Qalibr
Qalibr is an AI copilot that automates bug report intake by reading conversations from various channels, classifying them, and turning valid reports into structured tickets. It eliminates manual triage overhead by deduplicating, judging report validity, enriching with reproduction steps, and routing tickets to Linear—all while keeping human approval as a final gate. The system is designed to save engineering teams hours of manual work each week and close the feedback loop with the original reporter. When a bug is reported in a Slack thread or pasted from email, Qalibr processes the entire conversation, extracts the core signal, and checks for duplicates against existing tickets. It then applies a judgment engine—modeled after experienced QA engineers—to classify the report as a real bug, false positive, user error, missing context, or regression. Non-actionable reports are filtered out, while genuine issues are enriched with reproduction steps, assigned a confidence score, and routed to the appropriate project in Linear. Every ticket created requires a human to approve it at the gate, ensuring control over what reaches the backlog. Meanwhile, the person who reported the bug receives automatic updates about ticket status within the original thread. Qalibr supports native Slack integration, with plans to expand connectors, and offers a paste interface for other sources like email or chat. Pricing is subscription-based with a free trial, targeting teams that want to reduce manual triage and keep their backlog clean.
- Category
- Code
- Pricing
- Paid
Problem solved
Bug reports come in from multiple channels—Slack threads, emails, support tickets—but turning them into actionable, deduplicated, and enriched tickets for developers is time-consuming and error-prone. Qalibr automates the entire intake process: reading conversations, determining if a report is a real bug, avoiding duplicates, adding missing context, and routing it to Linear, all while keeping the original reporter informed. This saves hours of manual triage per week and prevents noise from clogging the backlog.
Use cases
- Automatically create Linear tickets from Slack bug reports
- Deduplicate duplicate bug reports to keep the backlog clean
- Classify reports as real bugs or noise (user error, false positive)
- Enrich tickets with reproduction steps and confidence scores
- Close the reporter feedback loop by notifying in the original thread
- Process bug reports from pasted email or chat conversations
Audience
- Developers
- Product Managers
- Startups
- Enterprise
Pros
- Turns messy conversations into structured tickets in ~8 seconds
- Reduces duplicate tickets by ~35%
- Encodes QA judgment to filter noise and false positives
- Keeps humans in the loop with an approval gate
- Closes the reporter loop by updating status in the original thread
Cons
- Currently limited to Linear as the ticket routing destination
- Native integrations first only for Slack; other channels via paste
- Paid service with no free tier beyond trial
- AI judgment may occasionally misclassify edge cases
- Requires configuration of QA rules for accurate judgment
Official website: https://qalibr.io/
Canonical LambdaBase page: https://www.lambdabase.com/tools/qalibr