Technical Deep-Dive
FOUNT's architecture reflects a simple idea: if you want to predict complex systems, your model must be able to represent how those systems actually work.
Standard transformer architectures are powerful because they capture relationships across sequences. But they were never designed to distinguish causation from correlation. They treat all relationships symmetrically, without direction.
FOUNT modifies this foundation. It embeds causal reasoning directly into the architecture, allowing the model to learn not just associations, but structured relationships where causes precede effects and interventions change outcomes.
The architecture begins by integrating diverse data sources into a single representation. Instead of processing inputs independently, FOUNT treats all signals — sales, pricing, weather, customer behavior, macroeconomic indicators — as parts of a single system.
This unified ingestion is critical because causal relationships often span across domains.
As data flows through the model, transformer attention mechanisms are used not just to identify dependencies, but to infer directional influence. Over time, the model learns which variables consistently act as drivers and which behave as outcomes.
The core processing layer is where FOUNT diverges most from conventional models. Here, attention is no longer just a weighting mechanism — it becomes a tool for causal discovery.
This is what enables FOUNT to move beyond observation into reasoning.
As the model scales across millions of multivariate sequences, it begins to capture patterns that are invisible to smaller or simpler systems.
Multiple variables combine to create amplified outcomes
Outcomes influence future inputs in cyclical patterns
Impacts that unfold over extended time periods
Rather than treating these as anomalies, FOUNT integrates them into its internal representation of the system. This is where forecasting becomes fundamentally different. The model is no longer extrapolating trends — it is modelling behavior.
The final layer of the architecture translates this understanding into outputs that are both predictive and actionable.
FOUNT generates forecasts across multiple targets simultaneously, ensuring that relationships between them remain consistent. It also enables counterfactual analysis, allowing users to explore how different decisions would impact outcomes.
This dual capability — prediction and simulation — is what makes the architecture valuable not just for forecasting, but for planning.
Most models improve performance by increasing scale. FOUNT improves performance by improving structure.
It is not just a larger model. It is a more aligned one.
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