QCI Introduces the Next Generation of Table Yield Management

Quick Custom Intelligence (QCI) introduced Table Yield Management, helping casino operators anticipate table games demand and plan operations with forecasts powered by a neural learning engine. The engine learns from prior decisions and their outcomes to improve future predictions, putting operational experience to work in planning what comes next.

For casino resorts, the value extends beyond a single forecast. Predictions can be compared with actual outcomes to evaluate performance and guide further training. As additional operational data becomes available, the models can be refined to support the conditions casino teams face.

“What matters for casino resorts is that these neural processes learn from prior decisions to improve their predictions. Table Yield Management puts that capability to work in an operational setting. We’re building on what the operation has experienced to help forecast what comes next, and that creates exciting possibilities for both casino teams and agents that can use those forecasts,” said Andrew Cardno, Co-Founder and CTO of QCI.

QCI’s learning engine brings a different approach from a conventional rules engine. Rules engines follow predefined conditions and thresholds within the factors they are configured to consider. Neural networks learn relationships across many interacting factors, allowing complex patterns in operational data to inform predictions.

By applying this capability to table games yield management, QCI is connecting its investment in neural network technology with a practical operational need. The focus is on providing forecasts that teams can evaluate and use in planning, supported by a learning process that can be tested and refined over time.

“Our customers bring deep experience to running their table games operations. Table Yield Management gives them a way to put that operational history to work in future planning. Our focus is on developing technology around the realities of their business and continuing to expand what they can accomplish with the QCI platform,” said Dr. Ralph Thomas, Co-Founder and CEO of QCI.

The learning engine also provides a foundation for QCI’s agentic capabilities. Agents can use forecast outputs within operational workflows, helping connect what the models have learned with recommendations and actions. Table Yield Management is the first application of the engine, establishing a starting point for further product development.