Every time a restaurant runs a bill through its system, it is not merely recording a sale. It is adding a new line to a digital history that reveals something about the business: When does it sell? How much does it sell? Are sales recurring? Which branches are strongest? And how does demand change over time?

For years, the value of this data was clear to restaurant owners; it helped them understand their business. But Foodics’ announcement of Capital 2.0 at the Money20/20 Middle East conference in Riyadh proposes a more ambitious use for it: turning the operating data itself into part of the financing decision.

The company says the new product uses a restaurant’s operating and financial performance, along with an artificial intelligence layer and a credit-scoring system, to automatically identify businesses eligible for financing. Eligible restaurants can choose their terms and complete the process digitally, while Foodics says approved financing can be disbursed within four hours. The announced financing range starts at around SAR 20,000 for working capital and reaches SAR 2 million or more for expansion, with a stated goal of injecting SAR 375 million during the first year. Repayment is made through small deductions from daily settlements.

On the surface, this is a story about a new financial product. At its core, however, it is a story about the value of data.

The Cashier That Knows the Restaurant

Foodics began in 2014 as a restaurant technology company and later expanded into payments and financial services. By June 2026, the company said its platform served more than 40,000 branches and had processed more than six billion orders since its founding. That same month, it completed the acquisition of Greek company Norma, which specializes in data intelligence and artificial intelligence for restaurants and hospitality, and integrated its business intelligence tools and analytical assistant into its platform.

The data generated by restaurant systems is not limited to total sales. Foodics’ own products provide reports on sales by period, location, and channel; order counts and average order value; payment methods; best-selling items; and operational information related to inventory, branches, and demand patterns.

This is where the real transformation begins.

When a company provides a system that a restaurant uses every day, its relationship with that restaurant differs from that of a lender seeing it for the first time when it submits a loan application. The platform does not merely receive a file about the business; depending on the products the customer uses and the data they authorize, it may possess an ongoing record of a substantial portion of its activity.

The data that helped the restaurant identify its best-selling dish yesterday may help answer a completely different financial question today:

Can this restaurant handle new financing?

What does Foodics know about the restaurant that a traditional lender does not?

Depending on the products used, a restaurant operating platform can see sales and their frequency, differences between branches, average order value, operating hours, sales channels, payment methods, movements in certain inventory items, and demand patterns over time.

This is not a list that Foodics has said it uses in its entirety in the Capital 2.0 model; the company has not published the scoring algorithm’s detailed variables. But it illustrates the type of operating data a restaurant management platform can produce and why it may have credit value.

An Old Problem Called “Information Asymmetry”

One of the most difficult aspects of lending to small businesses is that the owner knows the company better than the lender does. In economics, this problem is called information asymmetry: one party possesses information that the other party does not possess to the same degree.

That is why financial institutions require financial statements, bank records, credit data, documents, collateral, and more—not because they enjoy paperwork, but because they are trying to reduce the unknown.

Technology is changing the size of that unknown.

The World Bank notes that purchase, sales, order, inventory, and payment records can serve as “alternative data” to help assess a borrower’s ability to repay. The Organisation for Economic Co-operation and Development likewise argues that transaction data and artificial intelligence may reduce the cost of credit underwriting, speed up decisions, and help address part of the information gap facing small businesses.

This gives the marketing phrase Foodics uses—“the financing that finds you”—a broader economic meaning.

In the conventional model, the business owner starts by searching for money. In a data-driven model, the platform can periodically assess its customer base, identify businesses that meet the financing requirements, and then offer them capital.

In other words, data moves from describing the past to helping allocate capital for the future.

Foodics Is Not the First to Discover the Formula

This model has important precedents around the world.

Toast, a U.S. restaurant technology company, offers loans of up to $300,000 per location and says eligibility factors include the volume of payments processed through Toast and the length of time the system has been used. Repayment is made automatically as a percentage of daily card sales, so the repayment amount decreases when sales fall and increases when they improve.

Square, a subsidiary of Block, automatically reviews merchant accounts to determine eligibility, using payment-processing volume, account history, and transaction frequency among its factors. Its currently advertised offers range from $100 to $500,000, with repayment tied to a fixed percentage of daily sales.

