Getting Started

Transform your data analysis workflow in minutes. From upload to insights, Excel Copilot makes data magic accessible to everyone.

Quick Start Guide

Get up and running with Excel Copilot in just 3 simple steps

📁 Upload Your Data

Drag and drop your CSV or Excel files directly into the upload zone. Excel Copilot supports multiple formats and automatically detects your data structure.

💬 Ask Questions

Use natural language to explore your data. Ask things like "Show me sales trends" or "Create a chart of revenue by region" - no complex formulas needed!

📊 Get Insights

Receive instant AI-powered analysis, interactive charts, and actionable recommendations. Export results in multiple formats or generate code for automation.

Core Features

Powerful capabilities designed for modern data analysis

🧠

Smart Data Analysis

AI-powered insights that automatically detect patterns, trends, and anomalies in your data. Get comprehensive statistics and data quality assessments instantly.

Ask: "What are the key insights in my sales data?"
Get: Automatic trend analysis, outlier detection, and business recommendations

💬

Natural Language Queries

Skip complex formulas and SQL syntax. Ask questions in plain English and get precise, data-driven answers with visualizations.

"Show me top 5 customers by revenue"
"What's the correlation between age and spending?"
"Create a monthly trend chart"

📈

Auto Chart Generation

Beautiful, interactive charts generated automatically based on your data and questions. Support for bar, line, pie, scatter plots, and more.

✓ Bar Charts - Category comparisons
✓ Line Charts - Trend analysis
✓ Pie Charts - Proportion views
✓ Scatter Plots - Correlation analysis

⚡

Code Generation

Generate Excel VBA macros, SQL queries, and formulas automatically. Perfect for automating repetitive tasks and advanced data manipulation.

Sub RemoveDuplicates()
    ActiveSheet.Range("A:Z").RemoveDuplicates _
    Columns:=Array(1, 2), Header:=xlYes
End Sub

🔄

Format Conversion

Seamlessly convert between Excel formulas, SQL queries, and VBA code. Perfect for cross-platform data operations and learning.

Excel: =VLOOKUP(A2, Table1, 2, FALSE)
SQL: SELECT t2.value FROM table2 t2 
     WHERE t2.key = t1.key

💾

Multi-Format Export

Export your processed data, analysis, and visualizations in CSV, Excel, PDF, or JSON formats. Share insights effortlessly across teams.

📥 CSV - Raw data export
📥 Excel - Formatted workbooks
📥 PDF - Professional reports
📥 JSON - Analytics export

Supported File Formats

Import your data from various sources seamlessly

📄

CSV Files

Comma-separated values with automatic delimiter detection, header recognition, and data type inference.

✓ Automatic encoding detection
✓ Custom delimiter support
✓ Robust error handling
✓ Large file optimization

📊

Excel Files

Full support for .xlsx and .xls formats with automatic worksheet selection and formula preservation.

✓ Multiple worksheet support
✓ Formula calculation
✓ Formatted data preservation
✓ Chart metadata extraction

Best Practices

Tips for getting the most out of Excel Copilot

🎯

Data Preparation

Ensure your data is clean and well-structured for optimal AI analysis.

• Use clear, descriptive column headers
• Remove empty rows and columns
• Ensure consistent data formats
• Include units in column names when relevant

💡

Effective Queries

Frame your questions clearly to get the most accurate and helpful responses.

• Be specific about what you want to analyze
• Mention column names when relevant
• Ask follow-up questions for deeper insights
• Use comparative language for trends

⚡

Performance Tips

Optimize your workflow for faster processing and better results.

• Start with smaller datasets for testing
• Use the quick prompts for common tasks
• Export intermediate results frequently
• Leverage the format converter for code reuse

Real-World Examples

Discover how Excel Copilot transforms complex data analysis tasks into simple conversations. From sales analytics to financial modeling.

📈 Sales Performance Analysis

Business Intelligence

Analyze quarterly sales data to identify top performers, seasonal trends, and growth opportunities. Perfect for sales managers and business analysts.

