Turn data questions into a guided conversation.
AI Chat gives Gemini structured context about your dataset—its columns, statistics, quality signals, and a small sample—so you can ask practical questions without writing SQL or formulas.
- Dataset-aware prompt context
- Server-side API key
- Plain-English answers + next steps
Less setup. More useful analysis.
Each feature is designed to move a real dataset forward rather than add another disconnected dashboard.
Lower the barrier to exploration
Ask ordinary questions about the dataset instead of translating every question into a formula, pivot table, or query first.
Stay grounded in dataset context
The model receives column metadata, descriptive statistics, missingness information, sample values, and a small row sample rather than an empty generic prompt.
Get analytical next steps
The assistant is instructed to suggest appropriate cleaning, charting, or filtering actions when that helps answer the user’s question.
What AI Data Chat actually does
Concrete product behavior—not placeholder features.
Natural-language questions
Ask about columns, quality, patterns, suitable charts, or what to inspect next.
Multi-turn context
The chat keeps a bounded conversation history so follow-up questions can build on recent context without growing indefinitely.
Server-side credentials
The Gemini API key remains in server configuration and is not sent to browser JavaScript.
Honest limits
The system prompt tells the model not to pretend it computed statistics it cannot derive from the available context.
Shared AI workflow
AI Chat sits alongside Auto Insights, charts, cleaning, and reports instead of acting as a separate chatbot product.
Resilient requests
The AI engine can retry temporary upstream failures and use the configured fallback model when appropriate.
From raw file to next action
The feature stays connected to the same dataset, so you do not have to recreate context in a second tool.
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1
Open a dataset
Choose the file you want to understand.
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2
Ask a focused question
Reference a column, quality concern, comparison, or analysis goal.
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3
Review the answer
Use the response as an explanation or a guide to the next action.
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4
Verify with the workspace
Use statistics, rows, charts, and cleaning controls for concrete validation.
Know what is included before you use it.
Amridata is most useful when the product is explicit about what it does, what it stores, and where a user should verify the result.
- Context sent
- Dataset name and size, column metadata, selected statistics and sample values, plus a small sample of rows and recent conversation history.
- Context not sent
- Your Gemini API key is never exposed to browser code. The model does not automatically receive every row when the dataset is large.
- Best questions
- Specific questions tied to columns, data quality, chart choice, interpretation, or next analytical steps.
- Availability
- AI responses depend on the configured Gemini service and can be temporarily unavailable during upstream capacity or API errors.
Use it with the rest of Amridata
Analysis, cleaning, visualization, AI, and reporting are designed to work around the same dataset.
AI Data Chat questions
Clear answers about the current product behavior.
Does AI Chat receive my entire dataset?
The current AI context is built from dataset metadata, statistics, sample values, and a small sample of rows rather than blindly sending every row.
Is the Gemini API key visible in the browser?
No. AI requests are proxied through the Amridata server, so the configured API key stays server-side.
Can AI Chat replace checking the actual data?
No. AI Chat is an interpretation and guidance layer. Important conclusions should still be verified with the dataset table, statistics, cleaning status, and charts.