Customer Ops at Scale
Systematic documentation that turned high-volume support into a repeatable process
A professional social network reimagining how companies hire. Candidates record video interviews for job applications so companies can understand who they are, not just what's on their resume.
The Challenge
When I joined allUP, customer success was handled ad-hoc. There were a few trusted templates, but no formal system—responses were created on the fly without consistent structure. As the sole CS person, I needed systems that would let me handle growing volume efficiently while maintaining quality.
Building the Infrastructure
I created 80+ templates and knowledge base articles in Notion, organized by priority based on message frequency. This became our single source of truth for candidate communication. These templates then became the foundation for knowledge bases in Front and Claude, enabling fast, consistent responses across every channel.
Each template covered FAQ responses, common questions, and edge cases for both email and in-app replies. Responses often required pulling information from multiple sources—company admin, Customer.io, candidate profiles, Retool—so having organized, documented workflows was critical for speed and accuracy.
I also used Customer.io for candidate communications like status updates, data confirmations, and subscription management, enabling consistent messaging at scale rather than manual one-off responses.
I built Front tags to categorize incoming messages by type and urgency. High-priority cases moved into separate folders from the main inbox, making it clear at a glance whether something needed a quick template reply or manual review. This tagging system eventually became the foundation for AI-powered triage.
The Impact
These systems let me operate as a one-person CS function handling volume that would typically require a team. But they also prepared us for growth—everything was documented so others could step in when needed.
- Speed and consistency: Templated workflows meant faster responses without sacrificing quality
- Scalability: Systems that grew with volume instead of breaking under it
- Coverage ready: Anyone could step in when needed, since priority levels and templates were clearly documented
- Foundation for automation: The tagging taxonomy I built became the basis for later AI implementation
- Proactive improvement: Patterns I identified in support emails informed FAQ content, working with marketing and design to address common questions before they reached the inbox