Follow-up questions
Narrow, compare or explore an answer without repeating the original question every time.
Platform
From spreadsheet to decision, with a conversation that follows the work.
TalkToMyExcel preserves data structure, selects the right analysis and lets you explore results through natural follow-up questions.
How TalkToMyExcel works
01
Upload one or more files
Excel, CSV, TSV and Parquet files are checked before entering the user's dedicated workspace.
02
Confirm the useful data
Choose the sheets, tables and descriptive fields to include. The system suggests a selection; the decision stays explicit.
03
Ask and follow up
Start with one question and continue with related requests: recent context helps keep the thread.
04
Verify the answer
The result is grounded in selected evidence and shows tables or source rows when the analysis path makes them available.
Analysis experience
Closer to a real working conversation.
Multiple files, one view
Different datasets remain available in one workspace for comparisons and cross-file analysis.
Complex analysis
The system also handles cross-file differences, missing values, ratios, correlations and custom calculations.
Context under control
An indicator shows how much context is in use, and one control clears the conversation.
Formats and scenarios
One entry point for exports, reports and spreadsheets.
Current inputs
Excel .xlsx and .xls workbooks, plus .csv, .tsv and .parquet files. They are the simplest starting point for business datasets you already have.
Business use
After-sales, maintenance, tickets, quality, price lists, spare parts, CRM exports and periodic reports: different data, same question experience.
Hybrid engine
Precise numbers when numbers matter. Semantics when meaning matters.
An AI model alone can be brilliant but approximate. TalkToMyExcel assigns filters, counts, similar-case searches and calculations to specialized tools, then composes a readable answer.
See the security boundaryDemo
Bring a concrete use case.
A real question and, to begin with, a structure or synthetic or anonymized sample: the result must be checkable.