An agent for the exact step
where your team loses time.
Connect Jira, Linear, GitHub or Salesforce Service Cloud, or upload an event log. TwinFlow shows where tickets wait, which work bounces back, and an estimate of what it costs. Then we build the agent for that step. Nothing changes in your tools until a person on your team approves.
Uses synthetic Client Onboarding data · No login · No risk
Illustration — connect your own tools after the demo.
How it works
Free scan, then a written audit, then an agent built for one step.
Scan
Free. Connect Jira, Linear, GitHub or Salesforce Service Cloud, or upload an event log with no account. See where tickets wait, which work bounces back, and an estimate of what it costs.
Audit
A written report from your own ticket data: where work waits, and which agent to build first.
Build
An agent scoped to that one step. It alerts, drafts and pre-fills; a person on your team approves.
Free to set up yourself: Slack alerts when a step stays stuck, and fix tickets created in Jira or Linear when you click (Linear: single-team workspaces).
See how your process compares
Anonymous rankings of TwinFlow teams with two or more scored scans.
Scans read your workflow history. The only write is a ticket you click to create, in Jira or Linear. Watchers hold no credential to the tools they watch; they post alerts to the Slack webhook you choose. Tokens are encrypted at rest with AES-256-GCM.
- Can't support a meaningful score? TwinFlow says “Insufficient Data” — it never fakes confidence.
- Names and emails are replaced with one-way tokens. Text you write yourself in uploaded files is stored as written.
Ready to see your process as it really is?
No account needed. Real PM4Py process mining on synthetic Client Onboarding data.