The bottleneck isn't building.
It's knowing what works.
Remyx gives your team a systematic way to find approaches worth testing, validate them against your system, and compound each improvement into the next.
Idea to Production, Systematically
Experiment with confidence.
Integrate discovery, building, and validation.
Discover
Find approaches worth testing for your use case and spin up ready-to-run environments automatically.
Build
Measure changes against your production workload. Generate evidence for which approaches to pursue next.
Validate
Link every change to a measurable outcome. Know what differences matter.
Learn what works.
Remyx helps engineers test more ideas and helps leads know which ones drive improvements.
For ML Engineers
There's a new technique every week. But without a way to test it against your system, you won't know if it's better than what you have or what to try next.
For Team Leads
Your team is shipping changes fast, but you have no shared record of what's been tried, what worked, and where to double down next.
Built by practitioners, for teams building at the frontier
A team of mathematicians and award-winning ML innovators with a decade of experience applying AI in robotics, healthcare, content recommendation, and enterprise data/ml infrastructure.
Applied Mathematics, UC Berkeley. Former Solutions Architect at Databricks advising MLOps strategy from startups to Fortune 500. Award-winning ML innovator recognized by NVIDIA's developer community.
LinkedIn →
UC Berkeley. 10+ years applying ML in healthcare, robotics, and content recommendation at Riot Games, Tubi, Robust.AI. Open-source tools cited by Google DeepMind and used in peer-reviewed research.
LinkedIn →Talks, Pods & Writing
Conference talks, podcast conversations, and field notes on how AI teams go from experiment to production.
Active in the AI Community Open Source
We contribute open-source tools, datasets, and benchmarks across AI domains and the research community builds on them.