LiveChat + Bruin

Source

Ingest LiveChat data into your warehouse with incremental loading, quality checks, and full lineage. Defined in YAML, version-controlled in Git.

For business teams

What you get

Support data meets revenue data
Join LiveChat tickets with billing and product usage. Build customer health scores that predict churn before it happens.
SLA monitoring, automated
LiveChat response times and resolution metrics are quality-checked on every sync. Know when SLAs are at risk before customers escalate.
Support ROI in business terms
Connect LiveChat agent performance to revenue outcomes. Show leadership the business impact of support quality.
No more ticket export Fridays
LiveChat data syncs automatically. Reports are fresh every morning without manual pulls.

For data & engineering teams

How it works

Incremental ticket sync
Only sync new and updated LiveChat tickets. No full reloads, even for high-volume support queues.
YAML-defined, Git-versioned
Your LiveChat pipeline is a YAML file. Review in PRs, deploy with CI/CD, roll back with git revert.
SLA validation in SQL
Custom quality checks validate response times and resolution SLAs. Pipeline alerts when thresholds are breached.
Cross-source customer view
Join LiveChat tickets with CRM and billing data in SQL transforms. Bruin resolves dependencies automatically.

Before you start

LiveChat account with API access

API key from LiveChat developer console

Step 1

Add your LiveChat connection

Connect using API key authentication. Add this to your Bruin environment file — credentials are stored securely and referenced by name in your pipeline YAML.

Parameters

api_keyLiveChat API key for authentication

connections:
  livechat:
    type: livechat
    uri: "livechat://?api_key=<api-key>"

Step 2

Create your pipeline

Define a YAML asset that tells Bruin what to pull from LiveChat and where to land it. This file lives in your Git repo — reviewable, version-controlled, and deployable with CI/CD.

Available tables

chatsagentscustomersgreetingstickets

name: raw.livechat_chats
type: ingestr

parameters:
  source_connection: livechat
  source_table: 'chats'
  destination: bigquery

Step 3

Add quality checks

Add column-level and custom SQL checks to your LiveChat data. If a check fails, the pipeline stops — bad data never reaches downstream models or dashboards.

Validate ticket statuses against accepted values

Catch open tickets with no assignee

Ensure ticket IDs are unique — no duplicates

columns:
  - name: ticket_id
    checks:
      - name: not_null
      - name: unique
  - name: status
    checks:
      - name: accepted_values
        value: ['open', 'pending', 'resolved', 'closed']

custom_checks:
  - name: no tickets missing assignee
    query: |
      SELECT COUNT(*) = 0
      FROM raw.livechat_chats
      WHERE status = 'open' AND assignee_id IS NULL

Step 4

Run it

One command. Bruin connects to LiveChat, pulls data incrementally, runs your quality checks, and lands clean data in your warehouse. If a check fails, the pipeline stops — bad data never reaches downstream.

Backfill historical data with --start-date

Schedule with cron or trigger from CI/CD

Full lineage from LiveChat to your dashboards

$ bruin run .

Running pipeline...

  livechat_chats
    ✓ Fetched 2,847 new records
    ✓ Quality: campaign_id not_null     PASSED
    ✓ Quality: spend not_null           PASSED
    ✓ Quality: no negative ad spend     PASSED
    ✓ Loaded into bigquery

  Completed in 12s

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Ready to connect LiveChat?

Start for free, or book a demo to see how Bruin handles ingestion, quality, lineage, and scheduling for your entire data stack.

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