Know your
next move
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 learns from every evaluation, experiment, and production outcome to identify the next improvements most likely to drive measurable gains.
# stop guessing which change to make next.
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, numbers vary by repo and run
What it opened on real repos
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 works across your stack, recommending what to try next and turning every result into a record your next decision builds on.
The next change worth trying, ranked against your codebase and past experiments.
Your agents and CI ship it. Remyx ties every metric, commit, and ticket to the hypothesis.
Ship, iterate, or reject, with the rationale. Each decision becomes a record the next cycle builds on.
# an illustrative experiment record
Every result compounds what your team learns
Every evaluation, experiment, and production outcome becomes evidence. Remyx learns from those results to help teams identify promising improvements faster.
- 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 can gate 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.