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.
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Team plan can view logs from the past 90 days.Category: AccessAudit log Date Actor Category Action Result Sep 8, 14:02 you@company.com Project Created project Success Sep 8, 11:47 K. Ito Access Updated permissions Success Sep 7, 16:30 Squadbase AI Dashboard Deployed dashboard Success Sep 7, 09:15 M. Yamada Member Invited member Success Sep 5, 18:22 System Connection Synced connection Failure Sep 4, 10:03 R. Chen Export Exported audit log Success Showing 1–6 of 25
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
- Dashboards the business builds itself, on the same governed definitions
Business context
Learns from your team
- ARR
- Paid MRR × 12
- Churned
- Inactive for 30+ days
subscriptionscustomersOne certified definition
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
- 7 tools connectedConnected
Connecting your whole data stack.
Explore data- Read warehouse schemaSnowflake · BigQuery
- Sync transformation layerdbt
- Map existing BI toolsLooker · Tableau
Whole stack connected. One model, every team.
03
One agent across your whole data stack.
Connect once. The data team curates models once, the whole company benefits.
- Snowflake, BigQuery, and Redshift connectors, plus dbt, Postgres, Looker, Tableau
- No new data silo or migration required
Comparison
AI analytics vs. traditional analytics.
AI analytics | Traditional analytics | |
|---|---|---|
| Approach | Self-serve on governed models. | Analyst-built reports per request. |
| Speed | Real-time, instant answers. | Delayed by the ticket queue. |
| Governance | Grounded in your trusted semantic layer. | Definitions drift across teams. |
| Scale | Scales access without scaling headcount. | Bounded by analyst capacity. |
| Usability | Conversational. Stakeholder-friendly. | Requires 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.
01
Connect your warehouse and semantic models.
02
Stakeholders ask questions in natural language.
03
Get governed, AI-generated dashboards in seconds.
Your AI data analyst connects to all your data
- AWS Athena
- AWS Billing
- Airtable
- Amplitude
- Asana
- Attio
- Azure Cosmos DB
- Azure SQL
- Backlog
- BigQuery
- ClickHouse
- ClickUp
- Customer.io
- Databricks
- Freshdesk
- Freshsales
- Freshservice
- Gamma
- Gemini
- GitHub
- Gmail
- Google Analytics
- Google Audit Log
- Google Calendar
- Google Search Console
- Grafana
- Hacker News
- HubSpot
- InfluxDB
- Intercom
- JDBC
- Jira
- Linear
- Mailchimp
- Mixpanel
- MongoDB
- MySQL
- Notion
- OpenAI
- Oracle Database
- Outlook
- PostgreSQL
- Power BI
- Redshift
- SQL Server
- Salesforce
- Semrush
- Sentry
- Snowflake
- Stripe
- Supabase
- Tableau
- TiDB
- Wix Store
- Zendesk
- dbt
- kintone
- monday.com
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.
Related use cases
Stop being the bottleneck. Let everyone self-serve.
Scale data access without scaling headcount, with an AI data agent governed on your trusted models.