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RL Environment
An enterprise RL environment for sales and project management across seven apps — D365 CRM, PWS/PDP, Outlook, Teams, SharePoint, Workday, and Samay. Tasks span pipeline analysis, QBR prep, account briefs, deal-desk coordination, RFP responses, and handoff audits.
Screenshots
Industry
Environment specs
Persona / role
Problem
Sales and RevOps teams run complex, multi-system workflows – sweeping a CRM for stalled deals, routing escalations to the right manager, adapting mid-process when conditions change. Frontier AI agents are increasingly capable of automating these, but two gaps block real deployment: we don't have a rigorous way to identify exactly where and why they fail, and the training environments needed to improve them don't exist – most benchmarks use static, synthetic tasks disconnected from real enterprise systems and real domain knowledge.
Solution: We built a self-contained Opportunity-to-Cash practice environment grounded in actual SME workflows, with automatic grading that checks exact correctness across live enterprise tools. We then ran today's leading frontier models against it and applied a structured failure-mode taxonomy to every breakdown.
Impact: Even the strongest models fall short of reliable deployment – the failures are systematic and predictable, not random. Our contribution is a trustworthy foundation for enterprise RL: SME-validated tasks that reflect how work actually happens, an automatic grader that makes endless practice possible without human oversight of each run, and a failure taxonomy that tells you precisely what to fix rather than just that the model failed.
Solution
We built a self-contained Opportunity-to-Cash practice environment grounded in actual SME workflows, with automatic grading that checks exact correctness across live enterprise tools. We then ran today's leading frontier models against it and applied a structured failure-mode taxonomy to every breakdown.
Impact
Even the strongest models fall short of reliable deployment – the failures are systematic and predictable, not random. Our contribution is a trustworthy foundation for enterprise RL: SME-validated tasks that reflect how work actually happens, an automatic grader that makes endless practice possible without human oversight of each run, and a failure taxonomy that tells you precisely what to fix rather than just that the model failed.
Security
Disciplined security and privacy practices aligned with global standards to protect sensitive data, intellectual property, and model assets throughout the AI lifecycle.
Centific applies rigorous security, access control, and auditability standards to safeguard enterprise data, human workflows, and AI systems at scale.
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