The challenge
Every new customer brought different source data, mappings and reporting needs, and each one was set up by hand. Onboarding took four weeks, models drifted apart from customer to customer, and the operational work grew with every new account. 10xCRM needed one shared platform that still kept each customer’s data strictly separate.
What we delivered
A BigQuery data model that keeps each tenant’s data isolated, from ingestion to dashboards
Reusable Python ingestion and transformation components
dbt models and tests for consistent business logic
Orchestration in Mage for visible, repeatable workflows
Configuration-driven onboarding instead of customer-specific pipeline forks
Monitoring and operational support patterns for production use
How it works
Outcome
5 min
New-customer onboarding, down from four weeks
<1 s
Analytical query performance at scale
60–70%
ETL performance improvement
The platform reduced new-customer onboarding from four weeks to five minutes, delivered sub-second analytical query performance at scale, and improved ETL performance by 60–70%. Every customer runs on shared infrastructure, with its own data kept strictly separate. Pipeline maintenance on this platform is now handled by our Intelligent DataOps agent. Read the DataOps case study →
“AgentixLake built our multi-tenant analytics platform on Google Cloud. New customers now go live in minutes instead of weeks, and their AI agent handles the pipeline maintenance that used to take 40 hours a month.”
Muhammed BagriacikChief Executive Officer, 10xCRM
TECHNOLOGY
Google Cloud
Google BigQuery
Python- Mage
dbt- SQL