> ## Documentation Index
> Fetch the complete documentation index at: https://docs.remyx.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Introduction
> Remyx: the intelligence layer for AI experimentation
# 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.
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 projects 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
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.
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.
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.
The Projects view shows experiment velocity, hit rates, and metric trends across every project on one screen.
***
## Core Workflow
**Track outcomes, not tasks**
The 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
**Find what's relevant to your problems**
Semantic search and personalized recommendations surface techniques for your specific use case, with pre-built Docker environments that eliminate setup friction.
**See which directions compound**
Pattern detection groups experiments by direction and shows which themes consistently produce positive results. Insights also recommends what to try next based on your team's history.
**Visibility across projects**
The Projects view shows every project with health indicators, hit rates, and metric trends.
***
## Platform
Semantic search across papers, repos, models, and datasets. Pre-built Docker environments for reproducibility.
Personalized daily recommendations matched to your team's engineering challenges.
A scheduled action that opens reviewable draft PRs for the next paper worth implementing.
Track experiment outcomes, capture decisions, build institutional knowledge.
Cross-experiment pattern detection and recommended next experiments.
Portfolio view across all projects with health indicators.
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% |
By Q3: fewer experiments, better targeting, compounding results. The team works on the right things because the system learned what works.
***
## Access Remyx
**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
**AI-native interface for Claude Code and other MCP clients**
```json theme={null}
{
"mcpServers": {
"remyx": {
"type": "http",
"url": "https://mcp.remyx.ai/mcp",
"headers": {
"Authorization": "Bearer YOUR_API_KEY"
}
}
}
}
```
**Command line for scripts and CI/CD**
```bash theme={null}
pip install remyxai
remyxai papers digest --period today
remyxai outrider init --repo owner/name --auto-interest
```
**REST API for programmatic access**
```python theme={null}
import requests
headers = {"Authorization": "Bearer YOUR_API_KEY"}
resp = requests.get(
"https://engine.remyx.ai/api/v1.0/experimentops/experiments",
headers=headers,
params={"project": "Customer Support AI"}
)
```
***
## Learn More
5-minute guide to first experiment
Deep dive into the methodology
The discovery loop, run on a schedule
***
## Community
@remyxai
Email [contact@remyx.ai](mailto:contact@remyx.ai)