From Query to Trade: AI Workflows That Move at the Speed of the Market

See how trading and risk teams use AI to interpret, onboard, validate and analyze new datasets in a single workflow using Flexible Data Loader and QueryEdge in Sphere. 

New market data creates opportunity, but only if your team can turn it into insight fast enough to act. 

When a new trading or risk dataset arrives, multiple handoffs often stand between the file and the decision. Data must be interpreted, mapped and validated before analysts can compare it against market benchmarks and uncover what matters. 

Join us on September 29 for an end-to-end workflow demonstration of how AI can accelerate the journey from raw data to business decisions. Watch a realistic market dataset move from raw file to actionable insight as AI helps onboard, validate, publish, discover and analyze data within Sphere. 

No slides. No theory. Just the workflow. 

What You’ll See 

1. The Decision Challenge Why new datasets often take too long to become actionable. 
2. The Market Use Case A realistic trading or risk file and the business questions it helps answer. 
3. AI-Powered Data Onboarding Upload, interpret and map new data using Flexible Data Loader. 
4. Validation and Publication Review AI-generated mappings, identify potential issues and publish the dataset. 
5. Discovering Market Context Use QueryEdge to find benchmarks, compare datasets and explore market relationships. 
6. From Data to Decision Transform a newly onboarded dataset into intelligence that supports trading and risk decisions.