Small teams can normalize multi-model API for invoice data extraction

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A technical article from DEV Community proposes a practical method for small teams to extract structured data from invoices using multiple AI models. The approach centers on creating a single normalized API layer that works with OpenAI, Claude, and Gemini, keeping the specific model choice outside the core application code. This design prioritizes portability and simplifies future model swaps by focusing on common chat and JSON functions over vendor-specific features. The method emphasizes rigorous validation of extracted invoice data, such as invoice numbers, dates, and totals, to ensure accuracy over raw speed. The article includes a sample TypeScript script demonstrating the concept using environment variables for configuration.

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