Code that compiles,tests attached.
Particula-Code generates code that compiles and passes your tests before it answers. Every response ships with the tests it passed, in Python, TypeScript, Go, or Rust. It runs next to your repo, on your hardware, so proprietary code stays put.
One job, done to spec.
Here is the spec.
Everything below is work the model handles on its own, with no wrapper, no retry loop, and no second call out to a larger model when it gets stuck.
Compiles before it answers
A sandboxed loop compiles candidates and runs your test suite. What comes back is the best candidate that passed, not the first thing sampled.
Python, TypeScript, Go, Rust
Trained on the four languages, not eighty. Depth over coverage: idiomatic output in the stacks production teams actually run.
Tests attached to every response
Each answer carries the tests it passed, so review starts from evidence instead of a diff and a hope.
Repo-aware context
64K tokens fits the files around the change. Point it at the module, not a snippet, and the output matches your conventions.
Context, latency, hardware,
and what it takes to run.
The numbers a capacity plan actually needs. Sizing gets reviewed against your traffic before the pilot, and the figures go into the agreement in writing.
From first call to production
without a rewrite.
Three steps, and you keep the weights at the end of them. Nothing about the rollout depends on us staying in the loop afterwards.
- 01
Discovery call
30 minutes, no slides. Your task, your data shape, and the security constraints around it. If this model is the wrong fit, we say so on the call.
- 02
Pilot install
Containerized deployment to staging in your VPC or on-prem, on the same stack as Lumen and Notetaker. The eval harness runs on your data and the numbers go into the pilot agreement.
- 03
Production rollout
Runbook handoff, operator training, and 90 days on call. After that it is yours to operate, no vendor dependency.
Five others share
this runtime.
They run on the same serving stack, the same deployment path, and one API, so licensing a second model adds no operational surface.
“Bring a hundred real examples from your pipeline. We run the model on them live, the harness scores the output in front of you, and you decide with numbers instead of a sales deck.”
Sebastian Mondragon, Founder of Particula Tech