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Technical teams often assume deepfake detection requires custom ML pipelines, extensive retraining, or infrastructure overhaul. In reality, detection integrates as a standard API call with sub-1000ms latency and production-ready accuracy. However, without clear integration guidance, teams face latency spikes, pipeline disruption, model drift, and scaling challenges. We created this guide to eliminate ambiguity, providing technical decision-makers with deployment options, performance benchmarks, and a proven roadmap for shipping detection in weeks, not months.
This guide includes integration option comparisons, production latency benchmarks across industries, Quick API example with request/response structure, challenges and solutions for latency, pipeline disruption, model drift, and scaling, plus a 4-week deployment roadmap from PoC to production. Access the technical framework for shipping deepfake detection without infrastructure overhaul.