📋 Sample Data Structure

Date,Region,Salesperson,Product,Revenue,Units_Sold
2024-01-15,North,John Smith,Widget A,15000,300
2024-01-16,South,Jane Doe,Widget B,12000,200
2024-01-17,East,Mike Wilson,Widget A,18000,360
2024-01-18,West,Sarah Lee,Widget C,9000,150

💬 Natural Language Queries

• "Show me top 5 salespeople by revenue"
• "Create a monthly trend chart of total sales"
• "Which region has the highest average deal size?"
• "Compare product performance across quarters"
• "Identify seasonal patterns in sales data"

📊 Generated Insights

✓ Revenue Analysis: $2.4M total sales
✓ Top Performer: John Smith ($450K)
✓ Best Region: East (35% of total revenue)
✓ Growth Trend: 15% QoQ increase
✓ Seasonality: Peak in Q4, dip in Q1

⚡ Generated VBA Code

Sub CreateSalesDashboard()
    Dim ws As Worksheet
    Set ws = ActiveSheet
    
    ' Create pivot table for sales summary
    ws.PivotTableWizard _
        SourceType:=xlDatabase, _
        SourceData:=ws.Range("A1").CurrentRegion
        
    ' Format and style the dashboard
    With ws.PivotTables(1)
        .PivotFields("Region").Orientation = xlRowField
        .PivotFields("Revenue").Orientation = xlDataField
    End With
End Sub

💰 Financial Portfolio Analysis

Finance

Analyze investment portfolios, calculate risk metrics, and optimize asset allocation using advanced financial modeling techniques.

📋 Portfolio Data

Date,Stock,Price,Volume,Sector,Market_Cap
2024-01-01,AAPL,192.53,45234567,Technology,3000000000
2024-01-01,MSFT,384.52,23456789,Technology,2850000000
2024-01-01,JPM,168.45,15678901,Financial,480000000

💬 Analysis Queries

• "Calculate portfolio beta and alpha"
• "Show correlation matrix for my stocks"
• "What's the Sharpe ratio for each asset?"
• "Create a risk-return scatter plot"
• "Generate Monte Carlo simulation"

📊 Financial Metrics

Portfolio Beta: 1.23
Sharpe Ratio: 1.87
Max Drawdown: -12.4%
Volatility: 18.6%
Annual Return: 24.3%

🗃️ SQL Query

SELECT 
    Sector,
    AVG(Price) as avg_price,
    SUM(Volume) as total_volume,
    COUNT(*) as stock_count,
    STDDEV(Price) as price_volatility
FROM portfolio_data 
WHERE Date >= '2024-01-01'
GROUP BY Sector
ORDER BY total_volume DESC;

👥 Customer Behavior Analytics

Marketing

Understand customer segments, analyze purchasing patterns, and predict customer lifetime value for targeted marketing campaigns.

📋 Customer Data

Customer_ID,Age,Gender,Location,Purchase_Amount,Purchase_Date,Category
C001,28,F,New York,245.50,2024-01-15,Electronics
C002,35,M,California,89.99,2024-01-16,Books
C003,42,F,Texas,456.78,2024-01-17,Clothing

💬 Marketing Questions

• "Segment customers by purchasing behavior"
• "What's the average customer lifetime value?"
• "Which age group spends the most?"
• "Show purchase patterns by location"
• "Predict next purchase probability"

📊 Customer Segments

High Value (15%): $500+ avg spend
Regular (60%): $100-500 avg spend
Occasional (25%): <$100 avg spend

Key Insights:
• 25-35 age group: Highest CLV
• Electronics: Most popular category
• California: Largest market

⚡ Customer Segmentation VBA

Sub SegmentCustomers()
    Dim lastRow As Long
    lastRow = Cells(Rows.Count, 1).End(xlUp).Row
    
    For i = 2 To lastRow
        If Cells(i, 4).Value >= 500 Then
            Cells(i, 8).Value = "High Value"
        ElseIf Cells(i, 4).Value >= 100 Then
            Cells(i, 8).Value = "Regular"
        Else
            Cells(i, 8).Value = "Occasional"
        End If
    Next i
End Sub

🏭 Operations & Supply Chain

Operations

Optimize inventory levels, analyze production efficiency, and identify bottlenecks in your supply chain operations.

📋 Operations Data

Date,Product,Inventory_Level,Production_Rate,Demand,Lead_Time
2024-01-01,Widget A,1500,200,180,7
2024-01-02,Widget B,800,150,160,5
2024-01-03,Widget C,2200,300,280,10

💬 Operations Queries

• "Calculate optimal inventory levels"
• "Which products have the longest lead times?"
• "Show production vs demand trends"
• "Identify potential stockouts"
• "Calculate inventory turnover ratios"

📊 KPI Dashboard

Inventory Turnover: 8.2x annually
Average Lead Time: 7.3 days
Production Efficiency: 94.2%
Stockout Risk: 2 products (Medium)
Optimal Reorder Point: 450 units

🗃️ Inventory Analysis SQL

SELECT 
    Product,
    AVG(Inventory_Level) as avg_inventory,
    AVG(Demand) as avg_demand,
    AVG(Lead_Time) as avg_lead_time,
    CASE 
        WHEN AVG(Inventory_Level) < AVG(Demand) * AVG(Lead_Time) 
        THEN 'Reorder Now'
        ELSE 'Normal'
    END as status
FROM operations_data
GROUP BY Product;

👨‍💼 HR & People Analytics

Human Resources

Analyze employee performance, predict turnover, and optimize workforce planning with comprehensive HR analytics.

