Classifying whether text was written by AI; as models improve, detection needs constant fine-tuning and hybrid deep-learning-plus-heuristic methods.
AI content detection is the task of classifying whether a piece of text was written by a human or generated by an AI model. It's a moving target: as the latest Gemini, GPT and Claude models improve, they increasingly slip past even the best detectors, so a classifier needs a fresh fine-tune on each new model's output.
We brought detection in-house to keep pace. Our base model, DEJAN-LM, was pre-trained on a 10-million-sentence dataset of high-quality editorial content, then fine-tuned for detection on 20 million sentences split evenly between human and AI-derived text. When deep learning alone struggled — OpenAI's newest model was detected at just 20.8% — we added a word-frequency heuristic, lifting combined confidence on that elusive model to 68.1% and back into the "AI-generated" range.
For content and search, reliable detection underpins quality signals and trust. It's a sibling of content substance classification and relevant to how AI crawlers and platforms judge what they ingest.
