Integrated Quantitative Model
Quad Innovation – Quant Transformer
We have a complete Transformer model for enterprise Quantitative data - the transactions in your source systems, your data warehouse, databases, appliacations and data marts. This big portion of your data is not being consumed and processed by LLMs today. They are instead being consumed by 100s of single purposed models. The Quant Transformer - branded TensorQ is a large model for handling all the Quant data of the enterprise. It brings together an integrated data model and integrated AI Model that output a complete set of forecasts. The final output can be optimized for any outcome - revenues, profits , quality , sustainability or a combination of these. TensorQ is a single model that produces all this. Thats the innovation we are driving.
Integrated Predictive Model
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The Impact of our Quant Transformer is a single integrated model that can predict and optimize all your company's metrics and levers. Today you are building multiple AI models to generate predictions. Then you run separte optimization programs to optimize the outputs one at a time. This certainly is one strategy to get to optimum. But it does not take you to true optimum because the metrics are interconnected while your models are not. This results in sub-optimal solutions.
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By contrast there is a reinforcement and compunding effect of an Integrated Model. Reinforcement is happening when the effects of one metric are transmitted through drivers to other metrics. while the metrics do not directly impact each other, they make the drivers to change and this changes the other metrics.
Compounding happens as each standalone AI model that is integrated into a larger combined AI model produces a non linear incremental dimensional effect on the topline. The more that come together, the more is the compounding of these incremental effects. For example if each model can add 0.1 % improvement in the topline, the combination of 10 major models can be more that 1 %. (10 * 0.1), Further, it can be of an order of 3 to 5 times more than that. Producing about 3-5% incremental top line growth.This is the major benefit of having a single Integrated Predictive Model.
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Tensor Q enables Interconnected AI
The Hypercube Tensor Data Model for Interconnected AI
We understand you have multiple AI projects across the enterprise with millions of dollars in IT and departmental budgets. Do they communicate with each other ? Do they reinforce the metrics and modeling outputs with each other? If you go deep into these questions, you will realize that interconnected AI is probably not a reality in your enterprise.
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Take the example of demand generation for airlines. Demand is generated not just by pricing actions alone. It is also generated by actions from the Sales teams, from the Marketing teams, from the Loyalty teams,from the Interline teams and from the Ancillary revenue teams. So to factor all these actions and produce a model that integrates all these actions and estimates demand accurately is not easily done using existing data structures within the enterprise.
Quad has created an intelligent multi-dimensional data model called the Tensor Data Model. This is similar to the DNA data structures that contain the intelligence powering organisms. Microsegments store the essential intelligence from the AI models that generate demand signals based on internal and external actions. The Microsegment AI data model is Quad’s invention in response to this need. sometimes you may think of it as a Hypercube made up of summarized signals within microsegments in multidimensional Hyperspace
