Raw data isn't enough. We use AI to digest the DOM, Network, Console, and Visuals into perfectly classified, structured tickets.
Users click once. Engineers get clarity.
Other tools dump a zip file of logs on your engineers. We give you a finished report.
Users barely type anything. They just click. We silently capture the Visuals, Replay, DOM, and Network state. No forms, no classifications, no "is this a bug?" questions.
Our AI watches the replay, reads the logs, and analyzes the screen. It applies your team's custom instructions to generate tags, summaries, and reproduction steps.
Engineers get a perfect ticket. Classified. Tagged. Titled. Searchable. Filterable. They don't just see what happened; they understand why and where immediately.
Every engineering team has their own "definition of done" for a ticket. InContext respects that. You define the schema; our AI enforces it.
No more 'it broke' titles. We generate '500 Error on /checkout when currency is JPY'.
The AI decides if it's a Bug, Feature Request, or UI Glitch based on your rules.
Because the data is structured, you can finally aggregate and filter feedback effectively.
AI Summary
User clicked "Pay Now" after changing region to Japan. The frontend payload lacked the `currency: 'jpy'` field, causing the backend to throw a 500.
Component
CheckoutForm.tsx
Browser
Chrome 120 (Mac)
Join the engineering teams who have stopped playing detective and started fixing problems.