Join us at Experiment 2025

The bottleneck isn't building.
It's knowing what works.


Remyx gives your team a systematic way to discover what's relevant, test what's promising, and validate what improves production.

Try it free
Read the Docs
Featured by the AI community
How it works

Idea to Production, Systematically

Experiment with confidence.
Integrate discovery, building, and validation.

Email Classification - AI X+ Webflow Template

Discover


Generate environments from relevant ideas.

Integrated with More Apps - AI X+ Webflow Template

Build


Turn ideas into a testable changes.

Automated Reports - AI X+ Webflow Template

Validate


Stop shipping based on benchmarks and test against actual use.

Know What's Next

Each experiment lends more evidence to refine your next hypothesis

The space of possible improvements is too large to try everything. Remyx helps your team start from relevant prior work and build on what you learn.

EXP 1 First hypothesis EXP 3 Narrowing the search space EXP 10 Shared decision record
First hypothesis
You find a technique relevant to your problem. Remyx sets up a pre-built environment so you can test it against your application directly, instead of rebuilding it from scratch.
Narrowing the search space
Your past results inform what to try next. Your next experiment follows from evidence, from what your team has already learned, so you're building on real results, not reacting to hype or following hunches.
Shared decision record
Every technique tested, every result measured, every dead end recorded. New team members onboard from real experiment history. Leadership reviews the reasoning behind decisions alongside the outcomes.
EXP 1
First hypothesis
You find a technique relevant to your problem. Remyx sets up a pre-built environment so you can test it against your application directly, instead of rebuilding it from scratch.
EXP 3
Narrowing the search space
Your past results inform what to try next. Your next experiment follows from evidence, from what your team has already learned, so you're building on real results, not reacting to hype or following hunches.
EXP 10
Shared decision record
Every technique tested, every result measured, every dead end recorded. New team members onboard from real experiment history. Leadership reviews the reasoning behind decisions alongside the outcomes.

Your team's experiment history becomes the foundation for every decision after it.

Learn how ExperimentOps works
How Remyx Helps

Learn what works.

Remyx helps engineers test more ideas and helps leads know which ones drive improvements.

For ML Engineers

Keeping up with AI advances is a full-time job. Most publications don't ship production-ready code. Adapting an idea to your codebase takes days before you know if it's worth it.

Email Classification - AI X+ Webflow Template
Searching through ideas → Test relevant hypotheses
Career Growth - AI X+ Webflow Template
Stuck debugging → Measuring results
Reset Password - AI X+ Webflow Template
One idea at a time → Test more and iterate faster

For Team Leads

Your team is testing ideas, but without a way to validate ideas scientifically you can't tell what's working, what's been tried, or what to prioritize next.

Get Results - AI X+ Webflow Template
Ad-hoc experiments → Structured and repeatable experiments
Career Growth - AI X+ Webflow Template
Overreliance on intuition → Decisions backed by experiment data
Reset Password - AI X+ Webflow Template
One-off tests→ Compounding, reproducible improvements with science
From the Founders

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.

Salma Mayorquin
Salma Mayorquin
CEO & Co-Founder

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.

Enterprise MLData InfrastructureAI Strategy
LinkedIn →
Terry Rodriguez
Terry Rodriguez
CTO & Co-Founder

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.

RoboticsHealthcare AIContent Rec
LinkedIn →

Talks, Pods & Writing

Conference talks, podcast conversations, and field notes on how AI teams go from experiment to production.

