We think support teams who are manually copying feedback from their support tool into Linear are wasting their time. Title, description, customer email, impact notes, links back to the original conversation. Twenty minutes for a few pieces of feedback.
Then they have to remember to check Linear later to see if engineering closed it. Then they have to go back to the original customer and let them know it was fixed. Then they have to update both systems to reflect the resolution.
This is the workflow at many software companies. Feedback comes in through one or multiple systems, gets manually transferred to another system where engineers live, and then someone has to manually keep everything in sync.
It's not just inefficient. It's actively harmful to your product development process.
Why "Just Use Linear for Everything" Doesn't Work
Some teams try to solve this by using Linear (or Jira or GitHub Issues) for feedback collection too. Just skip the middle step and have customers create issues directly.
This is worse.
Linear is built for engineering workflows. It's built for teams who think in sprints and epics and technical tasks. It's not built for customer-facing feedback collection.
Your customers don't want to file a GitHub issue just to tell you the export button is broken. They want to click a feedback widget, type a sentence, and go back to what they were doing.
Your support team doesn't want to wade through 1200 Linear tickets mixing customer feedback with internal engineering tasks. They want a clean inbox of customer messages.
Your engineers don't want their Linear board cluttered with dozens of raw customer feedback items that haven't been triaged or prioritized. They want curated issues that are ready to work on.
The right tool for collecting and managing feedback is not the same tool for managing engineering work. They have different requirements, different users, different workflows.
The solution isn't to use one tool for everything. It's to make the two tools work together seamlessly.
What Seamless Integration Actually Looks Like
When feedback integration is done right, here's what happens:
Customer submits feedback → Your feedback tool (with its customer-friendly widget, AI analysis, impact scoring, and context collection) receives it.
AI automatically analyzes it → Impact score (1-5), sentiment analysis, categorization, translation if needed. All the smart stuff that helps you prioritize.
Automation rule triggers → You've set up a rule: "When feedback is high-impact (4 or 5) AND type is bug or feature request AND not from free tier, create Linear issue automatically."
Linear issue is created instantly → Title comes from the AI-generated summary. Description includes the customer's exact feedback, their email, the impact score, and a direct link back to the full context. Priority is mapped from impact score. Type label is added automatically. It's assigned to the right team.
Engineering works on it → They see all the context they need, right in Linear. If they need more details, the link takes them directly to the original conversation in your feedback tool.
Engineering closes the issue → Linear marks it as complete.
Webhook fires immediately → Your feedback tool gets notified that the Linear issue closed.
AI generates customer notification → Instead of a generic "your issue is fixed" message, the AI reads the Linear issue resolution, understands what was actually done, and drafts a personalized response: "We've fixed the export bug you reported—CSV files now download correctly for large datasets. Go ahead and try it out!"
Customer gets notified automatically → They see a real, contextual update about their specific issue. No generic templates. No manual work.
All of this happens automatically. The PM doesn't copy anything. The support team doesn't manually notify anyone. Context flows in both directions without friction.
How We Built This with Linear
When we built the Linear integration for Feedbackview, we focused on three principles:
1. Preserve all context, always
When an issue is created in Linear from feedback, it includes:
- The customer's exact words (not a summary)
- Their email and account details (optional)
- The AI-generated impact score, mapped to Linear's priority field
- Automatic labels for feedback type (Bug/Feature/Question) in colors that make sense
- A direct link back to the full conversation in Feedbackview
- Any relevant metadata (what page they were on, what plan they're on, etc.)
Nothing gets lost in translation because there is no translation. The data flows directly.
2. Make automation smart, not just automatic
You don't want every piece of feedback creating a Linear issue. That would be chaos.
Instead, you set up automation rules with conditions:
- Only create issues for feedback with impact score 4 or 5
- Only create issues for specific feedback types (bugs and features, not questions)
- Only create issues from paying customers
- Only create issues that contain specific keywords
- Any combination of the above
The automation is smart enough to know what should become an engineering task versus what should stay in the feedback tool for support to handle.
3. Bi-directional sync without manual work
The integration isn't one-way. When something changes in Linear, it updates the feedback system automatically:
- Issue closed in Linear → Feedback marked as resolved in Feedbackview
- If configured, AI generates a closure notification for the customer
- The notification includes what was actually done, pulled from the Linear issue
- Customer gets immediate context, not days later when someone remembers to check
This two-way sync means you have a single source of truth for the status of any piece of feedback that became a Linear issue.
Smart Defaults: The Details That Matter
Here's what makes or breaks a ticketing integration: the little details that prevent manual cleanup work.
Impact scoring → Priority mapping: Instead of dumping everything into Linear as medium priority, we map the AI-generated impact score to Linear's priority system:
- Impact 5 (Critical) → Urgent priority
- Impact 4 (High) → High priority
- Impact 3 (Moderate) → Medium priority
- Impact 1-2 (Low) → Low priority
This means the stuff that's actually urgent shows up as urgent in Linear, automatically.
Type-based labels: Feedback marked as "Bug" in Feedbackview gets a red Bug label in Linear. Features get a green label. Questions get blue. These are created automatically if they don't exist.
Your engineering team can see at a glance what category of work this is, without you manually tagging everything.
Default workflow state: Issues are created in whatever state you configure (we recommend "Todo" in Linear). This means new issues show up where your team expects to find new work, not buried in some default backlog.
These defaults save hours per week by eliminating the cleanup work that normally happens after manually creating tickets.
When to Auto-Create vs Manual Handoff
Not every piece of feedback should automatically become a Linear issue. Here's how to think about it:
Auto-create Linear issues for:
- High-impact bugs (score 4-5)
- Feature requests from enterprise customers
- Anything that affects revenue or core functionality
- Feedback that mentions "broken," "error," "crash," etc.
- Repetitive requests (when 5+ customers ask for the same thing)
Keep in Feedbackview, handle manually:
- General questions (should be answered, not turned into issues)
- Low-impact feature requests (collect these, create issues when there's a pattern)
- Feedback from free/trial users (unless it's a critical bug)
- Anything that needs more customer context before becoming an issue
- Complaints or frustration that needs empathy, not engineering
The automation rules let you codify these decisions. You're teaching the system when to create issues versus when to wait for human judgment.
When Linear Integration Isn't the Answer
To be clear, not every team needs this. Direct ticketing integration makes sense when:
You're already using Linear (or Jira, or similar) for engineering work You're getting enough high-quality feedback that manual handoff is painful You have clear criteria for what should become an issue versus stay in feedback You want engineering to have full context without leaving their tools
It doesn't make sense when:
- Your dev team is tiny and you just talk to them directly
- You're pre-product-market-fit and everything is changing too fast
- You don't have clear processes for prioritization yet
- You're getting mostly questions, not bug reports or feature requests
The integration amplifies your existing workflow. If your workflow is still being figured out, adding automation too early can lock in bad patterns.
Final Thoughts
The friction between feedback collection and engineering execution has always existed. In small teams, you could manage it with conversations. As you scale, manual processes break down.
The solution isn't to use worse tools or hire more coordinators. It's to eliminate the friction entirely with direct integration and smart automation.
Your feedback tool should understand customers. Your engineering tool should understand development workflows. The integration should let them talk to each other without you playing telephone.
When that works, feedback that matters gets to engineering faster, with more context, and with less effort. Customers get notified when things are fixed instead of wondering if anyone cared about their report.
The copy-paste workflow made sense when integrations were hard. They're not hard anymore. There's no good reason to keep doing it manually.