Pylon writes your customer issue data into a BigQuery table so you can query it next to your product, revenue, and usage data in the warehouse you already run.
Pylon automatically creates the destination table with the proper schema, so your issue data lands query-ready without manual modeling.
Data is written into a dataset and project you control, letting you house Pylon customer issue data wherever it fits your existing BigQuery structure.
In Pylon, head to the data warehouse app section and select the BigQuery app to connect it. We recommend creating a new dataset to house your Pylon customer issue data — you can also create a new project or use an existing one.
In Google Cloud, create a service account key for your account and upload the Key File JSON when configuring the connection.
Provide your Project ID and a Dataset ID (create the dataset where Pylon data will be housed). Optionally set the Location where the dataset lives.
Enter the Table ID for the table data will be written to. Pylon automatically creates this table for you with the proper schema.
Once all details are entered, hit test to verify your connection.
Navigate to the data warehouse app section inside Pylon and connect the BigQuery app. You'll need a Key File JSON, your Project ID, a Dataset ID where Pylon data will be housed, and a Table ID. An optional Location field lets you specify where the dataset is stored. Pylon will automatically create the table with the proper schema.
You can create a new Project or use an existing one. Pylon recommends creating a new dedicated dataset to house your Pylon customer issue data to keep it organized. The table itself does not need to be created in advance. Pylon generates it automatically once the connection is validated.
There are three primary use cases for synced product data: viewing usage data in the issue sidebar for context when responding to customer issues, creating worklists in Pylon with usage data as columns, and feeding account data into custom account management notebooks. The data can also be joined with other warehouse data for deeper reporting and analytics.
The warehouse sync is a one-way export from Pylon to BigQuery. Data will automatically be upserted into your warehouse. New records are inserted as new rows. And updated records replace existing rows. New data columns can be subscribed to as they become available.
In addition to BigQuery, Pylon supports Snowflake, Redshift, and AWS S3 for warehouse sync. All use the same no-code setup and upsert logic; the difference is simply in the credentials and connection details required for each provider.
Pylon connects with the tools your team lives in — Slack, email, CRMs, ticketing systems,and more. Meet your customers where they are and streamline every support workflow.
Receive and respond to customer messages from Telegram
Manage B2B support directly in Slack channels alongside your team.
Connect Pylon to Granola to ingest call recordings
Sync Outlook Calendar meetings to customer records in Pylon
Automatically capture and log Google Meet call notes to Pylon
Automcatically link active incidents to affected customer conversations.
Enrich customer records with usage data from Amazon Redshift.
Create Shortcut stories from customer conversations automatically
Trigger PagerDuty incidents from critical customer-reported issues.
