rroflow.

A little clarity goes a long way

Your AI does a lot.
See the whole story.

What happened? Where did it get stuck? What was worth keeping? Turn your agents’ activity into a clear picture you can actually use.

Checking member access…

From scattered activity to a clearer pictureConversation and tool cards connect to a central monitor, then flow into a reviewed finding.
Capture → understand → improve
Illustration of the workflow, not live activity.

Made for the person behind the prompts

Less digging. More “oh, that’s why.”

Start with the overview. Open the exact turn when you want the details.

01

Bring the work together

Link supported sources to your account. Find conversations, tool calls and received activity by agent, project or session.

Capture & activity
02

Find what needs a look

Use built-in signals or create your own. Follow a finding to its evidence and decide whether the rule got it right.

Signals & evidence
03

Keep useful corrections

Save your verdict against the exact captured turn. Those reviewed examples are the foundation for the learning work ahead.

Review & feedback

Connections, without the guessing

Start where your AI works.

A collector runs where the work happens. Its local queue keeps pending records for another delivery attempt when the connection returns.

Coverage depends on the source you connect. A subscription alone does not give RROFLOW access to every app or conversation.

Explore source setup →
Claude Code & CodexSession collectors

Read supported session files across projects for the linked operating-system user.

Claude, Codex & Grok SDKsInstrumented applications

Adapters record calls made through them. Compatibility tests use fixture providers; live-provider verification is still ahead.

ChatGPT, Grok & other subscription appsCoverage still being developed

Browser and mobile history is not automatically connected. Remote work must report from its execution source.

The next chapter · being built

Better examples.
Better learning.

The direction is automatic datasets, supervised learning from corrections, and unsupervised discovery of recurring problems. Proposed changes must be evaluated before promotion and remain reversible.

  1. Capture evidence
  2. Review & label
  3. Build a dataset Planned
  4. Evaluate & improve Planned

Your own space to understand the work

A workspace that belongs to you.

Account sign-in opens an isolated workspace. The member experience is being built alongside capture, signals and review. Free monitoring and paid dataset capabilities are planned; subscriptions and billing are not active yet.

Go to your workspace →
What is ready, and what is still ahead?

This development build includes activity, signal creation, evidence review and collector delivery reports. Public signup, subscription entitlements and the live Texas deployment still need end-to-end acceptance. Automatic dataset generation and training are not running.