AI-Powered Suicidal Ideation Detection for Text Safety
Developed and fine-tuned an LLM to detect signs of suicidal ideation in text with >94% accuracy, reducing manual review effort by
Overview
We collaborated with a mental-health tech provider to automate the detection of self-harm content across user messages and posts, ensuring rapid intervention while preserving privacy.
The Challenge
The client needed a high-precision solution to flag suicidal ideation in large volumes of unstructured text, without generating excessive false positives that could overwhelm support teams.
Our Solution
- Curated and ethically annotated datasets reflecting diverse linguistic expressions of self-harm
- Fine-tuned a state-of-the-art LLM on this dataset for robust intent recognition
- Deployed the model via a scalable API integrated into the client’s moderation pipeline
- Implemented confidence thresholds and human-in-the-loop review for edge cases
Results
Average inference time
Manual review workload reduced
False positives rate
Detection accuracy
Our AI-first approach delivered a reliable, fast-acting safeguard against self-harm content, enabling the client to scale content moderation while maintaining high ethical standards
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