Real-time AI dashboards for data teams.

Stop being the bottleneck for every question. Let stakeholders self-serve answers with an AI data agent governed on your trusted data models.

Data teams don't lack requests. They lack a way to stop being the bottleneck.

  1. 01

    Governed self-serve, without the ticket queue.

    The easy 80% self-serves. Your analysts reclaim time for high-leverage work.

    • Self-serve on your semantic layer, not raw SQL guesses
    • Fewer tickets, fewer context switches
    Learn more about data agents
  2. Term definitions are consistent across the team. One number, every team
    02

    Trusted data models. One number, every team.

    Every answer uses the metric definitions your team certified. No shadow metrics. No debates.

    • Single source of truth across every stakeholder
    • Definitions locked in as executable code, not free text
    Explore the platform
  3. Connect once. The data team curates models once, the whole company benefits.
    03

    One agent across your whole data stack.

    Connect once. The data team curates models once, the whole company benefits.

    • Snowflake, BigQuery, dbt, Redshift, Postgres, Looker, Tableau
    • No new data silo or migration required
    Our data connectors

Comparison

AI analytics vs. traditional analytics.

Approach

AISelf-serve on governed models.

TraditionalAnalyst-built reports per request.

Speed

AIReal-time, instant answers.

TraditionalDelayed by the ticket queue.

Governance

AIGrounded in your trusted semantic layer.

TraditionalDefinitions drift across teams.

Scale

AIScales access without scaling headcount.

TraditionalBounded by analyst capacity.

Usability

AIConversational. Stakeholder-friendly.

TraditionalRequires SQL or BI skills.

What data teams use it for

Less ad-hoc work, more leverage.

  • Deflect ad-hoc requests

    • Self-serve stakeholder questions
    • Fewer one-off report tickets
    • Reusable governed answers
  • Govern metrics centrally

    • Single source of truth
    • Certified metric definitions
    • Consistent answers across teams
  • Scale data access

    • Onboard non-technical users
    • Plain-English querying
    • No SQL bottleneck
  • Focus on high-value analysis

    • Reclaim analyst time
    • Deeper modeling work
    • Proactive insight delivery

How it works

From data model to answer, in three steps.

  1. 01

    Connect your warehouse and semantic models.

  2. 02

    Stakeholders ask questions in natural language.

  3. 03

    Get governed, AI-generated dashboards in seconds.

Your AI data analyst connects to all your data

Excel/CSVExcel/CSV
Squadbase DBSquadbase DB
SnowflakeSnowflake
BigQueryBigQuery
DatabricksDatabricks
RedshiftRedshift
dbtdbt
Excel/CSVExcel/CSV
Squadbase DBSquadbase DB
SnowflakeSnowflake
BigQueryBigQuery
DatabricksDatabricks
RedshiftRedshift
dbtdbt
Excel/CSVExcel/CSV
Squadbase DBSquadbase DB
SnowflakeSnowflake
BigQueryBigQuery
DatabricksDatabricks
RedshiftRedshift
dbtdbt
Excel/CSVExcel/CSV
Squadbase DBSquadbase DB
SnowflakeSnowflake
BigQueryBigQuery
DatabricksDatabricks
RedshiftRedshift
dbtdbt
AWS AthenaAWS Athena
PostgreSQLPostgreSQL
MySQLMySQL
Google AnalyticsGoogle Analytics
AirtableAirtable
kintonekintone
Wix StoreWix Store
AWS AthenaAWS Athena
PostgreSQLPostgreSQL
MySQLMySQL
Google AnalyticsGoogle Analytics
AirtableAirtable
kintonekintone
Wix StoreWix Store
AWS AthenaAWS Athena
PostgreSQLPostgreSQL
MySQLMySQL
Google AnalyticsGoogle Analytics
AirtableAirtable
kintonekintone
Wix StoreWix Store
AWS AthenaAWS Athena
PostgreSQLPostgreSQL
MySQLMySQL
Google AnalyticsGoogle Analytics
AirtableAirtable
kintonekintone
Wix StoreWix Store
Our stakeholders self-serve the easy questions now, so the data team finally has time for the hard ones.

Data team questions, answered.

  • Will self-serve answers stay governed?

    Yes. The AI data agent reasons on top of your trusted semantic layer, so every self-serve answer uses the exact metric definitions your team has certified. Governance and speed stop being a trade-off.

  • Does this replace our data team?

    No. Squadbase deflects the repetitive, low-leverage requests so your analysts can focus on high-value modeling work. The data team curates the models once, and the agent handles the rest.

  • Which parts of our stack does it connect to?

    Squadbase connects to Snowflake, BigQuery, dbt, Redshift, Postgres, Looker, Tableau, and spreadsheets. It works across your existing stack without forcing a new data silo or migration.

  • Can non-technical stakeholders use it safely?

    Yes. Stakeholders ask questions in plain English and get answers grounded in your governed data models. They never touch the underlying SQL or schema directly.

  • How does Squadbase keep metric definitions consistent?

    Business definitions are stored as tested, executable code rather than free-text descriptions. Every answer runs the same certified logic, so you get the same correct number every time, regardless of who asks.

Stop being the bottleneck. Let everyone self-serve.

Scale data access without scaling headcount, with an AI data agent governed on your trusted models.