From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence
Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances. The post From synthetic enterprise data evaluations to real Microsoft Copilot…


At a glance
- Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances.
- To resolve this tension, we built Industry Evaluation Data Generation Platform: full-scale synthetic enterprise environments grounded in publicly available real-world data sources such as O*NET occupational data, online job postings, and SEC filings.
- The synthetic environments mimic real business workflows, helping Microsoft find issues that generic benchmarks would miss.
- Safety and privacy are built into the platform by design. Every environment is fully synthetic and automated content-safety checks keep the generated data free of harmful or personally identifiable material.
Every day, millions of knowledge workers around the world rely on Microsoft Copilot to draft documents, summarize meetings, surface relevant information, and accelerate productivity. Behind the scenes, this is powered by a network of AI systems, and ensuring they work as intended requires a thoughtful approach to evaluation. At Microsoft, we know that high performance doesn’t come from guesswork. It comes from testing, measuring, and iterating constantly.
خواندن Microsoft Research با پرو
این منبع جزو کتابخانه پرو است. همه منابع ویژه، ترجمه هوشمند و فیدهای شخصی را باز کنید.
منتشرشده در
Microsoft Research
ادامه مطالعه





