Databricks AI employee
A Superkind AI employee that queries data, creates analyses, checks pipelines, and corrects records for you inside Databricks. Message it in Teams or Outlook, it works in Databricks 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 Databricks AI employee?
A Databricks AI employee is an AI that connects to your Databricks account and completes work in it. It does not just answer questions, it takes the actions: querying data with SQL, evaluating MLflow runs, checking Lakeflow pipelines, and searching Vector Search indexes. 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 Databricks actions, chains them with your other systems, and asks for your approval before sensitive actions run.
Databricks is a data and AI platform for SQL analytics, data engineering, machine learning, and enterprise data in the lakehouse.
You ask in Teams
Describe the data or analysis you need in plain English.
Superkind picks the actions
Selects the right Databricks actions and chains them together.
Superkind works in Databricks
Runs SQL queries, checks pipelines, and evaluates MLflow runs. Your real data.
Superkind reports back
Delivers the finished result to Teams or Outlook.
What can you ask Superkind to do in Databricks?
Messages you would actually send. Copy one, swap in your specifics, and Superkind takes it from there.
“Query the last seven days of revenue in Databricks and deliver the results as a table.”
you, to @Superkind“Create an analysis of this month’s MLflow runs with runtime, error rate, and the best model.”
you, to @Superkind“Correct the three wrong values in fact_sales, but wait for my approval before writing them.”
you, to @Superkind“Let me know here as soon as Databricks detects an outlier above 15 percent, with source and value.”
you, to @SuperkindHow does Superkind work with Databricks?
- Native integrations and connectors for 1,000+ tools
1Connect your systems
Databricks connects through a suitable OAuth login or service principal. Teams and the systems your team already uses connect alongside it. Superkind only operates with the Databricks permissions you approve.
@Superkind check yesterday’s revenue data in Databricks for outliers and prepare the required corrections.
On it. I am checking the Databricks table and preparing the corrections for approval.
2Tell Superkind what you need
Message your AI employee in Teams like a colleague. Ask for a Databricks query, an MLflow analysis, or the status of a Lakeflow pipeline. Plain English is enough, Superkind picks the right actions.
- SQL Warehouse: query complete
- 3 outliers in fact_sales
- Review table revenue_review created
Table revenue_reviewWaiting for your approvalCorrect three values in fact_sales?3Superkind operates, you approve
Superkind completes the work in Databricks and writes the result back in Teams. Changes to production data, compute, or access rights wait for your approval. Every step is logged.
What can Superkind do in Databricks?
Ask in plain English from Teams or Outlook. Superkind picks the right Databricks actions, runs the work, and reports back. No workflows to build.
Run SQL query
Runs a SQL statement on a Databricks SQL warehouse and returns the result set.
Create SQL query
Saves a Databricks SQL query with warehouse, catalog, schema, and query text.
Search query history
Finds statements in Query History by user, warehouse, status, or time range.
Create alert
Creates a Databricks SQL alert with a query, threshold, and notification destination.
Check warehouse status
Shows the state, size, and running queries of a SQL warehouse.
Search experiment
Finds an MLflow experiment by name, tag, or lifecycle stage.
Search runs
Searches MLflow runs by metrics, parameters, tags, or start time.
Get run metrics
Reads metrics, parameters, and artifacts from a specific MLflow run.
List model versions
Lists versions of a registered model with alias, stage, and run ID.
List pipelines
Lists Lakeflow Declarative Pipelines in the workspace with state and latest update.
Check pipeline updates
Shows updates for a Lakeflow pipeline with status, cause, and timestamp.
Start pipeline update
Starts a new update for a Lakeflow pipeline and its target tables.
Read pipeline events
Reads the event log of a Lakeflow pipeline for progress and error analysis.
List endpoints
Lists Vector Search endpoints with endpoint type, state, and capacity.
Create endpoint
Creates a Vector Search endpoint for new search indexes.
Query index
Searches a Vector Search index for similar records and selected columns.
Delete index
Deletes a Vector Search index and its search configuration.
List jobs
Lists Databricks jobs with tasks, schedule, and latest run.
Run job
Starts a Databricks job with the approved parameters.
Check cluster status
Shows the state, runtime, and active workers of a Databricks cluster.
Invite user
Adds a user or service principal to the Databricks workspace.
List tables
Lists Unity Catalog tables in a catalog and schema.
Get table details
Reads columns, data types, owner, and properties of a Unity Catalog table.
Query records
Reads selected rows from a Delta table with a SQL filter.
Correct records
Changes approved values in a Delta table through a SQL statement.
Check permissions
Shows grants for catalogs, schemas, tables, volumes, and other Unity Catalog objects.
Superkind AI employees that work with Databricks
Every role brings its expertise and uses Databricks as one of its tools.
Companies working with Superkind
Frequently asked questions
Everything you need to know about your AI employee for Databricks.
Yes. Superkind connects your Databricks workspace through a managed connector. Your team can then give the AI employee Databricks tasks from Teams or Outlook. It runs queries, checks pipelines, and returns finished results. Access remains limited to the Databricks resources and actions agreed with your team.
A Databricks admin configures OAuth access or a service principal and grants only the required workspace permissions. We then connect Databricks to your AI employee and test the agreed tasks together. Your team works from Teams or Outlook afterwards, without sharing credentials in messages.
It can run SQL queries, inspect Query History and SQL alerts, evaluate MLflow experiments and runs, monitor Lakeflow pipelines, and query Vector Search indexes. Jobs, clusters, and Unity Catalog objects can also be included. The Databricks actions actually available depend on your use case and the permissions you grant.
No. You describe the result you need in Teams or Outlook. Superkind picks the right Databricks actions, connects them with Jira, GitHub, or Slack when needed, and reports back with the finished result. Unlike Zapier or Make, you do not build and maintain triggers, steps, or error paths yourself.
Only with your approval. Read-only SQL queries, status checks, and analyses can run independently within the agreed scope. Changes to Delta tables, deleting a Vector Search index, creating compute resources, or changing access wait for an explicit yes in Teams. Your team defines which Databricks actions are sensitive together with us.
Superkind is hosted in the EU and we sign a GDPR data processing agreement. The AI employee receives only the minimum Databricks permissions required for the agreed use case. Your data is not used for training. Queries, results, approvals, and completed actions are logged so your team can trace and review access.
Databricks mainly publishes usage-based pricing that depends on the product, cloud, compute, and workload rather than a fixed per-user fee. Zapier starts at about 20 euros per month and Make at about 10 euros, both plus setup time. Superkind is priced per use case, a fraction of a full-time hire.