Why tickets come back asking for more information
Whoever hits the problem rarely knows what IT needs: the failed request, the console message, the environment. Capto captures it automatically while the person reproduces the issue, and AI rewrites the report in technical language.
The ticket before and after Capto
| Without Capto | With Capto | |
|---|---|---|
| What the user sends | “The system gave an error” | One sentence + the recorded reproduction |
| What IT has to ask for | Screenshot, browser, steps, time | Nothing: it comes with the ticket |
| Technical context | Missing | Requests, console and environment |
| Ticket text | The user’s own words | Rewritten by AI in technical language |
What support teams get
- A Chrome extension, no training: describe, record, send.
- The ticket lands in the Jira Service Management queue, Jira Software or Azure DevOps, with video and evidence.
- Works on any web app: ERP, CRM, internal portals, customer systems.
- Each person sees their own tickets; admins see the whole company’s.
From support to code
If the development team connects the repository, AI code analysis comments on the ticket with where the problem is in the code and how to fix it.
Frequently asked questions
Does the end user need technical knowledge?
No. They write what they were trying to do and reproduce it. Everything else is captured automatically.
Does it work with our ERP?
It works with any system that opens in Chrome (or Edge and Brave), internal or in the cloud.
Does sensitive data go to the AI?
Before sending anything to the AI, Capto masks fields such as passwords, tokens and authorization keys.