Most feedback tools tag your feedback. Distil reads it. Instead of labels like “feature request” or “bug,” you get structured problem statements with severity, affected users, and success criteria — ready for product decisions.
Manual tagging is inconsistent. Different team members tag the same feedback differently. One person's “bug” is another person's “improvement.” Over time, your categories drift apart and your data becomes unreliable. You end up with a taxonomy that means different things to different people — and no one trusts the counts.
Even when tags are consistent, they're too broad. “Feature request” covers everything from a minor UX tweak to a fundamental platform change. “Bug” covers everything from a CSS glitch to a data loss incident. You get counts without context: “47 feature requests this month” doesn't help you prioritize. Which features? How severe? Who's affected?
Sentiment analysis — the first wave of AI feedback tools — tells you how customers feel. That's useful for NPS dashboards, but it doesn't tell you what they need. The real product intelligence is buried in the details: what went wrong, for whom, how often, and what success looks like. Tags and sentiment scores flatten all of that into a single dimension. Product teams need the full picture.
You get labels. Not clarity.
Problem statement
Users cannot export data in bulk, forcing manual workarounds
Severity
High
User segment
Enterprise accounts
Frequency
12 reports
Success criteria
Users can export in bulk
You get a product-ready problem statement.
Four steps from raw feedback to structured, actionable intelligence.
Paste text from any source: support tickets, Slack messages, emails, call notes, survey responses. No formatting required — just raw feedback as it comes. Or auto-import from Zendesk, Intercom, and Slack with integrations.
Distil's AI reads your feedback and extracts a clear problem statement, severity level, affected user segment, success criteria, and category. Every piece of feedback becomes a consistent, structured card — not a tag.
Accept, edit, or reject each structured card. Every decision is documented with a timestamp. When AI gets something wrong, you correct it in place. Your product judgment stays in the loop — AI handles the tedious extraction, you handle the decisions.
When multiple customers report the same issue, evidence accumulates on a single card. Frequency becomes your priority signal. Instead of 12 scattered tickets about the same problem, you see one card with 12 pieces of evidence — and a clear indication of urgency.
Sentiment analysis was the first generation of AI applied to customer feedback. It answers one question: “Are customers happy?” That's useful for tracking trends on a dashboard, but it doesn't help product teams decide what to build next. Knowing that 34% of feedback is negative this quarter tells you there's a problem. It doesn't tell you what the problem is, who's affected, or what would fix it.
Categorization — or tagging — was the second generation. It answers: “What topic is this about?” Tools in this category assign labels like “billing,” “onboarding,” or “export functionality.” This is better than sentiment alone, but still not actionable. You know that 47 tickets mention exports. You don't know what specifically is broken, which customer segment is most affected, or what a successful fix would look like.
Structured extraction is the third generation — and it's what Distil does. Instead of classifying feedback into buckets, Distil reads the raw text and extracts specific fields: what's the problem, how severe is it, who's affected, how frequently does it occur, and what would success look like. This is the difference between a label and a brief. Labels tell you where to look. Structured extraction tells you what to do.
Most AI categorization tools stop at labels because extraction is harder. Categorization is a classification problem — assign one of N labels. Extraction requires understanding context, inferring severity from tone and language, identifying user segments from implicit clues, and synthesizing success criteria that the customer may not have stated explicitly. It's a fundamentally different AI task, and it produces fundamentally more useful output.
Distil goes further by connecting structured cards to your workflow. Auto-import feedback from Zendesk, Intercom, or Slack through integrations. Merge duplicate reports automatically so evidence accumulates. Push accepted cards to Linear or Jira when you're ready to act. The result is a complete pipeline from raw customer voice to engineering ticket — with AI doing the structuring and humans making the decisions.
The result: cards that Product can act on immediately, without needing to re-read the original feedback. Structured extraction doesn't replace your judgment — it gives your judgment better inputs.
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