From every workflow
to better decisions.
8Flow maps the work in every app, then helps you measure it, label it, fix it and automate it. Your call at every step.
One refund, up close
The work on the left.on top.
What 8Flow learns on the right.below.
One refund, six apps, seven rounds of rework. The leader's view updates the moment it happens.
Zendesk
Policy
SalesforceOrders
Slack
JiraHow mapping works
Zero integrations.
Your data stays yours.
Nothing to build or connect. 8Flow stores metadata only, and screens stay in your own cloud.
tickets/7431openkb/refund-policyviewcases/5003updateRides along
A light extension and desktop app see which pages, systems and actions each workflow touches. No integrations to build.
Untangles the web
Activity across every app is stitched into one clear order of steps and hand-offs.
- Ticket opened0:00
- Policy checked1:10
- Account checked2:05
- Order found6:10
- Refund approved8:24
Maps every workflow
Each run becomes a timed, labeled workflow, so the same workflow compares across users, teams and vendors.
What you can do with it
Map the work once. Better decisions everywhere.
Measure it, turn it into labeled datasets, find what to fix, then automate. Your call at every step.
Measure it
Measure every team, every vendor, every workflow.
Every person, case and system, in real time and against its own baseline. In-house and BPO teams measured the same way, and every fix measured before and after.
- Right-size. 9 users carry the whole overnight backlog
- Discover bandwidth. 200 hrs a month hiding in order-ID lookups
- Plan the workforce. Move 3 users to the late shift to clear the queue
BPO Manila is 39% over its own baseline, all of it in the order lookup step.
Create labeled datasets
Every workflow becomes a labeled dataset you own.
The steps, the screens and your team's labels, drawn from the work your people already do. 8Flow stores metadata only. Screens stay in a private bucket in your own cloud.
- Build a dataset library. 50 "good example" refunds, saved to your bucket
- Label what good looks like. Solved first try, followed standard policy
- Train your agents. Hand labeled runs to Claude, ChatGPT or your own models
Steps
Screens
ZendeskSalesforceOrdersLabels
Your dataset library
labeled refunds in your own cloud, ready for Claude, ChatGPT or your own models.
Optimize your business
Find what to fix, and what it's worth.
Ocho learns what normal looks like for every person, team and vendor, flags what broke with the cause attached, and sizes every opportunity in hours.
- Fix workflows. Support re-keys refunds into Sheets: 120 hrs a month
- Upskill. Share Sam's habits with the rest of the SDR team
- Optimize systems. Order search slowed after Tuesday's update
- Flag data loss risks. 14 copies of customer data in unapproved tools
Automate
Turn workflows into agents, on the platforms you already use.
See exactly where people add judgment and where an agent can take over. Then hand the spec, and the labeled examples behind it, to your agent platform.
- Automate. Order-ID lookups first, ~200 hrs a month
- Spec agents. A Now Assist spec from 8,480 labeled refunds
- Deploy. To Now Assist, Agentforce, Copilot Studio or UiPath
See how your work
really gets done.
Start with one team. Your first workflows are mapped within 48 hours.