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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

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

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.

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Operate your own AI lab.

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