Your engineering team tracks work in Linear. Your customers report bugs through chat. Boei's AI agent bridges the gap, creating well-structured Linear issues from customer conversations with severity, labels, and reproduction context already attached.
When a customer hits a bug, they describe it in their own words during a chat conversation. That description usually gets relayed through a support agent, loses detail in translation, and arrives in Linear as a vague issue that engineers have to investigate from scratch.
Boei shortens that chain to one step. The AI agent talks to the customer, asks clarifying questions about what happened, and extracts the details engineers actually need: what the customer was trying to do, what went wrong, what browser or device they were using, and how severe the impact is. Then it fires a webhook that creates a Linear issue with all of that structured data.
Engineers open Linear and find issues with real context from real users. No telephone game, no missing details, no "can you reproduce this?" back-and-forth. The customer's words go straight into the issue description, and the AI adds structure around them.
One conversation, one issue, full context
A user hits a bug and reaches out through your website chat or any Boei channel. They describe what went wrong in their own words.
The AI asks smart follow-up questions: what were you trying to do? What did you see instead? What browser are you using? It builds a structured report from the conversation.
A webhook fires and creates a Linear issue in the correct project with the title, description, labels, priority level, and a link to the customer conversation.
The AI extracts what the user expected, what actually happened, and environmental details. Engineers get issues they can start working on immediately instead of spending time reproducing vague reports.
Issues get labeled based on what area they affect. Payment issues get the billing label. UI glitches get the frontend label. The AI categorizes based on the customer's description so engineers can filter their views.
Backend issues go to the Backend project. Mobile bugs go to the Mobile team. The AI identifies which part of your product is affected and routes the issue to the right Linear project.
A customer who cannot complete checkout gets an urgent issue. Someone with a cosmetic complaint gets a low priority. The AI sets Linear priority based on how the bug affects the customer's ability to use your product.
Customer-reported issues feed into your Linear cycles with real priority data. Your team plans sprints based on what users actually experience, not internal assumptions about what matters.
The issue description includes the customer's actual words alongside the AI's structured summary. Engineers understand both the technical problem and the human impact without needing to read a full chat transcript.
Customer conversations become structured Linear issues with severity, labels, and reproduction context. No middleman needed.