Case Study: Leading Conglomerate Company achieves scalable, high-accuracy automated content moderation with Shaip

Over 30K+ documents web scrapped and annotated into Toxic, Mature, or Sexually Explicit categories

Leading Conglomerate Company engaged Shaip to source and prepare training data for an automated content moderation machine learning model. The customer needed ethically sourced, bilingual (English and Spanish) web content—collected, segmented, and labeled for toxic, mature, or sexually explicit material—within a six‑month timeline, so Shaip’s Web Scraping/Data Collection and Text Classification/Annotation services were contracted to fulfill the brief.

Shaip scraped and annotated more than 30,000 documents (≈15,000 per language), organized by short/medium/long segments and labeled into Adult/Sexually Explicit, Mature, and Toxicity categories (about 10k examples each). Using a two‑tier quality control process to meet the client’s 90% accuracy benchmark and delivering the dataset in six months, Shaip enabled the customer to deploy a more scalable, consistent, and efficient automated moderation system that measurably improved platform safety and moderation throughput.