🎙Podcast · Heavybit
ExperimentOps: Why MLOps Alone Isn't Enough
Salma unpacks the shift from MLOps to ExperimentOps with stories from Netflix, Yelp, and Stripe on bringing foundation models into production.
Salma MayorquinListen →
🎤Talk · MLOps Community
Beyond Benchmarks: Measuring Success for AI Initiatives
Why offline metrics don't predict online success and how to design evaluation systems that align model development with business outcomes.
Salma MayorquinWatch →
⚡Talk · AG2 Community
GitRank: Automating Paper-to-PR with Agentic Code Execution
Technical deep-dive on how GitRank uses AG2 agents to discover, build, and test research implementations from arXiv to live experiments.
Terry RodriguezWatch →
📰Feature · Cerebral Valley
Remyx: Your AI Production Assistant
In-depth interview on the mission behind Remyx, how the founders met at Berkeley, and their vision for the next generation of AI engineering tools.
Salma MayorquinRead →
✍️Blog · Substack
Vibes Don't Scale
Why AI-assisted coding falls apart past the demo stage and what disciplined experimentation looks like when complex code changes are under-determined by simple prompts.
Terry RodriguezRead →
🎙Podcast · Heavybit
ExperimentOps: Why MLOps Alone Isn't Enough
Salma unpacks the shift from MLOps to ExperimentOps with stories from Netflix, Yelp, and Stripe on bringing foundation models into production.
Salma MayorquinListen →
🎤Talk · MLOps Community
Beyond Benchmarks: Measuring Success for AI Initiatives
Why offline metrics don't predict online success and how to design evaluation systems that align model development with business outcomes.
Salma MayorquinWatch →
⚡Talk · AG2 Community
GitRank: Automating Paper-to-PR with Agentic Code Execution
Technical deep-dive on how GitRank uses AG2 agents to discover, build, and test research implementations from arXiv to live experiments.
Terry RodriguezWatch →
📰Feature · Cerebral Valley
Remyx: Your AI Production Assistant
In-depth interview on the mission behind Remyx, how the founders met at Berkeley, and their vision for the next generation of AI engineering tools.
Salma MayorquinRead →
✍️Blog · Substack
Vibes Don't Scale
Why AI-assisted coding falls apart past the demo stage and what disciplined experimentation looks like when complex code changes are under-determined by simple prompts.
Terry RodriguezRead →

Active in the Research Community Open Source

We contribute open-source tools, datasets, and benchmarks across AI domains and the research community builds on them.

🌐
From landmark paper to open infrastructure
We reproduced DeepMind's SpatialVLM data pipeline entirely with open-source tools, then trained models a fraction of the size, proving small, open models could learn spatial reasoning too. This enabled follow-on NeurIPS publications, new benchmarks, and a wave of open models across the field.
Google DeepMind · CVPR 2024 →
🎓
Datasets adopted by NeurIPS researchers
Oxford and UC Santa Cruz researchers trained and evaluated their models using Remyx open-source datasets.
Oxford / UCSC · NeurIPS 2025 →
🛠️
VQASynth: Open-source data synthesis pipeline
A toolkit for composing multimodal training datasets, used and extended by researchers worldwide to advance model capabilities.
GitHub · remyxai/VQASynth →
🤝
Partner integration with AG2 (formerly AutoGen)
Co-developed the RemyxCodeExecutor for AG2's agentic framework, enabling researchers to explore and test code from any paper.
AG2 Official Docs →
📈
30K+ downloads on community model releases
Lessons learned from publishing open models on Hugging Face and what makes research artifacts actually get adopted by practitioners.
Substack · Myx'd Results →
🌐
From landmark paper to open infrastructure
We reproduced DeepMind's SpatialVLM data pipeline entirely with open-source tools, then trained models a fraction of the size, proving small, open models could learn spatial reasoning too. This enabled follow-on NeurIPS publications, new benchmarks, and a wave of open models across the field.
Google DeepMind · CVPR 2024 →
🎓
Datasets adopted by NeurIPS researchers
Oxford and UC Santa Cruz researchers trained and evaluated their models using Remyx open-source datasets.
Oxford / UCSC · NeurIPS 2025 →
🛠️
VQASynth: Open-source data synthesis pipeline
A toolkit for composing multimodal training datasets, used and extended by researchers worldwide to advance model capabilities.
GitHub · remyxai/VQASynth →
🤝
Partner integration with AG2 (formerly AutoGen)
Co-developed the RemyxCodeExecutor for AG2's agentic framework, enabling researchers to explore and test code from any paper.
AG2 Official Docs →
📈
30K+ downloads on community model releases
Lessons learned from publishing open models on Hugging Face and what makes research artifacts actually get adopted by practitioners.
Substack · Myx'd Results →

Ready to stop guessing?

Start exploring relevant research and run your first experiment in minutes.

Try it free
Read the Docs