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Datasets with 100 million daily transactions must be segmented, modelled, and calibrated per institution. Every model output needs a full audit trail and transparent documentation, so regulators and examiners can interrogate assumptions directly rather than treating the model as a black box.

The model must isolate legitimate credit factors like income and credit history from potential bias signals in lending decisions. When regulators scrutinise the output, every statistical conclusion must be defensible, making precision in the underlying regression modelling non-negotiable. 

There's no objective ground-truth for adviser-client interactions, and outcomes like investment returns play out over months, not minutes. Building eval frameworks where success is inherently subjective required a fundamentally different approach.

Property managers have to collaborate across an ecosystem of homeowners, boards, vendors, and regulators. Building AI that operates across these stakeholders, each with different needs, permissions, and context, requires agents that coordinate with each other without losing accuracy across associations.

Cash forecasting spans multiple entity structures, currencies, and industry-specific configurations, each with unique customer setups. Every AI-generated insight must be fully traceable to source data, requiring explainability at every step of the chain across these diverse environments.

FP&A classification and vocabulary differ for every customer, so a generic model fails immediately. The text-to-SQL agent auto-personalises based on each customer's historical data, learning their specific terminology and schema to return accurate results without manual configuration.