TL;DR: Tired of manually analyzing spreadsheets? This tutorial shows how to build an AI-powered Excel summary tool using C#. By combining a .NET-compatible Excel library with GPT, you’ll learn to extract structured data from Excel files and generate intelligent summaries automatically, perfect for developers building smart dashboards, reporting tools, or productivity apps.
Excel is a cornerstone tool for managing structured data, whether it’s sales reports, operational logs, or support dashboards. But as datasets grow, interpreting them quickly becomes a challenge. That’s where AI-powered summarization comes in.
In this blog, you’ll learn how to build a C# console application that reads Excel files using the Syncfusion .NET Excel library and generates intelligent summaries using OpenAI GPT.
Prerequisites
To follow along with this guide, make sure you have the following:
- .NET SDK 8.0 or later.
- Syncfusion XlsIO and OpenAI NuGet package.
- An OpenAI API key
- Visual Studio or VS Code.
Step-by-step implementation
Step 1: Create a .NET Core Console app
Start by creating a new .NET Core Console application in Visual Studio.
Step 2: Install the NuGet packages
Then, install the latest Syncfusion.XlsIO.NET.Core and OpenAI NuGet packages in your application.
Step 3. Read Excel data
To extract data from an Excel file, we use Syncfusion’s XlsIO library. The goal is to convert the Excel sheet into plain text that can be sent to OpenAI.
Key points:
- Open the Excel file using ExcelEngine.
- Access the first worksheet.
- Loop through the used range (non-empty cells).
- Collect cell values row by row into a string.
public static string ExtractDataAsText(string filePath)
{
// Initialize the Excel engine
using (ExcelEngine excelEngine = new ExcelEngine())
{
// Get the Excel application instance
IApplication application = excelEngine.Excel;
// Set the default version to Xlsx
application.DefaultVersion = ExcelVersion.Xlsx;
// Open the Excel file
IWorkbook workbook = application.Workbooks.Open(filePath);
// Create a StringBuilder to hold the extracted data
StringBuilder sb = new StringBuilder();
// Iterate through each worksheet and extract data
foreach (IWorksheet worksheet in workbook.Worksheets)
{
// Append the worksheet name
sb.AppendLine($"Worksheet: {worksheet.Name}");
// Get the used range of the worksheet
IRange usedRange = worksheet.UsedRange;
for (int row = usedRange.Row; row <= usedRange.LastRow; row++)
{
for (int col = usedRange.Column; col <= usedRange.LastColumn; col++)
{
// Get the cell value and append it to the StringBuilder
sb.Append(worksheet.GetCellValue(row, col, true) + "\t");
}
sb.AppendLine();
}
sb.AppendLine();
}
return sb.ToString();
}
}
This gives you a clean, tab-separated text version of your Excel data.
Step 4. Send data to OpenAI GPT
Once you have the Excel data as text, you send it to OpenAI GPT to get a summary.
Key points:
- Use the OpenAI NuGet package.
- Set GPT-4 as the model.
- Provide a system role (e.g., “You are a data analyst”).
- Pass the Excel data as a user message.
public static async Task<string> SummarizeData(string inputText, string apiKey)
{
// Initialize the OpenAI client with the provided API key and model
ChatClient client = new(model: "gpt-5", apiKey);
// Create a chat completion request to summarize the Excel data
ChatCompletion completion = await client.CompleteChatAsync(
new SystemChatMessage("You are a helpful assistant that summarizes Excel data."),
new UserChatMessage($"Summarize the following Excel data:\n{inputText}")
);
// Check if the completion has content and return the summary
return completion.Content[0].Text?.Trim() ?? string.Empty;
}
This sends the data to GPT and returns a natural-language summary.
Step 5. Get an AI Summary from Excel
You can provide the Excel document and OpenAI API key to generate the summary. The method below provides a ready-to-use AI-powered summary from any Excel file.
static async Task<Main>()
{
string excelFilePath = "File Path"; // Replace with the path to your Excel file
string openAiApiKey = "OpenAI API key"; // Replace with your OpenAI API key
try
{
// Read Excel data
string excelText = ExtractDataAsText(excelFilePath);
Console.WriteLine("Excel data read successfully.");
// Get summary from OpenAI
string summary = await SummarizeData(excelText, openAiApiKey);
Console.WriteLine("Summary from OpenAI:");
Console.WriteLine(summary);
}
catch (Exception ex)
{
Console.WriteLine($"An error occurred: {ex.Message}");
}
}
Use case: Sales data summary
Imagine you’ve created an Excel file containing monthly sales data for 25 regions (January–May). Instead of manually analyzing the data, you want an AI-generated summary that highlights trends, top performers, and anomalies.
