AI Email Labeling for Google Workspace – From PoC to Production
Transform manual email triage into a secure, automated pipeline using Large Language Models (LLMs).
Many organizations have tested AI workflows using no-code tools like n8n - but scaling those proofs of concept (PoC) into production demands more:
- Stronger privacy – redact PII before inference
- Controlled cost – per-user budgets, quotas, rollback
- Enterprise readiness – audit logs, least-privilege access, CI/CD
Our white paper shows how to migrate an n8n-based prototype to a production-grade solution in Google Cloud, using Terraform, Cloud Run, and version-pinned LLMs.
What You'll Learn
- Why Google Apps Script and no-code tools fall short at scale
- How we built a scalable, secure architecture for Gmail auto-labeling
- A sprint-based Agile roadmap from PoC to full deployment
- FinOps controls and compliance evidence generation
- Optional managed services and clean DevOps handover
Who It's For
- IT leaders looking to automate email classification
- Compliance teams needing audit-ready AI workflows
- Developers and platform engineers planning Workspace-native automation
- Consultancies standardizing inbox workflows across teams
Download the full white paper
Need help implementing?
Request a workshop or discovery sprint: ragonaut.com/outcomes
or contact: expert@ragonaut.com