Pillar 01
Clean, connected data that makes automation and AI actually work
AI agents and dashboards are only as reliable as the data feeding them. When records are scattered across a CRM, an ERP, spreadsheets and legacy databases, I engineer a single, trustworthy source of truth on Google Cloud, with pipelines that run on schedule and tell you when something breaks.
Sounds familiar?
- Reports disagree because every team pulls numbers from a different system
- Someone spends hours each week copying data between tools
- An AI pilot gave wrong answers because the underlying data was messy
- Pipelines fail silently and nobody notices until a report looks wrong
What I deliver
Automated ingestion and ETL/ELT
Scheduled pipelines from third-party APIs, webhooks and operational databases into central storage, orchestrated with Airflow.
BigQuery data warehouse
A performant, cost-controlled warehouse designed around how your business actually reports and decides.
Data modeling with dbt
Cleaned, standardized and tested models that analysts, dashboards and AI agents can all consume.
Monitoring and alerting
Infrastructure as code, automated deployments and alerts, so failures are caught before your users see them.
Typical projects
- One revenue and customer view across CRM, webshop and accounting
- Automated data entry from emails and forms into a database
- Preparing company data so an AI agent or knowledge assistant can use it safely
Tools I use
Questions
Do we need to move everything to Google Cloud?
No. Source systems stay where they are. I connect them and consolidate the data you need for reporting and AI into BigQuery.
Who owns the pipelines and the warehouse?
You do. Everything is built in your own Google Cloud project, with the code in your repository and full documentation at handoff.
Next step
Custom AI Agents & Workflow Automation →
Task-focused AI agents that take real action in your tools: reading documents, updating your CRM, handling intake and support, with guardrails and human approval where it matters.