Current AI models can't handle finance at any scale.
They hallucinate balance sheets that don't balance. Generate cash flows that violate accounting identities. Produce forecasts that ignore debt covenants and regulatory constraints.
Finance isn't just about numbers, it's about relationships between numbers that must hold. When Assets ≠ Liabilities + Equity, your model isn't just wrong, it's fundamentally broken. When projected cash flows ignore existing debt obligations, you're not forecasting, you're fiction writing.
Yet we're forcing organizations to use AI built for casual conversation, not mission-critical calculations where every constraint matters and every decimal counts.
You can't patch financial intelligence into general models.
Finance has laws: accounting identities, regulatory requirements, mathematical relationships that are non-negotiable. Generic AI treats these as suggestions. It learns patterns in text, not the underlying financial laws that govern how money actually works.
This isn't a training data problem or a fine-tuning problem. It's an architecture problem. Models built for language will always be tourists in the world of finance.
We're building AI with finance baked in.
Neural networks that enforce accounting identities as mathematical constraints, not learned behaviors. Models that understand that cash flow forecasts must respect debt service requirements, that pro formas must satisfy regulatory ratios, that every financial projection exists within a web of non-negotiable relationships.
Not another LLM wrapper. Not fine-tuned ChatGPT. Purpose-built financial intelligence that treats constraints as requirements, not preferences.
This is Orvyn.