Microsoft has built a system-level approach to evaluate Microsoft Copilot in enterprise settings: the Industry Evaluation Data Generation Platform. The platform produces full-scale synthetic enterprise environments that mirror the complexity of real organizations while ensuring no customer data is used at any stage.
Why build synthetic environments?
Evaluating enterprise AI requires test data that captures reporting hierarchies, project histories, communication patterns and domain workflows. Real enterprise datasets contain sensitive information and cannot be reused for testing. To resolve this, Microsoft generates dynamic, isolated "tenant" workspaces that include realistic users, emails, meeting transcripts, documents and organizational structures without relying on any customer data.
Three core pillars of the platform
- Synthetic enterprise environments: simulated tenants with complete communication histories and organizational detail to provide meaningful evaluation baselines.
- Automated query generation pipelines: systems that convert generic query templates into industry-specific, environment-grounded questions at scale.
- Structured quality frameworks: metrics and assessments used to ensure synthetic data is suitable for evaluation.
Grounding in public, real-world signals
To keep the synthetic environments realistic, Microsoft grounds generation in public data sources reviewed by its legal and policy teams:
- O*NET (U.S. Department of Labor occupational data) to create realistic employee roles, responsibilities and activity mixes.
- Online job postings to provide realistic role titles, reporting hierarchies and departmental compositions.
- SEC filings to inform organizational scale, business unit structure and financial context.
These synthetic environments are time-evolving rather than static snapshots: meeting histories, project timelines and email threads develop over weeks, which matters for time-aware queries such as “What did the team decide last Thursday?” or “What changed since the last quarterly review?”.
Industry-specific simulations
Microsoft created three synthetic industry environments, each reflecting domain workflows:
- A pharmaceutical environment capturing regulatory submission workflows, clinical trial discussions and cross-functional safety reviews.
- A capital markets environment with deal pipelines, compliance communications and client portfolio reviews.
- An IT/software company simulation covering sprint planning, code-review discussions and product launch coordination.
This specificity helps surface domain-specific failure modes that generic benchmarks might miss.
Automated query generation pipeline
Synthetic data needs realistic evaluation queries. Manually authoring thousands of grounded, industry-specific questions is too slow and costly, so Microsoft built a six-stage automated pipeline. Starting from a large library of common workplace tasks, it selects industry-relevant items, adapts language and context, uses AI to generate queries grounded in the synthetic data, rewrites them to sound natural, and validates each query for accuracy and uniqueness. The outcome is a scalable set of lifelike questions for evaluating Copilot.
Validating synthetic quality
Microsoft measures synthetic environment quality with an internal semantic evaluation framework across these dimensions:
- Structural fidelity: do hierarchies, team sizes and cross-functional links reflect realistic organizations?
- Contextual relevance: are queries and workflows representative of real enterprise usage?
- Behavioral realism: do simulated users show human-like communication and collaboration patterns?
- Content safety: is generated content free of harmful, offensive or personally identifiable information and not impersonating real individuals?
Automation benefits and cost impact
Full automation addresses reproducibility, scale, iteration speed and cross-team consistency. According to Microsoft, automating dataset creation reduced what had been a multi-day manual process to automated runs. They estimate approximately $100,000 saved per evaluation dataset and about $300,000 in total project savings. Crucially, any team can regenerate an evaluation dataset from the same configuration and obtain identical results.
Privacy and safety by design
A design principle of the platform is that no real customer data is used at any point. All elements—organizational structures, emails, meeting transcripts—are synthetically generated and automatically safety-screened. Additional safeguards include privacy-preserving aggregation techniques that reference real-world statistical distributions, legal review and provenance tracking for all public data sources. Because datasets are both synthetic and safety-checked, teams can share and reuse them without additional privacy approvals or compliance overhead.
Results and impact
Since launching the project, Microsoft reports measurable improvements:
- Synthetic data quality: the composite quality metric improved by 39% compared to the initial generation approach, and some industry-specific environments achieved quality scores above 0.9 out of 1.0.
- Scale and efficiency: each synthetic environment now contains hundreds of users with full communication histories across three industry verticals; dataset creation moved from manual days to automated runs.
- Organizational adoption: the evaluation environments were published into an internal shared catalog and established as the first standardized evaluation baseline for Microsoft Copilot, shifting teams from ad hoc evaluation methods to standardized, reproducible infrastructure.
Next steps
Microsoft highlights ongoing evaluation challenges: assessing multi-turn agentic interactions, extending synthetic environments to capture organizational dynamics over months or years, and building evaluation frameworks for AI systems that perform actions rather than only generate text. The company positions the platform as infrastructure to ensure Copilot functions reliably across industries and scales, with evaluation, privacy and content safety as core commitments.
Acknowledgements were given to the teams who worked on the Industry Evaluation Datasets.
