Your model. Trains itself. Ships better.
Open infrastructure for self-improving agents
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×
FIG. 02 / VELOCITY
48 h
FIG. 03 / SCALE
100B+
FIG. 04 / LABOR
0
Self-generating eval suites
Auto-instrumented agents
RL at 2,500+ environments
Serverless model inference
Serverless model inference
Production data flywheel
Environment Hub
Access and contribute to 2,500+ open-source RL environments and a community of researchers and developers.
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.
The model is the lab.
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
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.
Platform
HEAD OF APPLIED RESEARCH, M3
PRINCIPAL AI ENGINEER, ASDA
Compute. Find reliable compute operated globally from a single GPU to largest clusters.
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.
Liquid Reserved Clusters
Request large-scale clusters from 50+ providers. Sell-back idle GPUs to our spot market.
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
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
2.3
Direct assistance from our research and infra engineering team
Dedicated solutions engineer from cluster bring-up through steady-state
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
