Enago: More AI Isn’t the Answer. Smarter Orchestration Is

As submission volumes grow and generative AI reshapes research writing, the integrity landscape is becoming more complex. Publishers face overlapping risks across research integrity, content reliability, and structural compliance. Yet many screening workflows still depend primarily on a single dominant method - rule-based systems, classical machine learning, or large language models. Each is powerful, but each has limitations.

This session presents a multi-layered integrity screening model that applies different technologies to the problems they address best. Deterministic rule-based systems ensure policy alignment, structural completeness, and metadata validation. Classical AI and machine learning detect patterns, anomalies, and behavioral signals across submission data. LLMs enable semantic reasoning, interpreting context and identifying subtle manipulation, including AI-assisted rewriting. The real innovation lies in the orchestration of all three into a coherent decision support layer.

We demonstrate, with Enago Reports, how this layered approach delivers consolidated credibility insights within real publishing workflows, rather than fragmented outputs.