How to Connect Linkup to Excel (4 Methods Compared)

The Problem With Getting Workbook Data In and Out of Linkup

You have an Excel workbook full of data — company names, blog topics, journalist contacts, market segments, competitor URLs. You need Linkup to search the web for each one and write the answers back, in a way that doesn't require you to run the API manually for every row.

Linkup is good at returning structured, sourced answers to natural language queries against live web content. But getting it to act on a column of 40 rows and write results into the workbook beside them is a different problem entirely. The default flow is: export to CSV, call the API for each row manually, paste results back in, re-import.

Below are the four common ways teams handle this. Only the last one scales.

Method 1: Manual CSV Export and Re-Import

The default. Export the workbook column as a CSV, run Linkup lookups against each value one at a time, collect the results in a text editor, paste them back into the right column, and save. For Excel users, copy-paste often gives way to the CSV export pattern because the data tends to live in more structured workbooks where direct editing is riskier.

For a one-time lookup on a handful of rows, this is manageable. You get your answer, you move on.

But Linkup's value is in bulk enrichment — running the same research query across every row. The moment you have 40 blog topics or 60 company names that need sourced summaries, the export-lookup-reimport cycle becomes the majority of your afternoon. And the next time you refresh the workbook, you start the whole sequence again.

Method 2: Power Automate

Power Automate has Linkup connector options, and you can wire up a flow that triggers on a new Excel row, calls Linkup, and writes the response back into the workbook.

Before you dig in — do you know what a connector action is? A dynamic expression? A response schema? If those don't mean anything yet, jump to Method 3 or 4. This path requires all of that before you see a single result in your workbook.

If you're still here: the flow works. You pick the Excel trigger, configure the Linkup action with the right input, parse the response, and map the output field back into the correct column. When a new row is added, the flow fires and writes the result. That part is real.

The structure, though, has limits.

A row-trigger flow is not a bulk enrichment tool.

If you have 50 existing rows that need Linkup summaries today, the flow covers new rows going forward. Getting those 50 existing rows processed means either triggering manually or building a separate batch run — which is a different flow with a different trigger.

And row-by-row processing means 50 API calls, 50 flow runs, and a run history that becomes hard to debug when row 23 returns a different shape and the rest write partial data silently.

You probably just need the funding data or the competitive summaries for the companies already in column A. You probably have no idea how to write a Power Automate expression that parses a nested JSON response — and that's not a gap in your skills, it's just not what you were hired to do. So you either figure it out over a long afternoon or you ask whoever on your team knows Power Automate and you wait.

Method 3: The Previous Generation — Connector Add-Ons

Until recently, the best option for repeatable workbook ↔ API workflows was a category of add-ons that let you manually configure column mappings and save query templates. You picked your range, tagged the input field, mapped the output columns, and ran it.

That was a genuine improvement over doing it row by row. The config was reusable, the output was consistent, the team wasn't reinventing the format each time.

But the thinking was still yours. You wrote the query template. You mapped which column held the input. You set the filter parameters. You decided what to do when a row came back empty. The tool moved data through, but you were the logic layer. And if column A got renamed or a new research question came up, you rebuilt the config.

This is the previous generation. It worked, but it put the operator in the middle of everything.

The Easy Way: Using SheetXAI in Excel

There is a different way entirely. SheetXAI is an AI agent that lives inside your Excel workbook. It reads the workbook, understands what you're looking at, and through its built-in Linkup integration it can search the web for each row and write the results back — for you. No query templates, no column mapping config, no running the API by hand. You just ask.

Example 1: Enrich a column of topics with sourced summaries

For each topic in column A, search the web using Linkup and write a one-sentence sourced summary of the current landscape into column B, with the top source URL in column C.

SheetXAI runs a Linkup search for each row, extracts the summary and the primary citation, and writes them into columns B and C. The whole workbook is processed in a single pass.

Example 2: Look up funding data for a list of companies

For each company name in column A, use Linkup to search for their latest funding round and write the amount, date, and lead investor into columns B, C, and D.

The pattern: instead of researching each company separately and then organizing the output into columns, you describe the full enrichment job in one prompt. SheetXAI handles the per-row execution and the field placement inline.

Try It

Get the 7-day free trial of SheetXAI and open any Excel workbook with a column of topics, companies, or URLs, then ask it to enrich the workbook using Linkup. The Linkup integration is included in every SheetXAI plan.