The organization struggled to efficiently process vast amounts of news data to track corporate reputation and industry trends. Their existing system relied on manual inspection and a basic machine learning model, making it difficult to handle large-scale information sources. This time-consuming approach delayed insights and limited the depth of analysis. The challenge was to develop a more automated and scalable solution that could process data faster, improve accuracy in identifying relevant articles, and extract deeper insights.

The solution developed a Generative AI-powered MVP to automate news data processing, addressing the challenge of slow, manual analysis. Using Google Cloud’s generative AI tools, including the PaLM 2 API (text-bison), it streamlined article retrieval, classification, and summarization. Advanced LLM prompt engineering enabled direct extraction from URLs, eliminating resource-intensive HTML scraping. Relevant information, such as domains, entities, topics, and summaries, was stored in BigQuery for deeper analysis, enhancing trend identification and sentiment analysis while significantly improving processing speed and accuracy.

Increased Efficiency: Automated news screening significantly boosted throughput, replacing the slow manual process.

Enhanced Accuracy: Improved article identification by 20%, ensuring more relevant insights.

Timely Intelligence: Faster processing enabled real-time monitoring of corporate reputation and industry trends.

Ready Foundation: Established groundwork for data monetization and advanced AI integration.