BigQuery
Your warehouse.
Your dashboards.
Zero infrastructure.
Connect camelAI to BigQuery and ship dashboards, reports, and data apps in one conversation. No Looker. No Metabase. No pipeline.
From warehouse to dashboard in one conversation.
BigQuery
Data Warehouse
Petabytes of structured data in GCP. Tables, views, materialized views — whatever you have.
camelAI
AI Agent
Writes BigQuery SQL, builds interactive charts, creates full applications — all from your description.
Live Dashboards
Published Apps
Published to a live URL at *.camelai.app. Share with your team, embed anywhere, set up scheduled refreshes.
Six months and six figures — or one conversation.
Traditional BI Stack
Looker license
$50–150k/year
ETL pipeline setup
4–8 weeks of engineering
Dashboard development
2–4 months in LookML
Ongoing maintenance
Dedicated analytics engineer
Total time to first dashboard
3–6 months
$100k–$300k+/year
camelAI
One BigQuery connection
Service account or OAuth — 2 minutes
One conversation
Describe what you need in plain English
Published in minutes
Live dashboard at a shareable URL
Iterate in real-time
Change anything by asking — no LookML
Total time to first dashboard
Minutes
Pay for what you use
Enterprise-ready from day one.
Authenticate with a service account or Google OAuth. Credentials never leave your organization. camelAI connects to BigQuery through GCP's own auth layer.
Set per-query byte limits, daily budget caps, and cost alerts. camelAI respects your BigQuery cost constraints — no surprise bills.
SAML and OIDC single sign-on for your entire data team. Onboard analysts without managing individual accounts.
Every query logged with user identity, timestamp, bytes scanned, and cost. Full visibility into who queried what and when.
VPC Service Controls and Private Google Access supported. Keep BigQuery traffic off the public internet.
Viewer, editor, and admin roles. Control who can query which datasets, publish dashboards, or manage connections.
SELECT
DATE_TRUNC(event_timestamp, MONTH) AS month,
APPROX_COUNT_DISTINCT(user_id) AS unique_users,
ARRAY_AGG(DISTINCT campaign IGNORE NULLS) AS campaigns,
COUNTIF(event_name = 'purchase') AS conversions,
ROUND(SAFE_DIVIDE(
COUNTIF(event_name = 'purchase'),
APPROX_COUNT_DISTINCT(user_id)
) * 100, 2) AS conversion_rate_pct
FROM `project.analytics.events_*`
WHERE _TABLE_SUFFIX BETWEEN '20250101' AND '20250331'
GROUP BY 1
ORDER BY 1;camelAI writes BigQuery SQL natively — including UDFs, window functions, table wildcards, and federated queries.
What will you build from your warehouse?
“Connect to our BigQuery warehouse and build a revenue dashboard pulling from the billing dataset. Show MRR, churn rate, and expansion revenue by quarter. Publish it for the finance team.”
Try this prompt“Query the analytics.events table — it has 4.2TB of web analytics data. Show me a user journey analysis with conversion funnels by campaign source.”
Try this prompt“Build a cost monitoring dashboard for our BigQuery usage. Show bytes scanned by project, query costs by team, and flag any queries over $50. Set up daily cron updates.”
Try this prompt“Create a marketing attribution report from our BigQuery data. Multi-touch attribution across paid, organic, and referral channels with interactive drill-downs.”
Try this prompt