How to Build a Scalable LLM Mentions Tracker with Bright Data

A Universal LLM scraper has been developed to track mentions of brands across various large language models (LLMs) such as ChatGPT, Perplexity, Gemini, Grok, and CoPilot, using a unified interface. This tool helps companies ensure their presence in AI-generated content, which is crucial as users increasingly rely on AI chatbots for information rather than traditional search engines. The scraper, built with Bright Data's Web Scraping API, streamlines the process of querying multiple LLMs and normalizes outputs for easy comparison, allowing businesses to quickly determine if they are being mentioned in AI responses. The project includes both a command-line interface and a Streamlit-based UI for running scrapes, managing prompts, viewing results, and scheduling automated tasks. It integrates with a Supabase database to store and track historical data, enabling insights into brand visibility trends and competitive analysis. This infrastructure is vital for enterprises looking to maintain and enhance their brand's presence in AI-driven environments.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.