📄️ Why Custom AI Is Expensive
Many companies assume AI is simple: connect to a model API, write a few prompts, and productivity increases overnight. For light use cases, that can be true. But the moment an organization needs AI to follow internal policies, comply with regulations, generate strict output formats, or handle domain-specific edge cases reliably, the complexity — and cost — rises dramatically.
📄️ Why is Finetuning Technically Difficult
Even when companies commit to customizing their models, they quickly discover that fine-tuning is fragile. The process is not modular, not easily reusable, and not structurally isolated. Each adjustment to the model can introduce unintended side effects, making iteration slow and scaling across customers increasingly complex.