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.
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.
Engagements
142 conversations
longwideone-hot
| 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 |
What it does
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.
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.
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.
Export to CSV or Excel
Take the table in long, wide, or one-hot format to your warehouse, BI tool, or spreadsheet.
Engagements
142 conversations
longwideone-hot
| 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 |
How it connects
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
When it runs
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.
FAQ
Questions about extraction
Can I upload documents to extract from?
Not today. Extraction works on the conversations Boom runs, including the audio and images a customer sends inside a conversation. It is not a standalone document-upload tool; the source is always a conversation.
Does the extracted data sync back to the CDP?
Not automatically today. Extracted fields land as typed columns on the Engagements dashboard and export to CSV or Excel. From there you take them into your own warehouse or logic. They are not auto-synced to the data platform or pushed to a webhook yet.
When does it extract?
On conversation close by default, optionally mid-conversation while a thread is still open, and on demand if you change the schema and want to re-extract from past conversations.
What kinds of fields can I define?
Typed fields you set up: a category, a yes or no, a number, a free-text reason. You design the schema for the question you are answering, like why customers churned or whether an issue was resolved, and Boom fills it from the conversation.
See what extraction connects to
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.