Xenition is an AI workspace with 23 real editing surfaces — document, spreadsheet, slides, code, diagram, video studio, 3D and more — that all share one memory. A single chat can write a document, design a deck, build and deploy a full-stack app, render a video or reconcile a pile of invoices, and hand the result to a surface you can edit.

Yes. The free plan includes 25,000 credits a month and needs no credit card. Paid plans start at about $16.67 per month billed yearly, with usage-based credits shared across every surface.

What if the tool I need is not in Xenition?

Ask for it in the product chat. The team reviews every request and most ship within 3 to 7 days.

How is Xenition different from a chatbot with plugins?

A chatbot returns text. Xenition returns work on a surface you can keep editing, and remembers it. It also does things a single-surface tool structurally cannot: learning paths with decaying mastery that can refuse to teach a topic whose foundation has lapsed, invoice reconciliation where confidence comes from arithmetic rather than a model's self-assessment, explainer videos where narration is measured before any frame is cut, and meeting notes that expand your own typed notes and drop any action item that cannot quote its source line.

Can Xenition build and deploy a real application?

Yes. Describe an app and Xenition generates a working product with a real Postgres database, authentication, storage, an API, a web front end and a native mobile app, then deploys it. There are also 153 full-stack templates, each with a live demo.

Which apps does Xenition connect to?

121 apps including Slack, Gmail, Google Calendar, Google Drive, Notion, GitHub, Linear, Jira, HubSpot, Salesforce, Stripe, QuickBooks and Airtable. Agents can act in them, with an approval gate on anything destructive.

Is Xenition available outside the browser?

Yes — a desktop app for macOS, Windows and Linux with a global hotkey and silent auto-updates, plus native mobile apps for iOS and Android. The interface is available in 36 languages.

All connectors

Google BigQuery integration

Google BigQuery is a fully managed data warehouse for large-scale data analytics, offering fast SQL queries and machine learning capabilities on massive datasets

Connect Google BigQuery to Xenition and work with it from chat, from an agent, or on a schedule — alongside every other app you have connected, in one workspace.

What you can do with Google BigQuery

Ask in plain language and Xenition calls the right one:

