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How to Pick an AI Text Classification API for E-Commerce Hiring Pipelines

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A developer building an e-commerce backend for scoring job candidates recommends choosing an AI classification API based on JSON contract reliability, per-tenant cost visibility, and model-switching flexibility rather than general benchmarks. The core metric proposed is schema-conforming classifications divided by attempted classifications, treating any output that fails JSON Schema validation as a failed call. OpenAI, Claude, and Gemini should all be evaluated on the same held-out set of real, previously reviewed candidate records spanning sparse, lengthy, and multilingual résumés. Per-tenant usage tracking is emphasized as critical, since large tenants can skew aggregate cost data and obscure billing accountability. The author also presents a buy-versus-build comparison of direct provider integrations versus a portable routing layer, advising teams to set accuracy thresholds from their own error budgets rather than universal standards.

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