BigQuery AI employee
A Superkind AI employee that connects to BigQuery, queries data, creates analyses, and maintains tables for you. Message it in Teams or Outlook, it works directly in BigQuery and reports back with the result. Sensitive actions wait for your approval.
- Hosted in the EU
- GDPR data processing agreement
- Ready to start today
- Works in Teams, Slack and more
What is a BigQuery AI employee?
A BigQuery AI employee is an AI that connects to your BigQuery account and completes work in it. It does not just answer questions about data, it takes the actions: checking datasets, running SQL queries, analysing tables, and providing results. Superkind builds this AI employee with your company knowledge and delivers the finished result to wherever your team works.
Unlike workflow builders such as Zapier or Make, there is nothing to configure. No triggers, no zaps, nothing to maintain. You describe the outcome in plain English, the AI employee picks the right BigQuery actions, chains them with your other systems, and asks for your approval before sensitive changes run.
BigQuery is Google Cloud’s serverless data warehouse for scalable SQL queries, analytics, and centralised company data.
You ask in Teams
Describe the data or analysis you need in plain English.
Superkind picks the actions
Selects the right BigQuery actions and chains them together.
Superkind works in BigQuery
Checks datasets, runs queries, and creates tables. Your real data.
Superkind reports back
Delivers the finished analysis to Teams or Outlook.
What can you ask Superkind to do in BigQuery?
Messages you would actually send. Copy one, swap in your specifics, and Superkind takes it from there.
“Query yesterday’s orders in BigQuery and send me revenue and unit count as a table.”
you, to @Superkind“Create an analysis of campaigns by channel, cost, and conversion rate from the marketing_events dataset.”
you, to @Superkind“After my approval, correct the missing country values in the customers table using our CRM data.”
you, to @Superkind“Let me know here as soon as daily revenue in BigQuery deviates over 20 percent from the monthly average.”
you, to @SuperkindHow does Superkind work with BigQuery?
- Native integrations and connectors for 1,000+ tools
1Connect your systems
BigQuery connects securely to your project. Add Teams and the systems holding your source data. Superkind handles the BigQuery connection. The AI employee is then available directly in Teams.
@Superkind please analyse yesterday’s orders in BigQuery and save the outliers in a new table.
On it. I am checking the orders table in BigQuery and preparing the outliers.
2Tell Superkind what you need
Message your AI employee in Teams like a colleague. Name the analysis and the relevant BigQuery dataset. Superkind translates your request into the required BigQuery actions. It clarifies missing details directly in Teams.
- 3 BigQuery outliers found
- Orders query completed successfully
- Table order_outliers prepared
Table order_outliersWaiting for your approvalCreate the BigQuery table now?3Superkind operates, you approve
Superkind runs the query in BigQuery and delivers the result in Teams. Changes to BigQuery tables wait for your approval. You see what will change first. Every step remains traceable in Teams.
What can Superkind do in BigQuery?
Ask in plain English from Teams or Outlook. Superkind picks the right BigQuery actions, runs the work, and reports back. No workflows to build.
List projects
Lists available Google Cloud projects with project ID and display name.
Check project
Shows metadata and BigQuery settings for a Google Cloud project.
Check service account
Checks which service account and BigQuery roles a connection uses.
Get reservations
Shows BigQuery reservations, assignments, and available slots for a project.
List datasets
Lists BigQuery datasets in a project with location and labels.
Get dataset
Reads metadata, location, expiration, and labels for a BigQuery dataset.
Create dataset
Creates a BigQuery dataset with location, expiration, and labels.
Delete dataset
Deletes a BigQuery dataset and optionally its contained tables.
List tables
Lists tables and views in a BigQuery dataset with type and creation time.
Get table schema
Reads columns, data types, modes, and descriptions from a BigQuery table schema.
Get table details
Shows metadata, partitioning, clustering, and row count for a BigQuery table.
Create table
Creates a BigQuery table with a defined schema, partitioning, and expiration.
Insert rows
Adds new JSON rows to a BigQuery table through streaming insert.
Delete table
Permanently deletes a BigQuery table or view from a dataset.
Run SQL query
Runs a standard SQL query in BigQuery and returns the result rows.
Validate query
Checks a BigQuery query with dry run for syntax and estimated bytes.
Explain query
Explains BigQuery SQL, filters, joins, and aggregations in plain language.
Create analysis
Calculates metrics from BigQuery tables and formats them as a clear table.
Correct rows
Updates selected BigQuery rows with a reviewed DML query.
List jobs
Lists BigQuery jobs with type, status, user, and creation time.
Get job status
Checks status, errors, and runtime for a BigQuery job.
Analyse job errors
Examines error messages from a BigQuery job and identifies the likely cause.
List routines
Lists stored BigQuery routines such as functions and procedures in a dataset.
List models
Lists BigQuery ML models in a dataset with model type and creation time.
Check access
Shows IAM roles and dataset permissions for a BigQuery project.
Analyse audit logs
Analyses BigQuery audit logs by users, queries, and unusual access.
Superkind AI employees that work with BigQuery
Every role brings its expertise and uses BigQuery as one of its tools.
Companies working with Superkind
Frequently asked questions
Everything you need to know about your AI employee for BigQuery.
Yes. BigQuery is one of the integrations Superkind connects through a managed connector. Once connected, your team can put the AI employee to work with data from Microsoft Teams or Outlook. No workflow builder, no code. The AI employee only receives access to the projects, datasets, and actions you approve for its tasks.
An admin connects the relevant Google Cloud project and confirms the required BigQuery permissions. You define which datasets and tables the AI employee may access. We configure the connection with you, verify it with a safe test query, and then make Superkind available to the team in Teams or Outlook.
It can list projects, datasets, and tables, read table schemas, run standard SQL queries, create analyses, and check jobs. After approval, it can also create datasets or tables, insert rows, and run reviewed data corrections. We define the exact BigQuery actions your team needs together for each use case.
No. With Zapier or Make, you build triggers and individual steps and maintain them when a table or requirement changes. With the AI employee, you describe the desired outcome in plain English. Superkind selects the right BigQuery actions, connects them with Teams, Outlook, or other systems when needed, and asks when details are missing.
Only with your approval. Superkind can independently run read queries, schema analyses, and reports. Changes to production data, new tables, DML queries, and deletions wait for an explicit yes from a responsible person in Teams. Your team decides which BigQuery actions are sensitive and who is allowed to approve them.
Superkind is hosted in the EU and we sign a GDPR data processing agreement. The AI employee uses minimal BigQuery permissions limited to the required projects and datasets. Your data is not used for training. Queries, approvals, and completed actions are logged so your team can trace access and changes.
BigQuery charges by data processed or through capacity models, not with one simple per-user price. Zapier starts at about 20 euros per month and Make at about 10 euros per month, both plus setup time and ongoing maintenance. Superkind is priced per use case, a fraction of a full-time hire. BigQuery usage is billed separately.