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NewsMaterial4 min readDisconfirmAI Editorial

How Microsoft Builds an Enterprise Evaluation Environment for Copilot Using Public Data

The Microsoft Research team describes its Industry Evaluation Data Generation Platform: anchored on U.S. Department of Labor O*NET occupational data, online job postings, and U.S. Securities and Exchange Commission (SEC) filings, it generates synthetic enterprise tenants with hundreds of users, paired with a six-stage query generation pipeline and a four-dimension quality validation framework to establish a standardized evaluation baseline for Copilot without ever accessing customer data.

2026-10-30 21:05 GMT+8

Enterprise AI evaluation faces a structural contradiction: to test how Copilot performs in real work scenarios, one needs complete organizational data including reporting lines, project histories, communication patterns, and industry-specific workflows; yet this data belongs to customers and cannot be used for evaluation under any circumstances. In a blog post published on October 30, 2026, the Microsoft Research team described their solution to this contradiction—the Industry Evaluation Data Generation Platform.

The core idea is to build full-scale synthetic enterprise environments, referred to in Microsoft terminology as "tenants": each tenant has its own users, data, and configurations, simulating structures and behavioral characteristics identical to real organizations. These environments are not static snapshots but simulate dynamic, evolving organizations—containing meeting histories, project timelines, email threads spanning weeks, and naturally occurring organizational changes. The team emphasized that the temporal dimension is indispensable because a large number of Copilot queries are inherently time-aware, such as what the team decided last Thursday or what has changed since the last quarterly review.

The realism of the synthetic environment comes from anchoring it in public data. The U.S. Department of Labor’s O*NET occupational database is used to align synthetic employee roles and responsibilities with reality, calibrate the mix and frequency of work activities assigned to different roles, and customize industry-specific workflows such as clinical trial reviews or portfolio updates. Online job postings provide authentic job titles, reporting hierarchies, and departmental compositions. U.S. Securities and Exchange Commission (SEC) filings are used to determine organizational scale, business unit structures, and financial backgrounds. All sources have been reviewed and approved by Microsoft’s legal and policy organization.

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The team generated synthetic environments across three industries: a pharmaceutical environment containing regulatory submission workflows, clinical trial discussions, and cross-functional safety review meetings; a capital markets environment containing deal pipelines, compliance communications, and client portfolio reviews; and an IT/software company environment simulating sprint planning, code review discussions, and product launch coordination. This industry specificity ensures that evaluation queries test questions that real users in those fields would actually ask, rather than relying on generic test cases that might fail to expose domain-specific failure modes.

With environments established, queries are needed. Manually writing thousands of industry-anchored evaluation queries is too costly and slow, so the team built a six-stage automated pipeline: starting from a library of common workplace tasks, filtering for relevance by industry, adapting them using domain-specific language, generating queries via AI based on synthetic environment data, rewriting them for more natural expression, and finally validating accuracy and uniqueness item by item.

Quality validation uses an internal semantic evaluation framework measuring four dimensions: structural fidelity (whether organizational hierarchy, team size, and cross-functional relationships reflect reality), contextual relevance (whether queries and workflows represent actual enterprise usage patterns), behavioral authenticity (whether simulated users exhibit human-like communication and collaboration dynamics), and content safety (ensuring generated content contains no harmful, offensive, or personally identifiable material, and avoids impersonating real individuals).

Regarding results, the team reported that synthetic data quality improved by 39% on their internal composite metrics compared to initial generation methods, with some industry environments scoring above 0.9 out of 1.0. Automation reduced evaluation dataset creation from a multi-day manual process to automated runs, saving approximately $100,000 per dataset and an estimated total of $300,000 for the project. The platform has been published to an internal shared catalog for cross-team reuse and established as the first standardized evaluation baseline for Copilot.

It must be noted that all the aforementioned figures are self-reported internal metrics by Microsoft and have not been verified externally. Whether synthetic environments truly capture failure modes that only real customer data can expose remains an unanswered question. The team itself acknowledged in the conclusion that evaluating multi-turn agent interaction quality, simulating organizational dynamics over months or years, and building evaluation frameworks for AI systems that take actions rather than just generating text are all unresolved open issues. Privacy design is positioned not as a constraint but as a prerequisite enabling broad reuse; this judgment itself also belongs to the vendor's self-stated position.

Sources

  • microsoft.comMicrosoft Research↗
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