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Start with off-the-shelf. If Azure Document Intelligence handles your invoice extraction or GPT-4 classifies your emails accurately with a few examples, you can be live in weeks instead of months. Building a custom model for the same task costs significantly more and requires thousands of training examples.
Custom AI makes sense in three situations. First, your process is highly domain-specific and generic models lack the right training data (think pharmaceutical lab notes or specialised legal documents). Second, you need explainability that black-box LLMs cannot provide. Third, data sovereignty requirements mean you need models running entirely on-premise.
Hybrid approaches are common and often the smartest path. Use off-the-shelf document intelligence for standard extraction, then layer custom validation rules for your specific business logic. Use a general LLM for initial classification, then route ambiguous cases to a more conservative domain-specific model.
Roborana evaluates both options during strategy. The trend favours off-the-shelf: models are improving rapidly and closing the gap on custom solutions. We recommend building custom only when the analysis clearly supports it.
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