Remyx AI — ExperimentOps: What’s Next

Category	DevOps	MLOps	ExperimentOps
Focus	Software orchestration	ML lifecycle management	AI system continuous refinement
Primary Owner	Dev/SRE teams	ML engineers	AI & Product engineers
Optimization Goal	Uptime & reliability	Reproducibility & scalability	Learning velocity & product impact
Core Practice	CI/CD, monitoring, incident response	Model training, deployment, pipelines	Experiments, retrospectives, knowledge curation

Why Remyx

The Missing Layer in Your AI Stack

DevOps operationalizes code. MLOps operationalizes data.
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ExperimentOps operationalizes knowledge, closing the loop between experimentation and AI that delivers on business goals.

How it works

Align AI for Real-World Impact

Capture and surface the insights that make AI experiments repeatable, trustworthy, and ready for production.

Structured, Reusable Experiments

Collaborate with versioned workspaces to run traceable, reproducible experiments so every insight compounds.

Metrics That Reflect Your Reality

Customize evaluation criteria aligned for your users, business outcomes, and product vision - because context beats benchmarks.

Guided Learning Loops

Capture what changed, what worked, and why. Remyx recommends next steps to accelerate iteration and turn tribal knowledge into shared intelligence.

Alignment That Scales

Engineering, product, and business teams collaborate with a shared source of truth to align cross-functional efforts around validated experiments, compounding efforts across your organization.