Solution

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Solution

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Your model. Trains itself. Ships better.

Open infrastructure for self-improving agents

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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 at 2,500+ environments
Serverless model inference
Serverless model inference
Production data flywheel
Continuous improvement loops

Environment Hub

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

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Explore

My Stars

My Environments

Featured

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primeintellect

opencode-science

Solve science problems using OpenCode agent via…

science

opencode

Updated 8 days ago

v0.3.8

primeintellect

deepdive

DeepDive QA RL environment with a Serper-powered search tool

Updated 11 days ago

v0.2.5

stochi0

rubric-discovery

Meta-environment for learning rubrics from labeled…

rlm

training

Updated 2 months ago

v0.1.2

Verifiers

1 import verifiers as vf

2 vf_env = vf.ToolEnv(

3 dataset=dataset,

4 parser=parser,

5 rubric=rubric,

6 tools=tool_list,

7 max_turns=10,

8 )

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

Prime-RL

uv run rl \

--trainer @ examples/reverse_text/rl/train.toml \

--orchestrator @ examples/reverse_text/rl/orch.toml \

--inference @ examples/reverse_text/rl/infer.toml

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

Sandboxes

DEEPSWE-SANDBOX-1

PYTHON:3.11-SLIM

DEEPCODER-SANDBOX-1

PYTHON:3.11-SLIM

I3-MATH-SANDBOX-1

PYTHON:3.11-SLIM

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

The model is the lab.

The lab never sleeps.

01

Deploy

Ship your model to production with one command. Alliz instruments every inference automatically — no SDK changes.

02

Observe

The platform tracks where your model hesitates, fails, or gets corrected — and generates synthetic evals instantly.

03

Train

RL post-training runs continuously against your model's own failure surface — not generic benchmarks.

04

Improve

The platform tracks where your model hesitates, fails, or gets corrected — and generates synthetic evals instantly.

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.

OUR TEAM WORKED WITH

Platform

Everything a lab needs. None of the overhead.

MODULE: AUTO-EVALS

Evals that write themselves

No more curating benchmark datasets. Alliz synthesizes targeted evals from your production trace — every blind spot becomes a test case within hours.

MODULE: RL

2,500+ RL environments

The largest open-source RL environment library. Code, science, reasoning, tool use — filtered by what your model actually needs.

MODULE: COMPUTE

H200 to B300

Spot or reserved clusters. Unified across 50+ providers with InfiniBand networking and real-time observability.

MODULE: FLYWHEEL

Production → Signal

Your users are your labelers. Every correction and retry flows back into training automatically — no pipeline to build.

"We used to spend two weeks curating evals. Alliz generates better evals from failures in two hours. We just don't think about reward engineering anymore."

Alex Shevchenko

HEAD OF APPLIED RESEARCH, M3

"The insight: production failures are your best training data. Alliz automates the whole pipeline. Our model improves every week without a touch."

Sali Romanu

PRINCIPAL AI ENGINEER, ASDA

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

SLURM, K8s Orchestration

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

1.2

Infiniband Networking

Scale distributed training with high-bandwidth interconnects across nodes.

1.3

Grafana Monitoring Dashboards

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

Get compute

Single-Node

Multi-Node

B300

$4.99/HR

Available x2 · x1

288 GB VRAM · 480 GB RAM · 48 vCP

B200

$3.49/hr

Available x2 · x1

192 GB VRAM · 384 GB RAM · 32 vCP

H200

$3.14/HR

Available x2 · x1

141 GB VRAM · 182 GB RAM · 44 vCPUs

H100

$2.43/HR

Spot 0.94/HR

Available x2 · x1

80 GB VRAM · 185 GB RAM · 32 vCPUs

Enter GPU name..

$5.00/HR/GPU

TOTAL $2,560/HR

B300 SXM6 x 512

SXM6

3-YEAR RESERVED

Reserved cost (3yrs)

$67,276,800

Idle hrs resold

6,727,680 hrs

Cost of idle capacity

$33,638,400

Revenue at $8.00/hr/gpu

$53,821,440

Profit on idle capacity

+$20,183,040

Liquid Reserved Clusters

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

1.1

Get quotes from 50+ datacenters within 24 hours

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

1.2

Re-sell idle GPUs back to our spot market

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

1.3

Direct assistance from our research and infra engineering team

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

Get a quote

start computing

Integration

From zero to self-improving in an afternoon.

Point Pacer at your existing model checkpoint. We instrument your deployment, start observing production, and begin the first training cycle — typically within 48 hours.

First improvement: avg. 48 hours

pacer.toml

$ pacer init --model your-model-7b
✓ Model registered
✓ Inference instrumented
✓ Eval suite generated (312 cases)
 
$ pacer deploy --serve prod
✓ Deployed → api.pacer-intelligence.com/v1/your-model
 
# Pacer-Intelligence is now observing. Loop starts.
 
→ Cycle 1 complete — +18% on coding evals
→ Cycle 2 complete — +31% on coding evals
→ Cycle 3 running

Ready?

Your model should get smarter while you sleep.

start training

Operate your own AI lab.

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LEGAL

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Solutions

Agentic Coding

Frontier Research

Enterprise Automation

Robotics & Embodied AI

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