What is Spreadsheet AI?

Spreadsheet AI is a feature of TableFlow that finds and extracts data from Excel files. It uses AI Sheet Selection to navigate multi-sheet Excel workbooks to identify and extract the right data tables. It then analyzes each sheet in a workbook, compares content against your template, and selects the most relevant sheets while ignoring irrelevant tabs like summaries, notes, and calculations.

How does Single-Sheet Selection mode work?

Single-Sheet Selection mode is designed for workbooks where all required data exists on one tab. The AI compares your template (including column names, descriptions, and examples) against every sheet in the workbook and selects the one that best matches. It can also handle duplicates by choosing the cleanest and most complete version of the data.

When should I use Multi-Sheet Selection mode?

Multi-Sheet Selection mode is ideal when your data is spread across multiple tabs in a workbook. For example, if a supplier organizes products by brand with each brand on a separate sheet, this mode will identify all relevant sheets, merge the data into a single table, and ignore irrelevant tabs like summaries or notes.

What challenges does Spreadsheet AI solve with Excel files?

Spreadsheet AI solves common Excel challenges including scattered information across sheets, inconsistent data structures with merged cells and blank rows, split data mixed with summaries, noisy data with calculations and duplicates, and large workbooks with hundreds of thousands of rows that make manual processing impractical.

How does TableFlow handle messy Excel formatting?

After selecting the right sheets, TableFlow uses AI-powered transformation to clean the data. It identifies table boundaries, filters out metadata and noise, detects headers even when they're not in the first row, normalizes complicated layouts, and matches the output to your required template format—delivering clean, import-ready data without manual cleanup.

automation

excel

data-extraction

Spreadsheet AI: Get the Right Data from Any Excel File

Excel files are rarely built for automation. TableFlow's Spreadsheet AI identifies, cleans, and structures data across sheets, ready for any workflow.

Eric Ciminelli

CTO & Co-Founder

Oct 8, 2025•5 min read

Spreadsheet AI: Get the Right Data from Any Excel File

Extracting tables from Excel seems easy enough—it's just rows and columns, right? In practice, most Excel files are built for people, not machines. They come packed with merged cells, nested headers, notes scattered across sheets, inconsistent layouts, and more. What looks simple can become a tedious, error-prone task when you actually try to automate it.

What if you could automatically locate and extract the right data from a complex workbook? With Spreadsheet AI, you can do just that. It uses intelligent sheet selection to navigate multi-sheet Excel files to find and extract the tables you need, even from the messiest of workbooks.

Real-World Excel Files

Data from suppliers, partners, or customers rarely comes in neat grids.

Star Wars Anakin and Padmé meme: 'I sent you the data in Excel' - 'It's one sheet with consistent columns right?' - 'Right?'

We see Excel files often include:

•

Scattered Info: Headers, footers, and notes scattered around the data.

•

Inconsistent Structure: Merged cells, blank rows, and decorative formatting that confuse automation.

•

Split Data: Information spread across multiple sheets mixed with summaries and notes.

•

Noisy Data: Summary rows, duplicates, and calculations.

•

Too Many Rows: Workbooks with hundreds of thousands of rows make manual cleanup, or normal AI transformation impractical.

The challenge: how to reliably extract clean, structured tables from this complexity? Our solution is a two-step process: locate the right data, then extract it cleanly.

Step 1: Sheet Selection

TableFlow template editor showing Multi-Sheet Selection mode dropdown with description: Uses AI to find and select one or more sheets to extract data from

Efficient data extraction from Excel files starts by finding the right sheets. Enabling AI Sheet Selection and choosing the right mode lets you do this automatically. Here are the available options:

Single-Sheet Selection

Perfect for use cases where the data is always on a single tab. TableFlow compares your template (containing columns, descriptions, examples, etc.) to every sheet and selects the one that matches best.

Multi-Sheet Selection

When data is spread across multiple tabs, this mode finds all of the relevant tables and combines them into one result. For instance, a supplier sends a product catalog where each brand is in a different sheet:

NotesBrand ABrand BSummaryPrice Breakdown

Multi-Sheet mode identifies the right sheets or sheet, merges the data, and ignores irrelevant tabs. It can even recognize when a sheet contains correct data that's duplicated in a different format, and choose to ignore that.

Step 2: Table Extraction

After locating the right sheets, the next step is extracting clean, usable data. TableFlow's AI-powered transformation handles:

Table Boundaries

Identifies where the data starts and ends, automatically detecting the first row of actual data and the last meaningful row—skipping headers, footers, and blank space.

Noise Filtering

Excludes metadata, titles, summary rows, and extra text that would clutter your dataset. Only the essential data makes it through.

Header Detection

Finds headers even if they're not in the first row, identifies labels as headers, or even works without headers by inferring structure from the data itself.