📋 Employee Data

Employee_ID,Department,Role,Salary,Performance_Score,Years_Experience,Training_Hours
E001,Engineering,Senior,95000,4.2,5,40
E002,Sales,Manager,85000,4.5,8,25
E003,Marketing,Specialist,65000,3.8,3,35

💬 HR Analytics Queries

• "Analyze salary distribution by department"
• "Predict employee turnover risk"
• "Show correlation between training and performance"
• "Identify high-potential employees"
• "Calculate cost per hire by department"

📊 HR Insights

Turnover Rate: 12.5% (Industry: 15%)
Avg Performance: 4.1/5.0
Training ROI: 23% performance boost
High Performers: 18% of workforce
Salary Equity Score: 85/100

⚡ Performance Analysis VBA

Sub AnalyzePerformance()
    Dim ws As Worksheet
    Set ws = ActiveSheet
    
    ' Calculate performance quartiles
    Dim perfRange As Range
    Set perfRange = ws.Range("E:E")
    
    For Each cell In perfRange
        If cell.Value >= 4.5 Then
            cell.Offset(0, 1).Value = "Top Performer"
        ElseIf cell.Value >= 3.5 Then
            cell.Offset(0, 1).Value = "Good Performer"
        Else
            cell.Offset(0, 1).Value = "Needs Improvement"
        End If
    Next cell
End Sub

API Documentation

Integrate Excel Copilot's powerful data analysis capabilities into your applications with our comprehensive REST API.

Authentication

Secure your API requests with API key authentication

POST /api/v1/auth/token

Obtain an access token for API authentication. Include this token in the Authorization header for all subsequent requests.

Request Headers

Header	Type	Required	Description
Content-Type	string	Required	application/json

Request Body

{
  "api_key": "your_api_key_here",
  "api_secret": "your_api_secret_here"
}

Response

{
  "access_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...",
  "token_type": "Bearer",
  "expires_in": 3600,
  "scope": "read write analyze"
}

Data Upload & Processing

Upload and process your data files through the API

POST /api/v1/data/upload

Upload CSV or Excel files for processing and analysis. Returns a dataset ID for subsequent operations.

Request Headers

Header	Type	Required	Description
Authorization	string	Required	Bearer {access_token}
Content-Type	string	Required	multipart/form-data

Form Parameters

Parameter	Type	Required	Description
file	file	Required	CSV or Excel file (.csv, .xlsx, .xls)
dataset_name	string	Optional	Custom name for the dataset
auto_analyze	boolean	Optional	Run automatic analysis (default: true)

Response

{
  "dataset_id": "ds_1234567890",
  "filename": "sales_data.csv",
  "rows": 1500,
  "columns": 8,
  "size_bytes": 125000,
  "status": "processed",
  "created_at": "2024-01-15T10:30:00Z",
  "preview": [
    {
      "Date": "2024-01-01",
      "Region": "North",
      "Sales": 15000
    }
  ]
}

GET /api/v1/data/{dataset_id}

Retrieve dataset information and metadata by dataset ID.

Path Parameters

Parameter	Type	Required	Description
dataset_id	string	Required	Unique dataset identifier

Query Parameters

Parameter	Type	Required	Description
include_data	boolean	Optional	Include raw data in response (default: false)
limit	integer	Optional	Limit number of rows returned (max: 1000)

AI Analysis & Insights

Generate insights and answer questions about your data

POST /api/v1/analyze/query

Submit natural language queries to analyze your data and receive AI-generated insights.

