Every closed conversation becomes a row you can read
Define the fields you care about, the reason a customer left, whether the issue was resolved, and Boom pulls them straight from the conversation into typed columns you can sort, filter, and export. Passing ten thousand transcripts to a model by hand is hard. This is the part of Boom that does it for you.
A schema you define, filled from the conversation
You decide the columns. Boom reads each conversation and fills them, so a pile of chats becomes a table you can sort, filter, and take into your own tools.
- 01
You define the schema
Set up typed fields, churn reason, yes/no, number, category, and Boom extracts to that exact shape, not a generic summary.
- 02
It reads the whole conversation
Boom transcribes audio and reads images a customer sends in, so every field is filled from the full conversation, not just the text.
- 03
Typed columns on the dashboard
Extracted fields land as typed columns on the Engagements dashboard, a row per customer, ready to sort and filter across hundreds of conversations.
- 04
Export to CSV or Excel
Take the table in long, wide, or one-hot format to your warehouse, BI tool, or spreadsheet.
The conversations the system runs are the ones it reads
Extraction sits at the end of the loop: the conversations the workflow builder and inbox produced, read by the same system that ran them. So the data you get is grounded in real customer conversations, not a survey. The reason someone churned comes from the conversation where they said it.
- Reads the conversations the platform already ran
- Schema is yours to define and change
- Output is dashboards plus export, not a black box
| Customer | churn_reason | promise_date | plan |
|---|---|---|---|
| Mariana G. | price | 2026-07-03 | pro |
| Daniel R. | missing feature | none | starter |
| Priya S. | switched vendor | 2026-07-09 | pro |
| Tomás L. | price | 2026-07-12 | scale |
Extraction fits the rhythm of the conversation
- 1
On conversation close
When a conversation resolves, Boom runs the schema against it and fills the columns. The default path: a closed thread becomes a finished row.
- 2
Mid-conversation
It can also extract while a conversation is still open, so a long-running thread surfaces what it knows so far without waiting for the close.
- 3
Re-extract on demand
Change the schema and re-run it against past conversations. New question this quarter? Pull it from the conversations you already had.
Reading transcripts by hand does not scale. A summary is not a column.
Where extraction differs from manual tagging or a generic conversation summary.
| Tag it by hand | A generic summary | Boom extraction | |
|---|---|---|---|
| Who reads the conversations | Your team, one by one | A model, into prose | Boom, into your schema |
| What you get | Notes, if anyone writes them | A paragraph to read | Typed columns you can sort |
| Audio and images | Someone listens and looks | Often ignored | Transcribed and read in |
| Take it elsewhere | Re-key it | Copy and paste | CSV or Excel export |
Questions about extraction
See extraction on your own conversations
Book a 15-minute walkthrough and we will define a schema and pull it from a real set of conversations.