Now, use the Syncfusion .NET Excel (XlsIO) library to read the Excel document. The extracted data will appear as shown in the table below.
| Region | Jan | Feb | Mar | Apr | May |
| Region 1 | $118,017 | $174,803 | $94,967 | $180,331 | $128,086 |
| Region 2 | $217,216 | $218,436 | $207,568 | $94,502 | $86,507 |
| Region 3 | $153,489 | $216,328 | $125,298 | $248,655 | $92,567 |
| Region 4 | $145,564 | $139,839 | $183,428 | $134,356 | $194,829 |
| Region 5 | $205,951 | $211,589 | $210,952 | $186,433 | $171,343 |
| Region 6 | $231,253 | $143,369 | $111,216 | $125,112 | $142,429 |
| Region 7 | $228,886 | $201,669 | $166,766 | $105,860 | $186,337 |
| Region 8 | $128,911 | $193,303 | $91,459 | $133,207 | $213,201 |
| Region 9 | $172,230 | $120,484 | $138,772 | $184,620 | $90,024 |
| Region 10 | $116,969 | $243,933 | $85,616 | $243,440 | $229,446 |
| Region 11 | $182,020 | $209,462 | $240,014 | $137,342 | $206,014 |
| Region 12 | $144,680 | $220,656 | $103,530 | $104,883 | $166,219 |
| Region 13 | $171,126 | $166,316 | $219,495 | $129,701 | $234,000 |
| Region 14 | $141,185 | $129,564 | $238,925 | $120,482 | $229,810 |
| Region 15 | $222,072 | $85,435 | $99,283 | $220,020 | $199,344 |
| Region 16 | $115,554 | $235,693 | $227,168 | $94,344 | $212,828 |
| Region 17 | $91,142 | $204,142 | $219,468 | $203,400 | $137,496 |
| Region 18 | $238,539 | $165,822 | $81,183 | $248,280 | $144,542 |
| Region 19 | $230,822 | $119,669 | $205,471 | $196,795 | $120,801 |
| Region 20 | $221,408 | $80,048 | $229,625 | $107,344 | $104,077 |
| Region 21 | $94,270 | $142,935 | $159,161 | $166,505 | $234,993 |
| Region 22 | $244,581 | $218,727 | $212,618 | $120,062 | $99,059 |
| Region 23 | $189,859 | $128,230 | $133,847 | $179,155 | $93,149 |
| Region 24 | $182,197 | $165,389 | $160,310 | $173,761 | $95,213 |
| Region 25 | $233,747 | $146,593 | $107,373 | $135,004 | $125,395 |
By applying the remaining steps, converting the data to plain text, and sending it to GPT, you instantly receive a natural-language summary that highlights:
- Total and average sales across months and regions
- Monthly performance trends and shifts
- Top and bottom-performing regions
- Volatility indicators and key takeaways
| Category | Metric/detail | Value/notes |
| Overall totals | Total (Jan–May) | $20,668,938 |
| Avg per month (all regions) | $4,133,788 | |
| Avg per region (YTD) | $826,758 | |
| Monthly trend | Jan $4.422M → Feb $4.282M → Mar $4.054M → Apr $3.974M → May $3.938M | |
| % Change Jan to May | ↓ 10.9% | |
| Monthly leaders & laggards | January – High / Low | Region 22 $244,581 / Region 17 $91,142 |
| February – High / Low | Region 10 $243,933 / Region 20 $80,048 | |
| March – High / Low | Region 11 $240,014 / Region 18 $81,183 | |
| April – High / Low | Region 3 $248,655 / Region 16 $94,344 | |
| May – High / Low | Region 21 $234,993 / Region 2 $86,507 | |
| Highest single month overall | Region 3 in April ($248,655) | |
| Lowest single month overall | Region 20 in February ($80,048) | |
| Top Regions (YTD) | Top 5 | Region 5 $986,268; Region 11 $974,852; Region 13 $920,638; Region 10 $919,404; Region 22 $895,047 |
| Bottom 5 | Region 1 $696,204; Region 9 $706,130; Region 23 $724,240; Region 12 $739,968; Region 20 $742,502 | |
| Regions above YTD avg | 12 of 25 | |
| Monthly totals & averages | January | $4,421,688 (avg/region $176,868) |
| February | $4,282,434 (avg/region $171,297) | |
| March | $4,053,513 (avg/region $162,141) | |
| April | $3,973,594 (avg/region $158,944) | |
| May | $3,937,709 (avg/region $157,508) | |
| Volatility | Most Volatile Regions | Region 18 ($167,097), Region 10 ($158,317), Region 3 ($156,088) |
| Key takeaways | Highlights | Jan strongest month; steady softening through May |
| Top performers | Region 5 leads YTD; Regions 10, 11, 13, 22 also strong | |
| High variability | Regions 18, 10, and 3 showed biggest swings |
This automated approach transforms raw spreadsheet data into actionable insights, saving hours of manual effort and enabling faster, data-driven decision-making.
For more details on working with Excel files in C#, check out the Essential XLSIO user guide.
GitHub reference
You can download the complete samples from here.
Conclusion
By combining Syncfusion Excel Library with OpenAI GPT, you can transform raw Excel data into actionable insights. Whether you’re building dashboards, reports, or decision-support tools, this integration empowers your applications with intelligent summarization.
Check out the Syncfusion .NET Excel Library documentation to discover additional features like exporting to PDF, working with charts, and using template markers, all with ready-to-use code samples. For hands-on experience, visit our online demos to see the library in action.
If you’re a Syncfusion user, you can download the setup from the license and downloads page. Otherwise, you can download a free 30-day trial.
You can also contact us through our support forum, support portal, or Feedback Portal for queries. We are always happy to assist you!
Related Blogs
- Easily Create Dynamic Charts in Excel Using C#
- Create Excel Table in Just 3 Steps Using C#
- 3 Easy Steps to Add Watermarks to Your Excel Document Using C#
- Seamlessly Import and Export CSV Data in Excel Using C#
This article was originally published at Syncfusion.com.
Top comments (0)