Cancel BigQuery Job — Tool to cancel a running BigQuery job. This call returns immediately, and you need to poll for the job status to see if the cancel completed successfully. Note that cancelled jobs may still incur costs.
Create Capacity Commitment — Tool to create a new capacity commitment resource in BigQuery Reservation. Use when you need to purchase compute capacity (slots) with a committed period of usage for BigQuery jobs. Supports various commitment plans (FLEX, MONTHLY, ANNUAL, THREE_YEAR) and editions (STANDARD, ENTERPRISE, ENTERPRISE_PLUS).
Create BigQuery Connection — Tool to create a new BigQuery connection to external data sources using the BigQuery Connection API. Use when setting up connections to AWS, Azure, Cloud Spanner, Cloud SQL, Salesforce DataCloud, or Apache Spark.
Create Analytics Hub Listing — Tool to create a new listing in a BigQuery Analytics Hub data exchange. Use when you need to share a BigQuery dataset with specific subscribers or make it available for discovery. The dataset must exist and be in the same region as the data exchange.
Create BigQuery Dataset — Tool to create a new BigQuery dataset with explicit location, labels, and description using the BigQuery Datasets API. Use when the workflow needs to set up a staging/warehouse dataset and correctness of region is critical to avoid downstream job location mismatches. Surfaces 409 Already Exists errors cleanly without retrying.
Create Analytics Hub Data Exchange — Tool to create a new Analytics Hub data exchange for sharing BigQuery datasets. Use when you need to set up a container for data sharing with descriptive information and listings.
Create Analytics Hub Listing — Tool to create a new listing in a data exchange using Analytics Hub API. Use when publishing a BigQuery dataset to make it available for subscription by other users or organizations.
Create BigQuery Data Policy (v2beta1) — Tool to create a new data policy under a project with specified location using the v2beta1 BigQuery Data Policy API. Use when you need to set up data masking rules or column-level security for sensitive data. The v2beta1 endpoint uses a nested request structure.
Create Analytics Hub Query Template — Tool to create a new query template in a BigQuery Analytics Hub Data Clean Room (DCR) data exchange. Use when you need to define predefined and approved queries for data clean room use cases. Query templates must be created in DCR data exchanges only.
Create BigQuery Reservation — Tool to create a new BigQuery reservation resource to guarantee compute capacity (slots) for query and pipeline jobs. Use when you need to reserve dedicated compute resources for predictable performance and cost management. Reservations can be configured with autoscaling, concurrency limits, and edition-based features.
Create BigQuery Reservation Assignment — Tool to create a BigQuery reservation assignment that allows a project, folder, or organization to submit jobs using slots from a specified reservation. Use when setting up resource allocation for BigQuery workloads. Note: A resource can only have one assignment per (job_type, location) combination.
Create BigQuery Routine — Tool to create a new user-defined routine (function or procedure) in a BigQuery dataset. Use when you need to define SQL, JavaScript, Python, Java, or Scala functions/procedures for reusable logic, data transformations, or custom masking. Supports scalar functions, table-valued functions, procedures, and aggregate functions with comprehensive type definitions.
Create BigQuery Table — Tool to create a new, empty table in a BigQuery dataset. Use when setting up data infrastructure for standard tables, external tables, views, or materialized views. Supports partitioning, clustering, and encryption configuration.
Delete BigQuery Dataset — Tool to delete a BigQuery dataset specified by datasetId via the datasets.delete API. Before deletion, you must delete all tables unless deleteContents=True is specified. Use when cleaning up test datasets or removing unused data warehouses. Immediately after deletion, you can create another dataset with the same name.
Delete BigQuery Job Metadata — Tool to delete the metadata of a BigQuery job. Use when you need to remove job metadata from the system. If this is a parent job with child jobs, metadata from all child jobs will be deleted as well.
Delete BigQuery ML Model — Tool to delete a BigQuery ML model from a dataset. Use when you need to remove a trained machine learning model permanently. The operation deletes the model and cannot be undone.
Delete BigQuery Routine — Tool to delete a BigQuery routine by its ID. Use when you need to remove a stored procedure, user-defined function, or table function from a dataset. This operation is irreversible.
Delete BigQuery Table — Tool to delete a BigQuery table from a dataset. Use when you need to remove a table and all its data permanently. The operation deletes all data in the table and cannot be undone.
Get BigQuery ML Model — Tool to retrieve a specific BigQuery ML model resource by model ID. Use when you need detailed information about a trained machine learning model including its configuration, training runs, hyperparameters, and evaluation metrics.
Get BigQuery Connection IAM Policy — Tool to get the IAM access control policy for a BigQuery connection resource. Returns an empty policy if the resource exists but has no policy set. Use this to check who has access to a specific connection before modifying permissions.
Get BigQuery Dataset Metadata — Tool to retrieve BigQuery dataset metadata including location via the datasets.get API. Use this before creating jobs/queries if the workflow has been failing with location mismatch to confirm the dataset's region and correct the job location accordingly.
Get BigQuery Job — Tool to retrieve information about a specific BigQuery job. Returns job configuration, status, and statistics. Use this to check job status after running queries or to get details about job execution.
Get BigQuery Query Results — Tool to get the results of a BigQuery query job via RPC. Use this to retrieve results after running a query, or to check job completion status and fetch paginated results.
Get BigQuery Routine — Tool to retrieve a BigQuery routine (user-defined function or stored procedure) by its ID. Use to inspect routine definitions, arguments, return types, and metadata.
Get BigQuery Routine IAM Policy — Tool to retrieve the IAM access control policy for a BigQuery routine resource. Returns an empty policy if the routine exists but has no policy set. Use this to check current access permissions before modifying them.
Get BigQuery Service Account — Tool to get the service account for a project used for interactions with Google Cloud KMS. Use when you need to retrieve the BigQuery service account email for KMS encryption configuration or key access permissions.
Get BigQuery Table IAM Policy — Tool to retrieve the IAM access control policy for a BigQuery table resource. Returns an empty policy if the resource exists but has no policy set. Use this to check current access permissions before modifying them.
Get BigQuery Table Schema — Tool to fetch a BigQuery table's schema and metadata without querying row data. Use before generating SQL queries to avoid column name typos and confirm field types and nullable modes. This is especially useful when INFORMATION_SCHEMA access is restricted.
Insert Data into BigQuery Table — Tool to stream data into BigQuery one record at a time without running a load job. Use when you need immediate data availability or inserting small batches. Supports row-level deduplication via insertId and error handling via skipInvalidRows.
Insert BigQuery Job — Tool to start a new asynchronous BigQuery job (query, load, extract, or copy). Use when you need to run a query as a job, load data from Cloud Storage, extract table data to GCS, or copy tables. For dry-run validation without execution, set dryRun to true in configuration.

…and 20 more Google BigQuery actions available in the workspace.

Give an agent access to Google BigQuery

An agent you create can use Google BigQuery on your behalf and keep working while you do something else. Every action an agent takes goes through an approval gate first, so nothing touches your Google BigQuery account without you agreeing to it.

Other connectors

Frequently asked questions

How do I connect Google BigQuery to Xenition?

Open the connectors directory in Xenition, find Google BigQuery, and sign in when the authorisation window opens. The connection is managed for you — there is no API key to create, paste or rotate.

What can Xenition do with Google BigQuery?

Xenition can run 50 different Google BigQuery actions, including Cancel BigQuery Job, Create Capacity Commitment, Create BigQuery Connection. You ask in plain language and the right one is called for you.

Can an agent use Google BigQuery on my behalf?

Yes. Agents can use Google BigQuery while you do something else, and every action passes through an approval gate before it runs.

Is there a free plan?

Yes. Xenition starts free with 25,000 credits a month and no credit card, and all connectors are available on every plan.