Capture Feature Requests and Bug Reports Straight From Chat

Your customers tell you what they need during live conversations. Boei's AI agent listens, categorizes their input, and creates structured Notion database entries, so product teams never lose track of what users actually want.

Auto-create Notion database rows

AI categorizes feedback type

Full conversation context included

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Your Product Backlog Fills Itself From Real Customer Conversations

Product teams spend hours combing through support tickets and chat logs to find feature requests buried in conversations. Most of that feedback never makes it into a structured backlog. It sits in a Slack thread or gets forgotten after the chat window closes.

Boei changes that. When a visitor describes a bug or requests a feature during a chat conversation, the AI agent recognizes what they are asking for, extracts the key details, and sends a webhook to create a new entry in your Notion database. The entry includes the request type, description, priority hints, and a link back to the full conversation.

Your product team gets a clean, categorized feed of real user input in Notion, without anyone needing to copy-paste or manually triage chat transcripts. Feature requests land in your feature request database. Bug reports go to your bug tracker. Everything stays organized from the moment a customer speaks up.

From Chat Message to Notion Entry

Three steps, zero manual work

Customer Shares Feedback

A visitor reports a bug or suggests a feature through your website chat, WhatsApp, or any other Boei channel.

AI Agent Extracts and Categorizes

Boei's agentic AI identifies the feedback type, pulls out key details like severity or use case, and structures the data for Notion.

Notion Database Entry Created

A new row appears in your Notion database with the title, description, category, customer info, and conversation link. Ready for your product team.

What Product Teams Build With This

Bug Report Collection

When a customer describes something broken, the AI agent creates a Notion entry with reproduction steps, browser info, and severity. Your engineering team gets actionable bug reports without playing telephone.

Feature Request Tracking

Feature ideas from chat conversations become database entries with use case descriptions and customer context. Vote counts and prioritization happen naturally as similar requests stack up.

Knowledge Base Gap Detection

Questions that the AI chatbot cannot fully answer signal missing documentation. Those gaps get logged to a Notion database so your content team knows exactly what pages to write next.

Customer Feedback Wiki

Create Notion pages with full conversation transcripts organized by topic. Product managers can browse real customer language when writing specs, instead of guessing what users meant.

Feedback Categorization Dashboard

The AI tags each entry as bug, feature request, UX issue, or praise. In Notion, you can build views that show trends over time, helping you spot patterns across hundreds of conversations.

Sprint Planning Input

Filter your Notion feedback database by date range and category to pull a prioritized list of customer requests into your next sprint. Real demand drives your roadmap.

Frequently Asked Questions

Is this a native Notion integration?

The connection works through webhooks and Zapier. Boei's AI agent sends structured data via webhook when it identifies feedback during a conversation, and that data flows into your Notion database through Zapier or a direct Notion API connection.

Which Boei plan do I need for webhooks?

Webhook integrations require the Growth plan at $49/month (annual billing). This plan includes 3 chatbots, 7,000 AI credits, and API access alongside the webhook functionality.

Can the AI tell the difference between a bug report and a feature request?

Yes. Boei's agentic AI analyzes the conversation context to categorize feedback. It distinguishes between bugs, feature requests, general questions, and other feedback types before sending the data to Notion.

What data gets sent to Notion?

You control the fields. Typically this includes the feedback title, description, category, customer name and email, the page they were on, and a link to the full conversation transcript.

Does it work with existing Notion databases?

Yes. You map the webhook output fields to your existing Notion database columns. No need to create a new database or change your current workflow structure.

How quickly do entries appear in Notion?

Webhook delivery is near-instant. Most entries appear in your Notion database within a few seconds of the AI agent identifying the feedback during conversation.