Know your
next move
The decision layer between your AI coding agents and production.
Remyx helps you identify the next improvement worth making, filter out what doesn't apply, and learn from every result.
From signal to outcome
Teams generate more evidence than ever. Remyx turns it into the next change most likely to drive real impact.
# Remyx finds the changes that drive real impact, the next maxima in your system · illustrative
Teams ship to learn
Every team building AI ships dozens of changes a year across prompts, retrieval, tools, routing, and orchestration. Each one is a bet that the product gets better.
More changes than ever to weigh
Coding agents now propose changes around the clock, more than any team can test by hand.
The next edge is impact
Remyx finds the next best change and helps you ship and test it.
The next change worth making
Remyx ranks candidate improvements against your codebase, architecture, constraints, and past results. It recommends the highest-confidence opportunity or explains why no change is warranted.
- an implementable method matched to a call site in your code
- scored for confidence, gated on fit, reachability, and license
- across prompts, retrieval, tools, routing, orchestration
# illustrative funnel · counts vary by repo and run
What it opened on real repos
Real draft PRs Outrider opened on well-known public repos, with the gates it checked in plain sight. Open any to read the selection reasoning and the diff.
Turns repeated user corrections into a runtime check, so the agent stops making the mistakes you already corrected.
Lets a robot policy learn from imperfect demonstrations instead of throwing the messy data away.
Scores whether a fine-tune hit its goal without degrading what the model should keep, the core of safe unlearning.
Idea to production, systematically
Remyx closes the loop across your stack, recommending what to try next and learning from every result so the next recommendation is sharper. Hover a step to trace it through the cycle.
Every result compounds what your team learns
Every evaluation, experiment, and production outcome becomes evidence. Recommendations build on your real results, so each cycle starts sharper than the last.
- your eval suite, offline and A/B
- promote, comment, or stay silent, on your policy
- observe-only by default, earns autonomy on results
# you set the policy. starts in observe-only.
Works with your stack
The tools you already use, in one experiment record. More ship every month.
plan & ship
# planned, shipped, reviewed
measure & learn
# offline + online results
build & run
# implemented + executed
# Claude Code today, more providers soon.
Security that fits how you already work
Remyx runs server-side through a scoped GitHub App. Access is per repo and revocable, your keys never touch repo secrets, and a human gates every merge.
For teams building AI systems
The best AI teams don't stop at shipping. They measure, evaluate, and refine. Remyx turns evaluation results, experiment history, and production outcomes into a shared system for identifying and prioritizing the improvements most likely to drive better results.
Run better experiments
Remyx carries forward what your team has learned, helping you evaluate ideas faster and focus on the changes most likely to improve results.
- recommendations informed by prior outcomes
- context on every experiment
- faster validation of new ideas
Make better decisions
Remyx turns experiment results into organizational knowledge, helping teams prioritize work based on evidence instead of isolated findings.
- visibility across experiments and outcomes
- evidence behind every decision
- a shared system of record for improvement
Built by practitioners, for teams shipping AI in production
Mathematicians and award-winning ML practitioners, a decade applying AI in robotics, healthcare, recommendation, and enterprise data.
Salma Mayorquin
ceo & co-founder
Applied Math, UC Berkeley. Former Databricks Solutions Architect, startups to Fortune 500. Recognized by NVIDIA's developer community.
Terry Rodriguez
cto & co-founder
UC Berkeley. 10+ years of production ML at Riot Games, Tubi, and Robust.AI. Open-source tools cited by Google DeepMind.
Ready to decide with evidence?
Start free with Outrider and get your first recommendation in minutes. We're in early access with a first group of teams shipping AI in production.
# your next move, with evidence.