ExperimentOps for AI Teams
Your team shipped 14 experiments last quarter. Three moved the needle. Do you know which three, and why they worked? Most teams can’t answer that. The reasoning lives in someone’s head, a Slack thread, or a notebook that left with the last engineer. MLflow logged the runs but not the decisions. Leadership asks “are we getting better?” and nobody has a concrete answer. Remyx is the system of record for AI experimentation. It captures what your team tried, why they tried it, what they learned, and what to do next. Every experiment builds institutional knowledge that persists through team changes. Over time, patterns emerge across experiments, and the team’s next steps become informed by everything that came before.Quick Start: Run Your First Experiment
Create an experiment, connect your tools, see results
The Problem
AI teams are experimenting faster than ever. New techniques ship weekly. Coding agents generate implementations in hours. But three structural problems prevent most of that effort from compounding: 1. Context disappears. An engineer spends two months testing retrieval strategies. The reasoning behind the final choice (why hybrid search won, what alternatives were tested, what tradeoffs were considered) lives in their memory and a few Slack messages. When they leave, the next person starts from scratch. 2. Patterns stay hidden. A team runs 14 experiments in a quarter. Five explored retrieval and all produced positive results. Three explored routing and none did. But each experiment is tracked in a different tool (a Jira ticket, an MLflow run, a Notion page) so the strategic signal across them is invisible. 3. Leadership has no portfolio view. A CTO managing three AI initiatives needs to know which are producing results. Getting that answer today requires scheduling meetings with each team lead and hoping they remember the details.How Remyx Solves This
Capture every experiment, including the decisions
Each experiment records where the idea came from (a paper, a repo, a model, a hypothesis, a production incident), the hypothesis, the target metric, and the observed result. It also captures the team’s decision after seeing results: ship, iterate, or abandon, and why. This is the context that MLflow doesn’t track.
Outcomes
Stay current without the noise
The pace of change in AI is outrunning every team’s ability to keep up. Remyx provides semantic search and personalized recommendations across papers, repos, models, and datasets, matched to what your team is building.
Search
See which directions are working
After enough experiments, Remyx groups them by direction and computes which themes consistently produce positive results. This turns a collection of isolated experiments into a visible strategy.
Insights
Core Workflow
- Experiments
- Discovery
- Insights
- Portfolio
Track outcomes, not tasksThe Outcomes view shows your team’s full experiment history with metric trends, decision logs, and linked artifacts:
- Timeline: All experiments with metric trend chart, status filtering, and search
- Detail: Full lifecycle of one experiment: origin, hypothesis, implementation, results, decision, and activity feed
Outcomes
Platform
Search
Semantic search across papers, repos, models, and datasets. Pre-built Docker environments for reproducibility.
Feed
Personalized daily recommendations matched to your team’s engineering challenges.
Outcomes
Track experiment outcomes, capture decisions, build institutional knowledge.
Insights
Cross-experiment pattern detection and recommended next experiments.
Overview
Portfolio view across all initiatives with health indicators.
Connectors
Connect GitHub, Linear, Jira, Slack, and Claude Code. Bidirectional sync via webhooks.
Why Learning Compounds
Traditional approach: Each experiment starts from scratch. Context lives in someone’s head. When they leave, the team loses it. ExperimentOps: Each experiment builds on the last. Decisions persist. Patterns emerge. The team gets smarter with every iteration, even as people change.| Quarter | Experiments | Pattern Detected | Outcome |
|---|---|---|---|
| Q1 | 14 experiments across 6 directions | Retrieval cluster: 5/5 positive, avg +3.2% | Resolution rate 34% to 52% |
| Q2 | 8 experiments, focused on 2 directions | Tool use + retrieval synthesis: 3/3 positive | Resolution rate 52% to 61% |
| Q3 | 6 experiments, precision targeting | Multi-hop retrieval: 2/2, avg +2.8% | Resolution rate 61% to 67% |
Access Remyx
- Studio
- MCP
- CLI
- API
Visual interface
- Experiment outcomes with timeline, detail, and portfolio views
- Resource discovery with search, feed, chat, and Docker environments
- Connector management and project configuration
- Team collaboration with comments and @mentions
Open Studio
Learn More
Quick Start
5-minute guide to first experiment
ExperimentOps Concepts
Deep dive into the methodology
Connectors
Connect your tools
Community
X
@remyxai
GitHub
Newsletter
Questions?
Email contact@remyx.ai