The infrastructure your customer conversations run on
Most AI customer agents deflect tickets. Boom drives revenue, retention, and intelligence. You keep your domain logic and your customers. Boom runs the conversation and hands the variables back.
Four components, one connected loop
Each part is useful on its own and stronger together. The inbox runs the conversation, the data platform gives it context, the workflow builder decides what happens next, and extraction closes the loop by turning talk into structured signal.
- 01
Shared inbox
Where AI and your team work the same conversations. The agent handles most of them end to end; a person resolves, snoozes, or picks up an escalation. Nine views, labels, multiple numbers, and a context panel that opens an account right inside the chat.
- 02
Customer data platform
People, your own custom objects, and an append-only event log, fed by Shopify, Skio, a read-only connection to your database, or the events API. The agent reads it live, and the variables you segment on are right there. No SQL.
- 03
Workflow builder
Two layers. Initiatives are an outcome brief the agent navigates on its own, no flowcharts. Journeys are a visual builder for the path around the conversation: triggers, sends, delays, and branches.
- 04
Extraction
Structured fields pulled from the conversations themselves into a schema you define. They land as typed columns on the Engagements dashboard and export to CSV or Excel, so a pile of chats becomes a table you can read.
The inbox and the data sit side by side
When a conversation comes in, the context panel resolves it to the right person in the data platform and pulls in their related records and deep links, right next to the thread. Whoever is replying, the agent or a teammate, is reading the same account context.
The agent does not just see the message. The data platform schema is injected into it, so it answers with the order, the plan, or the balance already in view, instead of asking the customer for things you already know.
- Context panel opens an account from inside the chat
- The agent reads people, objects, and events live
- A human owns a conversation to reply; escalation pings the team
Segments decide who gets reached, and when
Build a segment on the variables you care about, up to three relationships deep, no SQL. When a customer enters it, the workflow builder takes over: send, delay, branch, hand the objective to the agent. You describe the outcome; Boom runs the talk.
Every closed conversation turns into a column
Extraction reads the conversation, including audio and images sent in, and fills the typed schema you defined. The result: a row per customer on the Engagements dashboard, exported to CSV or Excel. Ten thousand transcripts become a table you can sort.
| 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 |
The agent is graded before and while it runs
Before any change to the agent ships, it has to clear an offline evaluation gate: a suite of test cases it must pass and a regression baseline it cannot fall below. Nothing reaches your customers because someone tweaked a prompt and hoped for the best.
Once it is live, every conversation is scored at run time for leaked PII, card numbers, prompt or system leaks, and planted canary tokens, and for drift away from the criteria you set. Your team marks conversations thumbs-up or thumbs-down, and that feedback feeds back into the next version. So the agent gets safer the longer it runs, not riskier.
- Offline eval suite and regression baseline gate every change
- Runtime scoring for PII, card numbers, prompt and canary-token leaks, and drift
- Your team thumbs-up or thumbs-down trains the next version
- RBAC, encrypted credentials, read-only replicas; SOC 2, GDPR, ISO 27001 and ISO 42001 in progress
A human team converses but does not scale. Configurable tools scale but do not converse.
Boom is the layer that does both, and hands you the data on the way out. An honest look at the three options.
| A human team | Configure it yourself | Boom | |
|---|---|---|---|
| Real two-way conversations | Yes, capped by headcount | You build and maintain the bot | Yes, the agent navigates it |
| Who designs the dialog | Each rep, their own way | You script every branch | The agent, from a brief and your feedback |
| Reads your customer data | If they look it up | You wire every integration | Connected, injected into the agent |
| Turns talk into data | Notes, if anyone writes them | You build the pipeline | Extracted to columns and export |
| Cost | Around $100K to $300K per role | Engineering time, ongoing | Billed per fully automated conversation |
What the system does once it is running
- 450 to 70
A neobank reached 450 customers and held 70 effective conversations in about a week, work that would take one person many days.
Research deployment, neobank
- 15 in 2 days
A lending team ran 15 effective customer interviews in two days, against 2 done by hand in a full week.
Research deployment, lending
- 25 to 30%
Response rate on inactive customers that one-way notifications never reach.
Research deployment, healthtech
For us, collections is a conversation, not a blast, and the data never leaves our database.
Questions teams ask first
See the system on your own customers
Book a 15-minute walkthrough and we will run a case that matters to you across the inbox, the data, and the extraction.