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• Executives see the full picture – instantly. Ask Derive what you need to know from your data. "What are the three biggest drivers of profit change since last month, and how much did each contribute" and "forecast next quarter under three scenarios, base case, downside, upside, then show the exact assumptions and calculations behind each."
   

• Analysts automate repetitive work and focus on what moves the needle. Ask Derive "where did conversion change the most since last week, and which segments caused it" and "build a metric tree for this KPI, map each node to source tables and business rules, then produce a fully traceable calculation path."
   

• Data Scientists validate models and explore with precision. You'll want to ask things like "is there measurable drift in the input data compared to the training window, and where" and "validate this model end to end, data, features, training, evaluation, then output a reproducible report with metrics, thresholds, and traceable results."
   

• Business Teams get answers about data and code they actually understand, like "which customers are most at risk of churn this month, and what measurable factors are driving that risk" and "map the order to delivery process end to end, quantify bottlenecks by time and cost, then show the projected impact if each bottleneck is removed."
   

• Developers can ask "what does this code do" or even "traverse all of my code, only look in the relevant context (rules in a database server, and the rules those rules have, and their constraints, etc.), then give me a desired output (work item backlog, user stories, Jira tickets, etc.) in a fully traceable pipeline that includes live source code change detection for a claims adjudication platform."
  
  Done.

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That wasn't a hallucination. Your results won't be, either.

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