Every LLM that has come out since ChatGPT in 2022 is based on Text, Video and Voice data. That is Language data.
QLLM is the First Large Learning Model exclusively based on Numerical Quantitative Data.
It scales very large data like that seen in departments of major governments. These data are stored in large numbers of Tables across multiple functions in government or private sector. They need 1000s of programs and applications to work on them. This dissipates the essence of all this data. It is not possible to understand all these relationships and answer user queries like Opportunities, Risks, Scenarios, GDP impacts etc.
Now for the first time Quad is introducing a single model that can take virtually unlimited data in departments and organize them in a unique Tensor Data Model, comprised of microsegments. Then Quad’s Deep Learning based scalable engine works on the microsegments in highly parallelized ways to learn all the relationships and contexts of these data. It can then make linear equations that connect all the variables to outcomes. This is the key result. With this result we have a completely explainable large model that can predict every outcome with very high accuracy. The final step is to enable variables to be part of “Scenario Analysis” which allows leaders to change variables and predict outcomes. They can choose the “Optimal” scenarios and outcomes. Quad then changes the levers in all the underlying microsegments to deliver this optimal scenario to the leaders. All of the above results can be extracted through an Agentic AI architecture in keeping with the latest trend of 2025.
This is economical (no large data centers or GPU farms are needed to use and train QLLM) – training can be done within 1-5 million compared to billions for other LLM- easy to use and far more accurate than any LLM built for decision making so far. It’s the first one that is based entirely on Quantitative data. It is especially useful for very large scale integrated applications like those in the largest corporations or in governmental departments.
Currently it is being trialled in leading industry players like the largest airlines, manufacturers, and consumer goods companies, and supply chain industries. Results are very promising and can change the game of how AI is perceived. This puts AI to use in making the best decisions for economics and well-being of nations , corporations and individuals.
Microsegment engine
Decomposes businesses or departments into millions of stable “voxels” (example – people – services – cities – companies – channels ), each with its own local linear demand model and elasticities.
Causal, explainable models
Uses Taylor series–based linearization and causal drivers to produce transparent equations for every metric in every microsegment, not black-box
scores.
Global digital twin
Stacks all local models into an enterprise tensor that behaves as a constraint-aware digital twin of real-world processes.
Optimization & scenarios:
Runs Monte Carlo optimization across thousands of future scenarios to recommend strategies that are robust, not just point-optimal.
QLLM is designed as the quantitative “right brain” that complements existing GenAI/agentic AI investments.
Quad proposes four high-value pillars where QLLM can unlock step-change performance:
The timing is critical: as agentic AI begins to scale, the companies that pair language-based AI with a quantitative foundation model will set the performance frontier in pricing, promotions, and supply chain. The largest organizations are uniquely positioned to lead this shift.
