Filtering job posting data before LLM-assisted clustering and hierarchical labeling yields taxonomies with better AI skill coverage than unfiltered approaches.
Proceedings of the 2025 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region , pages =
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TingIS uses multi-stage LLM event linking plus routing and filtering to extract high-priority incidents from noisy customer data at 2,000 messages per minute, delivering 3.5-minute P90 latency and 95% discovery in production.
citing papers explorer
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Building a Custom Taxonomy of AI Skills and Tasks from the Ground Up with Job Postings
Filtering job posting data before LLM-assisted clustering and hierarchical labeling yields taxonomies with better AI skill coverage than unfiltered approaches.
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TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale
TingIS uses multi-stage LLM event linking plus routing and filtering to extract high-priority incidents from noisy customer data at 2,000 messages per minute, delivering 3.5-minute P90 latency and 95% discovery in production.