Developers, meet the spreadsheet engine you didn’t know you needed

Spreadsheets are everywhere — whether you’re calculating budgets, managing workflows, or running complex financial models. But what if you could integrate those spreadsheets seamlessly into your applications? Enter GRID’s REST API, the headless spreadsheet engine you didn’t know you needed.

GRID is a spreadsheet technology company. We’ve conducted a lot of research to back up the following claim: Our spreadsheet engine is the most advanced independent spreadsheet engine out there, based on speed, features and functions we support. We’re fully compatible with Excel and Google Sheets, and for the last couple of years our engine has been powering our product Calculator Studio.

Recently, we’ve been developing a new product that makes it possible to turn your spreadsheets into web services, making use of our engine behind the scenes. That means, users are now able to integrate spreadsheet calculations to their own applications.

How does it work?

Here’s a 2 minute video that shows exactly how to turn your spreadsheets into REST APIs. It’s simple:

1. Go to grid.is

2. Sign up

3. Upload your spreadsheet from your computer or connect it to GRID through Google Drive. By connecting the spreadsheet through a live connection, changes to your Google Sheet will be reflected immediately in GRID.

4. By adding your spreadsheet to GRID, you’ve already got a web service you can program against.

Note: We’re currently working on making it possible for developers to upload spreadsheets through our API directly. That means they only need to sign up once, get an API key, and do the rest through our API.

We have thorough documentation about our REST API for developers and we’re publishing a TypeScript/JavaScript SDK next week to provide convenient and type-safe access to our API — stay tuned!

How do you program against a spreadsheet?

Let’s take a simple example. In the picture below, you’ll see a simple pricing model spreadsheet.

Example API request and its response based on a simple pricing spreadsheet model.

The pricing model calculates a licence fee for a demo SaaS company. The price is based on the number of seats needed and if support should be included or not. In the black box to the left, you’ll see how a request might look to the endpoint of this particular spreadsheet, where we’re updating the values in B2 and B3. The box to the right shows how the response looks from the API. There’s one important thing to point out here: We never change the original spreadsheet data, the updates made in a request are temporary — so there’s no need to worry about altering your spreadsheet!

If you’re curious to try this out, we have documentation where you can try out queries against your spreadsheet in our API environment. When you’ve added a spreadsheet, you can play around with requests to your own spreadsheet. We initially suggest a query based on what we know about your spreadsheet, but you can also write your own queries to get a feeling for the responses.

Who needs a spreadsheet engine?

It’s common that companies underestimate how much they use spreadsheets. The following quote is from an article written by the founder and CEO of GRID a couple of years back:

The reality is that every organization has a “spreadsheet fabric”, a growing collection of workbooks ranging from standalone one-off working documents to a mesh of interconnected workbooks that run critical processes for years — even decades.

This probably sounds familiar. The reality is that spreadsheets are an integral part of companies, often containing critical business logic essential to their operations. If you’re a developer and you’ve ever been asked to rewrite some business logic in code, a spreadsheet engine might be your solution: It enables you to reuse data and logic already stored in spreadsheets.

With the rise of generative AI and next-gen productivity tools, companies are looking to automate their workflows. Why spend time manually updating an Excel file to calculate something, when you can automate the process? That’s where we come in — now you can integrate your spreadsheet calculations to your workflow or application. Let’s explore some use cases!

Use cases

Custom application

Let’s take an insurance company as an example. The company calculates business insurance premiums using multiple spreadsheets for each quote they offer. They’ve been looking to automate these calculations, to save time and increase efficiency of their team that spends most of their day manually searching and updating spreadsheets. They want to be able to call around 100 spreadsheets each time they need to calculate a premium — and the first thing they thought of was writing the rules from scratch in a custom application. If you even begin to scope the project of re-coding 100 spreadsheets in another language, you arrive at big numbers fast. Luckily, this is a real life example and this company found GRID. By turning their spreadsheets to web services, they can integrate their pre-existing calculations to their solution in a much quicker way than writing them from scratch again.

Calculations that live in spreadsheets and are used every day in critical business processes are a common challenge across more sectors than just insurance. Finance, banking, healthcare, logistics, manufacturing, retail, energy, and government agencies all rely on complex spreadsheet-based calculations to drive decisions, manage risk, and ensure compliance.

Workflow automation

Automation software like Zapier or Make can be a game changer for business users. They enable non-devs to automate their workflows and often, these workflows involve spreadsheets. By turning spreadsheets to a web service, you can integrate your calculations to your workflow with no-code tools like the ones mentioned above.

A webhook in Zapier used to integrate spreadsheet calculations into a workflow.

AI

GRID’s spreadsheet engine is uniquely tailored to support AI use cases. We believe a spreadsheet engine is the missing piece in the LLM puzzle because Large Language Models (LLMs) can’t perform reliable spreadsheet calculations without one. We’ve made it possible to integrate your spreadsheets to custom GPTs in ChatGPT and we offer a step-by-step guide in our product to make it as easy as possible. We’ve also integrated our API with Claude for Desktop using a local MCP server.

GRID bridges the gap between spreadsheets and LLMs.

Our engine defines labels in a spreadsheet to make it possible for the LLM to understand the spreadsheet — basically, we don’t call a cell simply B2 but also “Number, Seats” to teach the LLM what cell to update when a user wants to update the number of seats (example from the pricing model earlier). This is why our engine is optimized for AI and by turning spreadsheets into web services, users can now integrate them to their AI solutions.

An example of GRID’s label identification.

If you’re building an assistant using OpenAI’s Assistant API for example, assistants support function calls. That unlocks the possibility to integrate reliable spreadsheet calculations to your assistant: Imagine a chatbot on a website that acts as a sales representative and generates customized quotes for your leads, or an internal financial planning and budgeting assistant that has access to several spreadsheets to assist financial teams creating budgets, track expenses and more.

Another big advantage of defining labels in a spreadsheet is that it converts the data into “human language". LLMs are exceptional at language, so they could use an overview of labels to search for specific things in a pool of different spreadsheets (think about the spreadsheet fabric mentioned above). Imagine if you could ask an internal AI agent “What will our cash flow look like in Q2 2026 if our revenue grows by 5%?” and the agent would know where to find the answers in different spreadsheets because it can search for labels and their synonyms.

Templates

Last but not least, we have an endpoint in our API that’s called export. It makes it possible to apply changes to your spreadsheet and export it with the changes. Normally when changes are made to a spreadsheet through our API, we never save the changes because we treat them as scenarios — but when the export endpoint is used, we open up the possibility for users to keep the changes.

Imagine a financial advisor that needs to do a portfolio expense comparison based on several statements in a PDF form. LLMs are great at reading PDFs and extracting values from them. The advisor could have a template spreadsheet that contains the relevant fields that need to be filled out, and then make use of a LLM to extract the values and fill in the template. Once completed, the advisor could export the spreadsheet, making it easy to review and present to clients. This workflow would make the advisor’s work much more efficient and scalable.

Leverage your existing calculations

The wide range of use cases mentioned above are just a few examples — the beauty of APIs is that you can ‘plug-and-play’ your spreadsheets wherever needed. Chances are, a spreadsheet engine wasn’t the first solution that came to mind when tackling your problem. But maybe it should be. Instead of reinventing the wheel, why not leverage the business logic you’ve already built?The examples I’ve mentioned in this article are mostly from conversations we’ve had with our early customers. If you think you have a use case for GRID’s REST API, we’d love to hear from you.

Spreadsheets run the world and now your applications can run spreadsheets. Sign up and turn your first spreadsheet into an API.