How do you use DeepResearch to analyze data from a provided dataset, or does it strictly browse text content?

DeepResearch is designed to handle both structured dataset analysis and text-based content browsing, but the approach differs depending on the input type. For datasets (e.g., CSV, Excel, or JSON files), the tool processes structured data using statistical methods, pattern recognition, or machine learning models. For unstructured text content (e.g., articles, social media posts), it uses natural language processing (NLP) to extract insights. The key distinction lies in how the tool interacts with the data: structured datasets enable quantitative analysis, while text browsing focuses on qualitative interpretation.

When working with a provided dataset, DeepResearch typically requires preprocessing steps like data cleaning, normalization, or feature engineering. For example, if you upload a CSV containing customer purchase records, the tool might identify trends in sales over time, segment customers based on buying behavior, or detect anomalies in transaction amounts. This is achieved through algorithms like clustering, regression, or classification, depending on the use case. Developers can configure these workflows via APIs or scripting interfaces, allowing integration with existing data pipelines. A practical example: analyzing a dataset of sensor readings from IoT devices to predict equipment failures using time-series forecasting models.

For text content, DeepResearch scans and parses documents, web pages, or user-provided text to extract entities, sentiment, or themes. For instance, if you input a collection of product reviews, the tool could summarize common complaints or highlight positive feedback. However, this mode is less suited for numerical analysis or structured queries. Developers might use this to monitor brand mentions across forums or generate summaries of research papers. While dataset analysis relies on structured data formats and statistical rigor, text browsing emphasizes semantic understanding and contextual interpretation. Both modes can complement each other—for example, combining sales data with customer reviews to build a holistic view of product performance.