Support Thread Analysis
Every support ticket gets read and tagged by AI — what it was about, how the customer felt, how long it took — so product, engineering, and ops all work from the same clear picture.
Imagine receiving five thousand letters and having someone read every single one, then hand you a tidy tally: what people asked about, how upset they were, and what took longest to fix. Your support inbox already contains that story — this just makes it readable.
The problem, in plain words
Your support tickets are the most honest record you have of what is breaking. But nobody can read three thousand of them. So decisions get made from the tickets people happen to remember: the angry one from last Tuesday, the one the CEO forwarded. Product thinks the big problem is onboarding; engineering thinks it is the export feature; support knows it is billing but cannot prove it with a number. Everyone is arguing from anecdotes drawn from the same pile that nobody has actually counted.
What we set up
We run an extraction pass across your whole ticket history and every new ticket as it closes. For each one, the AI records what the customer wanted (the intent), what it was about (the subject), how the customer seemed to feel (the sentiment), a satisfaction estimate (a CSAT proxy — a stand-in for the survey score most customers never fill out), how long the resolution took, and which documentation was cited. Those fields roll up into views by product area, by release, and by customer group — and the same numbers feed both a live dashboard and a weekly summary, so every team looks at one source of truth. Every number can be clicked through to the actual threads behind it, so nobody has to take the summary on faith.
How it works, step by step
- A ticket closes
The finished conversation — question, replies, resolution — goes in for analysis. New tickets and the historical backlog both count.
- The AI reads and tags it
Intent, subject, sentiment, a satisfaction estimate, resolution time, and which docs were cited — each ticket becomes a row of honest data.
- Tickets roll up into views
By product area, by release, by customer group. Three thousand tickets become a handful of readable charts.
- Dashboards and summaries update
The live dashboard and the weekly summary draw from the same extracted fields, so no two teams see different numbers.
- You drill into the evidence
Every count links back to the real threads behind it. When a number looks surprising, the receipts are one click away.
What changes for you
Before: priorities get set by whoever tells the most vivid ticket story in the standup. After: product, engineering, support, and ops open the same dashboard and see the same counts — which topics are growing, which release spiked complaints, which fix actually shrank a category. Arguments shift from whose anecdote wins to what to do about a number everyone accepts. What it won't do: it won't decide your priorities — it shows you what customers are saying, and your team still chooses what matters most.