Shopify applies a similar logic. The platform says its model assesses eligibility automatically and that sales performance—including sales volume, the frequency of selling days, and the number of orders—is among the key factors in determining merchant eligibility and the size of the financing offer.

The common thread is clear: a company begins by selling technology, business activity flows through its platform, knowledge accumulates, and that knowledge then becomes a financial product.

This is one form of embedded finance: making financial services part of the platform through which a merchant runs its business, rather than starting the journey with a separate financial institution.

Why Might Restaurant Data Be Different?

Foodics’ potential advantage is not the data alone, but its sector specialization.

Restaurants have a different economics: sales that vary by hour, day, and season; quickly perishable inventory; food and labor costs; major differences between branches; and margins that can be rapidly affected by rising input costs or declining order volumes.

That is why sales do not always carry the same meaning.

A restaurant generating SAR 1 million in sales is not necessarily financially stronger than one generating SAR 800,000. The questions are: What is the margin? How stable is it? How seasonal is it? Is it growing? How are the branches performing? And how much cash does operations consume?

We do not yet know the details of the model Foodics uses or the weights assigned to its variables, so it would be wrong to claim that the company answers all these questions.

But owning a specialized platform within the restaurant sector theoretically gives it an opportunity to build contextual knowledge that does not come from the number alone, but from comparing the number with the business’s history and with similar businesses.

This is where data becomes more valuable than a mere sales spreadsheet.

Has Foodics Become an “Investor” in Successful Restaurants?

Not in the legal sense.

An equity investor buys ownership and shares in the company’s upside and downside. Capital 2.0, according to the published information, is a financing product, not an investment in restaurant equity.

But there is a narrower economic meaning worth noting: Foodics is moving toward becoming a financial partner in a restaurant’s growth.

CEO Ahmed Al-Zaini said at the announcement that the goal is to turn performance data from a tool for understanding the business into financial value that helps the restaurant grow. This may be the best way to interpret the project.

The shift, then, is not from a “cashier company” to an “investor in restaurants,” but from a software provider to a platform that combines operations, payments, analytics, and access to capital.

That is a more economically powerful ecosystem.

The restaurant uses the system, generating data. The data helps assess the business. The eligible business receives capital. The capital may finance inventory, equipment, or a new branch. If the expansion succeeds, more transactions flow through the platform, generating more data.

It is a cycle connecting data, capital, and growth.

But Data Does Not Eliminate Risk

An algorithm’s ability to make a quick decision does not mean the decision will always be better.

The Organisation for Economic Co-operation and Development warns that alternative scoring models using data and artificial intelligence, despite their ability to improve speed and prediction, also raise issues involving bias, privacy, and the explainability of decisions. In addition, the performance of some models across long credit cycles and severe recessions still requires broader testing.

There is another commercial question: What happens to the restaurant if the same platform manages its operations, payments, analytics, and access to financing? Each additional service increases the value of the ecosystem, but it may also increase the customer’s dependence on it.

Then there is an issue that data alone cannot answer: the cost of capital.

Speed of access to money is not enough to judge the quality of financing. Restaurant owners need to know the total cost, repayment terms, available alternatives, and the impact of daily deductions on liquidity.

The model’s true quality will emerge over time, when it becomes possible to measure defaults, the quality of the businesses financed, and their ability to grow after receiving financing.

The Real Asset Is Not Data—It Is What You Can Do With It

It is common to describe data as the “new oil,” but the comparison is misleading if taken literally.

Accumulated data without quality, context, or the ability to inform decisions is not necessarily a treasure.

Value emerges when data becomes a better decision.

That is precisely what makes Capital 2.0 an experiment worth watching. The question is no longer simply how many restaurants use Foodics or how many orders pass through the system, but what the company can build on top of that daily relationship.

Years ago, the point of sale knew how much the customer paid.

Today, the platform can know how the restaurant operates.

The next stage, which Foodics has now begun testing, is more ambitious:

to use what it knows to decide which restaurants can obtain the capital needed for their next step.

If this model succeeds, the greatest value of a point-of-sale system will not lie in recording what happened at the restaurant table.

It will lie in its ability to read what that restaurant may be capable of doing next.