, from a single node to full clusters.

2

Deterministic Sandboxes

Reproducible rollouts make policy regressions traceable to environment changes.

3

Policy Lineage

Every export carries environment version, reward spec and evaluation results.

4

Ready for Hardware

Policies leave the lab packaged for deployment on real robots and fleets.

PI is the closed-loop AI stack where models observe their own failures, generate new training signal, and improve — without you writing a single reward function.

Deploying with

At industry standards

FIG. 01 / EFFICIENCY

3.2×

Avg. improvement per cycle

FIG. 02 / VELOCITY

48 h

First improvement deployed

FIG. 03 / SCALE

100B+

Parameters on-stack

FIG. 04 / LABOR

0

Reward functions written

  • Self-generating eval suites
  • Auto-instrumented agents
  • RL across 2,500+ environments
  • Serverless model inference
  • Real-time training observability
  • Production data flywheel

[ What teams say ]

"We used to spend two weeks curating evals. PI generates better evals from failures in two hours. We just don't think about reward engineering anymore."
2 wks → 2 hEval curation time

Compute. Find reliable compute operated globally from a single GPU to largest clusters.

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On demand

Instant access to 1-256 GPUs. Use your GPUs across clouds in a single platform.

  1. 1.1

    SLURM, K8s Orchestration

    Orchestrate dynamic workloads with enterprise-grade scheduling and container automation.

  2. 1.2

    Infiniband Networking

    Scale distributed training with high-bandwidth interconnects across nodes.

  3. 1.3

    Grafana Monitoring Dashboards

    Visualize metrics in real time with customizable dashboards for full system observability.

Liquid Reserved Clusters

Request large-scale clusters from 50+ providers. Sell-back idle GPUs to our spot market.

  1. 2.1

    Get quotes from 50+ datacenters within 24 hours

    One request, parallel bids for options, from H100, H200, to B200, B300, GB300 NVL72

  2. 2.2

    Re-sell idle GPUs back to our spot market

    Resell idle nodes on our spot market with no manual ops. Reclaim capacity instantly when you need it.

  3. 2.3

    Direct assistance from our research and infra engineering team

    Dedicated solutions engineer from cluster bring-up through steady-state

[ Ready? ]

Your model should get smarter while you sleep.

Engineers, at full stride. Point PI at your checkpoint — first improvement in about 48 hours.

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