Launch AI outbound calls from updated database records

When a row is updated in Google BigQuery, automatically start an outbound call in Vapi to follow up with leads or verify client details.

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How Knit builds this workflow

The Agent researches both APIs and wires the trigger on one side to the action on the other — no pre-built connector required. Here is the vocabulary it has to work with.

Vapi Explore Vapi →

Triggers

Agent run completed

Fires when an Vapi agent finishes a task or run.

Call or conversation completed

Fires when an Vapi agent finishes a call or conversation.

Lead qualified by agent

Fires when an Vapi agent marks a lead qualified.

Actions

Run an agent or tool

Triggers an Vapi agent or tool with a set of inputs.

Start an outbound call

Has an Vapi agent place an outbound call.

Add a lead to a campaign

Adds a lead to an outreach campaign in Vapi.

Google BigQuery Explore Google BigQuery →

Triggers

New row inserted

Fires when a new row is added to a table in Google BigQuery.

Row updated

Fires when an existing row changes in Google BigQuery.

Query result ready

Fires when a scheduled query in Google BigQuery finishes running.

Actions

Run a query

Runs a SQL query against Google BigQuery and returns the result.

Insert or update a row

Writes a new or updated row to Google BigQuery.

Sync a table

Syncs a table in Google BigQuery with data from another source.

Log completed AI conversations into your central warehouse When a call or conversation is completed in Vapi, automatically insert a new row into Google BigQuery containing transcript summaries and key structured data. See details

Record qualified leads straight to your database When a lead is qualified by an agent in Vapi, automatically create or update customer records in Google BigQuery for downstream reporting. See details

See all Vapi + Google BigQuery integrations → · Browse the full workflow library →

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FAQ

Common questions

Can Knit build “Launch AI outbound calls from updated database records” between Vapi and Google BigQuery?

Yes — describe it in the box above and Knit's Integrations Agent researches Vapi and Google BigQuery's public API docs (or your own uploaded docs) and builds a working workflow, whether or not either app already has a pre-built connector.

How long does it take to build?

Minutes to a first working version, not weeks — you test it against real data before it goes anywhere near production.

Why sync data between Vapi and Google BigQuery?

Connecting these tools ensures your AI workers execute tasks using fresh data stored in Google BigQuery, while saving conversation outcomes and lead qualification details back into your central database.

Can database changes initiate AI agent tasks automatically?

Yes, inserting or updating rows in Google BigQuery can prompt Vapi to start outbound calls or run targeted workflow automation tools.