Data Normalizer is an AI-powered solution designed to handle the complexities of data normalization. The tool cleans and standardizes data from various sources including Excel, Python, R, and SQL, combating issues such as typos, shortcuts, and inconsistent spellings. This ensures your data is accurate, consistent, and ready for analysis, thereby improving the reliability of insights extracted from your datasets.

Deep-Research-With-Web-Scraping-by-LLM-And-AI-Agent is designed to automate the end-to-end research workflow by combining web scraping techniques with large language model capabilities. Users define target domains, specify URL patterns or search queries, and set parsing rules using BeautifulSoup or similar libraries. The framework orchestrates HTTP requests to extract raw text, tables, or metadata, then feeds the retrieved content into an LLM for tasks such as summarization, topic clustering, Q&A, or data normalization. It supports iterative loops where LLM outputs guide subsequent scraping tasks, enabling deep dives into related sources. With built-in caching, error handling, and configurable prompt templates, this agent streamlines comprehensive information gathering, making it ideal for academic literature reviews, competitive intelligence, and market research automation.