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Describe your dataset
Ask for "fintech companies founded after 2008 with revenue data." AskTab identifies the entities and proposes the right columns.
Research, structured
Describe the dataset you need. AskTab proposes the structure, researches selected cells, and keeps the source behind every answer.
Preview the proposed table for 1 credit before creating it.
Fintech research
3 rows · 3 columns
Per-cell sources
See what supports every researched value.
Review before use
Inspect and edit results directly in the grid.
Portable output
Export the finished work as a clean CSV.
Workflow
01
Ask for "fintech companies founded after 2008 with revenue data." AskTab identifies the entities and proposes the right columns.
02
Research runs only where you choose. Every completed value keeps its source attribution and review state.
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Edit cells, add rules, inspect evidence, and export clean data without rebuilding the table.
Built for research
Describe datasets in plain English. AskTab infers entities and structure.
Fill selected cells with up-to-date information from connected providers.
Open the evidence behind a researched value before using it.
Start with a proposed schema, then edit rows and columns directly.
Move the finished table into the rest of your workflow.
Ask questions about the table without losing the underlying data.
Create with AI
Start with a plain-language request. You can inspect the proposed rows, columns, and credit estimate before creating anything.
You review the proposed structure before creating the table.
Source transparency
Every researched value keeps its source and review state. Compare datasets and sort the example below.
Preview a table for 1 credit. Creation costs 2 credits plus 2 per generated row; enrichment is 2 per cell.
Try the full research workflow
For steady research and enrichment
For higher-volume recurring research
Start with a prompt, review the proposed structure, and keep control over every cell that runs.