DeepResearch does not directly analyze structured datasets (like CSV, Excel, or SQL tables) or perform numerical computations, statistical modeling, or visualization. Instead, it focuses on processing and understanding text-based content. If you provide a dataset, the tool can only work with textual representations of that data—for example, descriptions of the dataset’s structure, summaries of trends, or excerpts from the data formatted as plain text. It cannot parse raw data files, execute code, or generate charts.
For example, if you share a text summary like “My sales dataset has 1,000 rows with columns for date, product, region, and revenue. The average revenue in Q2 was $50,000, and the top product was X,” DeepResearch can help analyze patterns, suggest potential insights, or answer questions about the metadata. However, it cannot calculate the average revenue itself from a raw CSV file, identify outliers algorithmically, or create visualizations. Developers would need to preprocess data into textual summaries or use external tools (like Python scripts) to handle numerical analysis, then feed those results into DeepResearch for interpretation.
This approach is useful for brainstorming hypotheses, refining questions about the data, or contextualizing results from other tools. For instance, after using a library like Pandas to calculate statistical metrics, you could ask DeepResearch to explain the implications of a correlation coefficient or suggest business strategies based on the findings. While limited for raw data tasks, its strength lies in synthesizing text-based insights, connecting concepts, or providing domain-specific context to supplement quantitative analysis.