Request Body

{
  "dataset_id": "ds_1234567890",
  "query": "What are the top 5 regions by sales revenue?",
  "output_format": "structured",
  "include_visualization": true
}

Parameters

Parameter	Type	Required	Description
dataset_id	string	Required	Dataset to analyze
query	string	Required	Natural language question
output_format	string	Optional	Response format: "structured" or "narrative"
include_visualization	boolean	Optional	Generate chart data (default: false)

Response

{
  "query_id": "q_987654321",
  "answer": "Based on your sales data, here are the top 5 regions by revenue...",
  "data": [
    {
      "region": "North",
      "total_revenue": 125000,
      "percentage": 28.5
    }
  ],
  "visualization": {
    "type": "bar_chart",
    "config": {...},
    "data": {...}
  },
                    

GET /api/v1/analyze/insights/{dataset_id}

Get automatically generated insights and statistics for a dataset.

Response

{
  "dataset_id": "ds_1234567890",
  "insights": [
    {
      "type": "trend",
      "title": "Revenue Growth Trend",
      "description": "Sales revenue shows consistent 15% monthly growth",
      "confidence": 0.89,
      "data_points": ["2024-01", "2024-02", "2024-03"]
    }
  ],
  "statistics": {
    "total_rows": 1500,
    "numeric_columns": 5,
    "completeness": 96.8,
    "duplicates": 12
  },
  "correlations": [
    {
      "column1": "advertising_spend",
      "column2": "revenue",
      "correlation": 0.78,
      "significance": "high"
    }
  ]
}

Code Generation

Generate VBA, SQL, and Excel formulas automatically

POST /api/v1/code/generate

Generate code in various formats based on natural language requirements.

Request Body

{
  "dataset_id": "ds_1234567890",
  "requirement": "Create a VBA macro to remove duplicate rows",
  "output_language": "vba",
  "include_comments": true
}

Parameters

Parameter	Type	Required	Description
dataset_id	string	Optional	Dataset context for code generation
requirement	string	Required	Natural language description of requirement
output_language	string	Required	"vba", "sql", "excel_formula", "python"
include_comments	boolean	Optional	Add explanatory comments (default: true)

Response

{
  "code_id": "c_456789123",
  "language": "vba",
  "code": "Sub RemoveDuplicates()\\n    ' Remove duplicate rows from active sheet\\n    ActiveSheet.Range(\"A:Z\").RemoveDuplicates Columns:=Array(1, 2), Header:=xlYes\\nEnd Sub",
  "explanation": "This VBA macro removes duplicate rows from the active worksheet...",
  "estimated_execution_time": "< 1 second",
  "dependencies": []
}

POST /api/v1/code/convert

Convert code between different formats (Excel formulas ↔ SQL ↔ VBA).

Request Body

{
  "source_code": "=VLOOKUP(A2, Table1, 2, FALSE)",
  "source_language": "excel_formula",
  "target_language": "sql",
  "context": "Customer lookup table"
}

Response

{
  "converted_code": "SELECT t2.value FROM table1 t2 WHERE t2.key = ?",
  "explanation": "Converted Excel VLOOKUP to SQL JOIN query",
  "conversion_notes": [
    "Replace '?' with actual lookup value",
    "Adjust table and column names as needed"
  ]
}

Visualization & Charts

Create beautiful charts and visualizations from your data

POST /api/v1/charts/create

Generate chart configurations based on data and visualization requirements.

Request Body

{
  "dataset_id": "ds_1234567890",
  "chart_type": "bar",
  "x_column": "region",
  "y_column": "revenue",
  "title": "Revenue by Region",
  "aggregation": "sum",
  "filters": {
    "date": {
      "start": "2024-01-01",
      "end": "2024-12-31"
    }
  }
}

Parameters

Parameter	Type	Required	Description
dataset_id	string	Required	Source dataset for chart
chart_type	string	Required	"bar", "line", "pie", "scatter", "heatmap"
x_column	string	Required	Column for X-axis
y_column	string	Required	Column for Y-axis
aggregation	string	Optional	"sum", "avg", "count", "min", "max"

Response

{
  "chart_id": "ch_789123456",
  "chart_config": {
    "type": "bar",
    "data": {
      "labels": ["North", "South", "East", "West"],
      "datasets": [{
        "label": "Revenue",
        "data": [125000, 98000, 156000, 87000],
        "backgroundColor": ["#2563eb", "#8b5cf6", "#10b981", "#f59e0b"]
      }]
    },
    "options": {
      "responsive": true,
      "plugins": {
        "title": {
          "display": true,
          "text": "Revenue by Region"
        }
      }
    }
  },
  "export_urls": {
    "png": "https://api.excelcopilot.com/charts/ch_789123456.png",
    "svg": "https://api.excelcopilot.com/charts/ch_789123456.svg",
    "pdf": "https://api.excelcopilot.com/charts/ch_789123456.pdf"
  }
}

Data Export

Export processed data and analysis results

GET /api/v1/export/{dataset_id}

Export dataset in various formats with optional filtering and processing.