Structure Normalization

Converts complicated layouts with merged cells, nested data, and inconsistent formatting into clean, import-ready table formats.

The result? Clean, structured data, ready to use—no manual cleanup required.

See It In Action

This supplier packing list has multiple sheets, merged cells, inconsistent formatting, and breakdown charts (the more you look the worse it gets).

The Result

We ran this file through TableFlow to extract the product data. With Multi-Sheet Selection enabled, it figured out the product data was in the 'Thorne & Wilder' and 'Veloren Wear' tabs and used context to know these were the brand names. It then got to work on cleaning, breaking out, combining, and formatting the data to give us this table as a result:

Brand	Style Num	Color	Size	Quantity	Description
Thorne & Wilder	79178-NB	Navy Blue	XS	24	Fitted Crew Neck
Thorne & Wilder	79178-NB	Navy Blue	S	88	Fitted Crew Neck
Thorne & Wilder	79178-NB	Navy Blue	L	272	Fitted Crew Neck
Thorne & Wilder	79178-NB	Navy Blue	XL	30	Fitted Crew Neck
Thorne & Wilder	79177-EG	Elm Green	S	116	Fitted Crew Neck
Thorne & Wilder	79177-EG	Elm Green	M	40	Fitted Crew Neck
Thorne & Wilder	79177-EG	Elm Green	XL	23	Fitted Crew Neck
Veloren Wear	6291-AB	Beige	XS	40	Active XCell T-Shirt
Veloren Wear	6291-AB	Beige	L	49	Active XCell T-Shirt
Veloren Wear	6291-AB	Beige		21	Active XCell T-Shirt
Veloren Wear	6291-AR	Ruby	M	23	Active XCell T-Shirt
Veloren Wear	6291-AR	Ruby	L	33	Active XCell T-Shirt
Veloren Wear	6291	Ruby	XL	12	Active XCell T-Shirt
Veloren Wear	6291-AG	Grey	XS	42	Active XCell T-Shirt
Veloren Wear	6291-AG	Grey	M	12	Active XCell T-Shirt
Veloren Wear	6291-AG	Grey	L	52	Active XCell T-Shirt
Veloren Wear	6291-AG	Grey	XL	29	Active XCell T-Shirt
Veloren Wear	6291-AC	Copper	S	20	Active XCell T-Shirt
Veloren Wear	6291-AC	Copper	M	21	Active XCell T-Shirt
Veloren Wear	6291-AC	Copper	L	34	Active XCell T-Shirt
Veloren Wear	6291-AC	Copper	XL	25	Active XCell T-Shirt

Traditional Methods vs Spreadsheet AI

The Scenario: A supplier sends a 12-sheet Excel workbook with product data scattered across multiple tabs mixed with summaries and irrelevant data.

❌ Manual Process:

1. Open the workbook and review each sheet
2. Identify which tabs have relevant data
3. Copy data from each relevant sheet
4. Paste into a master spreadsheet
5. Clean up duplicates and formatting
6. Manually remove summary rows and notes
7. Import into your system

⏱️ Time: 15-30+ minutes per workbook

✓ Spreadsheet AI:

1. Upload the Excel file to TableFlow
2. AI automatically identifies relevant sheets
3. Data is extracted and merged
4. Clean, structured output is ready

⏱️ Time: Under 60 seconds

Impact: Process 100+ spreadsheets per month? That's saving 25-50 hours of manual work, time your team can spend on higher-value tasks.

Smarter Data Workflows

Spreadsheet AI eliminates the need for scripts or manual copy-pasting. Define the structure you need, and let the system do the work. Whether your data is buried in one sheet or split across many, it finds, cleans, and delivers it in the format you need.

Key Takeaways

• Real-world Excel files are messy with scattered info, inconsistent structures, and split data
• Single-Sheet Selection automatically finds the most relevant tab in complex workbooks
• Multi-Sheet Selection merges data from multiple tabs while ignoring irrelevant sheets
• AI-powered transformation handles table boundaries, noise filtering, and header detection
• Clean, structured data is delivered in your required format without manual work

In Summary: AI Sheet Selection transforms complex Excel workbook processing by automatically finding the right sheets and extracting clean, structured data. With Single-Sheet and Multi-Sheet modes, it handles everything from simple single-tab files to complex multi-sheet workbooks, delivering import-ready data without manual cleanup or scripting.

Frequently Asked Questions

About Eric Ciminelli

CTO & Co-Founder at TableFlow. Expert in AI/ML systems, distributed computing, and building enterprise-grade document processing solutions.

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Finding the Right Data in Complex Excel Workbooks

TableFlow's AI automatically identifies and extracts relevant data from complex multi-sheet Excel workbooks, skipping templates, archives, and irrelevant tabs.