Enterprise AI
AI observability tooling for checkout diagnostics.
During my PayPal Checkout internship, I worked on AI observability tooling across 15+ internal checkout systems. The public-safe summary: the project reduced issue detection time by 75% in the internship workflow and made complex diagnostic data easier for product teams to use.
Problem
Checkout systems generate technical signals that are hard for non-engineering stakeholders to interpret quickly during friction investigations.
Role
Technical PM intern connecting product workflows, diagnostics, AI tooling, and observability patterns.
Result
75% faster detection in the internship project workflow. $100M target uplift remains a modeled opportunity, not an achieved claim.
Public-safe boundaries
- No internal screenshots, logs, customer data, or system names beyond resume-approved language.
- Use "built AI observability tooling" instead of implying ownership of PayPal-wide AI agents.
- Use "targeted" or "modeled" for revenue opportunity language unless a public result exists.
What this demonstrates
- Comfort operating inside a large technical system with many dependencies.
- Ability to translate diagnostics into product workflows.
- Judgment about what to say publicly and what to keep confidential.