Datix

What is Datix?

Datix is an AI data analysis tool designed for spreadsheet-based workflows. It lets teams upload spreadsheet files and ask business questions in plain English to receive structured answers, summaries, and trends—without building dashboards or writing SQL.

Datix also connects to live sources so analysis can start from existing business data instead of relying on manual exports. It supports spreadsheet uploads (CSV and Excel) and direct connectivity to sources such as Google Drive and Supabase.

Key Features

• Conversational analytics for spreadsheets: Ask business questions in natural language and get data-backed, structured responses instead of manual calculations.
• Spreadsheet and file inputs (CSV/Excel): Upload CSV and Excel files to analyze structured data without setting up a separate BI workflow.
• Connectors for existing data sources: Use built-in connections to Google Drive and Supabase so you can analyze files and tables your team already uses.
• Live file sync for recurring work: Keep analyses close to the source by pulling spreadsheets from shared Google Drive folders and staying in sync.
• Supabase table access: Import structured application data from Supabase and query it through Datix without exporting to CSV first.
• Automated visualization: Automatically generate charts by identifying appropriate ways to represent trends and anomalies.

How to Use Datix

1. Get started by setting up Datix and choosing an input method: upload a CSV or Excel file or connect a data source such as Google Drive or Supabase.
2. Create or open your Datix workspace so the connected data is available for analysis.
3. Ask questions in plain English about the business data (for example, metrics, changes over time, or notable patterns).
4. Review structured outputs such as answers, summaries, and trends, including automated charts when helpful.

Use Cases

• Founder reporting from spreadsheets: Track runway, burn rate, and key metrics by working directly from Excel and CSV without relying on a dedicated data team.
• Marketing performance analysis: Connect marketing spend with revenue and use natural language queries to explore topics such as LTV and churn.
• Operational and finance insights: Analyze exports and structured operational/finance data to surface trends and anomalies without assembling dashboards or writing SQL.
• Teams standardizing analysis on shared files: Pull recurring reports from shared Google Drive folders using live sync so updates remain close to the source.
• Product or app data analysis via Supabase: Access structured app data directly from Supabase and ask questions across tables without exporting to CSV first.

FAQ

• What file types can I analyze with Datix? Datix supports CSV and Excel files.

• Can Datix analyze data from live sources or only uploads? Datix can start analysis from uploaded files and from connected sources, including Google Drive and Supabase.

• Do I need SQL or dashboard building to use Datix? No. The workflow is designed around asking questions in plain English and receiving structured answers without building dashboards or writing SQL.

• Is Datix suitable for founders and small teams? Yes. The product page states it is built for founders and lean teams that need answers from spreadsheets without relying on SQL, BI dashboards, or a dedicated analyst.

• How does Datix handle sensitive data? The site describes “Privacy by Design” and “Zero-Knowledge Analysis,” including encryption patterns intended to keep raw data invisible to underlying AI models, and that local processing helps ensure sensitive data does not leave the user’s environment.

Alternatives

• Spreadsheet-based analysis with pivot tables and formulas: A direct approach for teams that want calculations inside Excel/Google Sheets, but it typically requires more manual work for complex questions and visualization.
• BI dashboards and query tools (dashboard-first workflow): These focus on building and maintaining dashboards and structured datasets; they may involve more setup than a conversational spreadsheet workflow.
• General-purpose LLM tools with spreadsheet upload + prompting: These can provide conversational answers, but they may not include purpose-built connectors and structured analysis/visualization workflow tailored to spreadsheets and live data sources.
• Data analytics platforms with SQL-first exploration: Useful when teams already work with warehouses/tables and prefer query-based exploration, but they differ from Datix’s “no SQL” conversation workflow.