Your team has thousands of support conversations, but you only ever review a tiny slice, so the real insights stay buried.
Turn every support chat into clear, useful intelligence
Conversation Analyst is a team of AI agents that reads your finished support conversations and reports what happened. You get a summary, a sentiment read, an action list, and a quality score for every single chat, not just a small sample.
Splits the transcript across four specialists
Customer mood turned negative after a second refund denial
Agent missed the required empathy step, score 7 of 10
Summary, sentiment, 2 action items, and QA score delivered
one run · manager plans, specialists report, you get one answer
The job, before you had a team for it.
This is the work Conversation Analyst quietly takes off your plate.
You review under 2 percent of chats by hand
Coaching is based on a lucky handful of calls
Follow-up promises slip through the cracks
Top complaints hide in unread transcripts
A team of agents, not one prompt.
A manager agent reads the goal and hands each part to a specialist built for it, then reconciles their work into one result.
Reads each conversation, splits the work across specialists, and merges their findings into one clear report
Scores how the customer felt and flags moments of stress like escalations or cancel threats.
Boils the whole conversation down to the core issue and what was decided.
Pulls out every promise and follow-up so no commitment gets missed.
Grades the agent against your service rules for fair, repeatable coaching.
How it runs.
Fork it, point it at your inputs, and let the team work.
Paste or connect a chat, call transcript, or email thread.
Add the quality standards your human reviewers already use.
The team scores mood, quality, and pulls out every action item.
Read a clear summary you can act on in seconds.
A frustrated customer disputes a double charge.
"Analyze this support chat and tell me how the customer felt, how the agent did, and what we still owe them."
- Summary: customer was charged twice and wants a full refund
- Sentiment: negative, turned tense after the first denial
- Action items: issue refund and send a follow-up email
- Quality score: 7 of 10, missed the empathy step
Where teams put it to work.
Grades 100 percent of chats to coach agents on real evidence.
Spots rising complaints and sentiment dips before they spread.
Learns which bugs and gaps customers name most often.
- A conversation transcript
- Your quality or scoring rules
- The sentiment scale you want used
- A short conversation summary
- A customer sentiment score
- A list of action items and follow-ups
- An agent quality score
What is conversation intelligence?
Conversation intelligence is the use of AI to read customer conversations and pull out useful meaning. It reports what happened, how the customer felt, and how well the agent did. It replaces the slow job of reviewing calls one at a time.
Instead of a sample, it reads every chat and turns raw talk into structured data like summaries, sentiment scores, and quality grades.
How is this different from basic support analytics?
Basic customer support analytics counts events like ticket volume and wait time. Conversation intelligence reads the content of the talk itself. It tells you the why behind the numbers.
That means you learn a customer left angry over a denied refund, not just that a ticket took twelve minutes to close.
New to this? Read our guide on how to build a sales battlecard.
Questions, answered.
What kinds of conversations can it read?
It works on chat logs, call transcripts, and email threads. If you have the text of the conversation, the team can analyze it.
Do I need engineers or a data team?
No. You describe what you want in plain English and the agents do the analysis. There is no model to train and no code to write.
Can I use my own quality rules?
Yes. You feed in the same standards your human reviewers use, so the scores match your existing process.
Fork Conversation Analyst and make it yours.
Start from a working team of agents, then swap the tools, tune the instructions, and run it in your own workflows.
Fork this template