Data Infrastructure & Reliability for Copilot

The Problem

Millions of people use Microsoft Copilot every day. Behind every AI-generated response is a pipeline of signals that needs to be accurate, fast, and reliable — because when the data is wrong, users get wrong answers. As Copilot scaled rapidly, there was no systematic way to validate the 10B+ signals flowing into evaluation pipelines each day. Regressions could silently degrade the experience for end users, and detection was manual and slow.

What I Did

I owned the distributed data infrastructure and validation layer for this ecosystem. This wasn't a single project — it was the intersection of three problems: building reliable distributed systems at scale, creating automated validation and observability for LLM signals, and designing scalable APIs that other teams could build on.

• Architected distributed infrastructure across Azure services, ensuring petabyte-scale data durability and consistency for 10B+ events per day
• Designed and built validation systems in C# (.NET) and U-SQL that caught quality regressions before they could reach model evaluation
• Built automated regression detection and observability that replaced manual checks — reducing detection time by 60%
• Designed scalable APIs for internal platform services, including high-security identity management with complex permission hierarchies and high-concurrency access patterns
• Created reusable tooling and patterns that were adopted as the standard across multiple teams in the Copilot ecosystem

The Hard Parts

The interesting challenge wasn't any single technical problem — it was that reliability at this scale is fundamentally a user-facing problem disguised as a systems design problem. A silent regression in signal validation doesn't just show up in a dashboard — it shows up as worse answers for real people using Copilot. You can't just add validation checks; you need to design distributed pipelines where quality is observable by default, regressions are caught before they reach users, and the whole system degrades gracefully when upstream signals change unexpectedly.

Impact

• Reduced regression detection time by 60% through automated validation and observability
• Built the reliability and data-quality standard adopted across the Copilot ecosystem
• Infrastructure served 10B+ daily events with petabyte-scale durability
• Directly improved the reliability of AI outputs for millions of Copilot users