Query Parameters

Parameter	Type	Required	Description
format	string	Required	"csv", "xlsx", "json", "pdf"
include_analysis	boolean	Optional	Include insights and statistics
columns	string[]	Optional	Specific columns to export
filters	object	Optional	JSON filter conditions

Response (Success)

{
  "export_id": "ex_321654987",
  "download_url": "https://api.excelcopilot.com/downloads/ex_321654987",
  "filename": "sales_data_processed.xlsx",
  "size_bytes": 145000,
  "expires_at": "2024-01-16T10:30:00Z",
  "format": "xlsx"
}

SDK Examples

Code examples for popular programming languages

🐍

Python SDK

Complete example using the Python SDK for data upload and analysis.

import excelcopilot

# Initialize client
client = excelcopilot.Client(api_key="your_api_key")

# Upload dataset
dataset = client.upload_file("sales_data.csv")
print(f"Dataset ID: {dataset.id}")

# Analyze data
result = client.analyze(
    dataset_id=dataset.id,
    query="What are the top performing regions?"
)

print(result.answer)
print(result.data)

# Generate chart
chart = client.create_chart(
    dataset_id=dataset.id,
    chart_type="bar",
    x_column="region",
    y_column="revenue"
)

# Export results
export_url = client.export(
    dataset_id=dataset.id,
    format="xlsx",
    include_analysis=True
)

📜

JavaScript SDK

Node.js example for server-side integration and automation.

const ExcelCopilot = require('excel-copilot-sdk');

// Initialize client
const client = new ExcelCopilot({
  apiKey: process.env.EXCEL_COPILOT_API_KEY
});

async function analyzeData() {
  try {
    // Upload file
    const dataset = await client.uploadFile('data.csv');
    
    // Get insights
    const insights = await client.getInsights(dataset.id);
    console.log('Insights:', insights);
    
    // Generate code
    const code = await client.generateCode({
      requirement: 'Create pivot table for sales by region',
      language: 'vba'
    });
    
    console.log('Generated VBA:', code.code);
    
  } catch (error) {
    console.error('Error:', error.message);
  }
}

analyzeData();

⚡

cURL Examples

Direct HTTP requests for testing and integration.

# Upload file
curl -X POST "https://api.excelcopilot.com/v1/data/upload" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -F "file=@sales_data.csv" \
  -F "dataset_name=Sales Analysis"

# Analyze data
curl -X POST "https://api.excelcopilot.com/v1/analyze/query" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "dataset_id": "ds_1234567890",
    "query": "Show monthly sales trends",
    "include_visualization": true
  }'

# Generate VBA code
curl -X POST "https://api.excelcopilot.com/v1/code/generate" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "requirement": "Remove blank rows from sheet",
    "output_language": "vba"
  }'

Rate Limits & Error Handling

Important information about API limits and error responses

⏱️

Rate Limits

API usage limits to ensure fair access and optimal performance.

Plan	Requests/Hour	Upload Size	Datasets
Free	100	10 MB	5
Pro	1,000	100 MB	50
Enterprise	10,000	1 GB	Unlimited

🚨

Error Responses

Standard error format and common error codes.

{
  "error": {
    "code": "INVALID_DATASET",
    "message": "Dataset not found or access denied",
    "details": {
      "dataset_id": "ds_invalid",
      "suggestion": "Check dataset ID and permissions"
    },
    "request_id": "req_123456789"
  }
}

Common Error Codes

• 401 UNAUTHORIZED - Invalid or expired token
• 403 FORBIDDEN - Insufficient permissions
• 429 RATE_LIMIT - Too many requests
• 413 FILE_TOO_LARGE - Upload size exceeded

🔐

Security Best Practices

Keep your integrations secure and compliant.

• Store API keys securely (environment variables)
• Use HTTPS for all API requests
• Implement proper error handling
• Rotate API keys regularly
• Validate file uploads before processing
• Monitor API usage and logs
• Follow data privacy regulations

API Support

Resources and support for API integration

📞

Developer Support

Get help with API integration and troubleshooting from our developer team.

📧 Contact Support

💬

Community Forum

Join our developer community to share ideas, ask questions, and get peer support.

📊 View Status 💬 Join Forum

🔧

API Status

Check real-time API status, uptime statistics, and planned maintenance windows.

📊 View Status