What does the sector's data say, and why did we build it this way?

Classic POS pricing rests on a fixed logic: a monthly fee per device, an extra fee per user, a package per module. This structure shrinks relative to revenue in a large chain, but hits profit directly in a single branch. In other words, the sector's most fragile segment pays the proportionally highest price for software.

Add to that the "locked module" practice. When functions considered basic today — the kitchen display, QR menu or detailed reporting — are held back for higher tiers, the business either operates incomplete or squeezes its profit margin to pay.

Sector data shows an awareness of this pressure: operators prioritise POS and back-office investment because that's where the loss occurs. The problem isn't a lack of need for technology; it's being unable to afford the technology you need.

Most restaurants aren't strangers to technology; on the contrary, they use too many separate tools. The problem isn't the number of tools, it's that the tools don't see each other. When sales data is disconnected from stock, stock from cost, and cost from finance, every decision is made with a delay.

This disconnect produces two costs. The first is visible: entering the same data twice, the hours spent on reconciliation. The second is invisible but more expensive: profitability only becoming clear once the month closes — meaning the intervention window is missed.

Sector research flags operational execution and system fragmentation as leading barriers; businesses that add and integrate technology report measurable efficiency gains. The critical distinction isn't "using software," it's owning a single source of truth.

The traditional credit process looks at a business's past: balance sheet, collateral, credit history. For businesses like restaurants — with a fast cash cycle but a light asset structure — this measurement systematically produces incomplete results. Even with strong revenue, the absence of collateral closes the door.

Embedded finance reverses this measurement: a business's daily transaction data — sales movements, collection rhythm, cancellation rate, seasonality — becomes a direct input to the evaluation. Market analyses point to exactly this as the reason for growth: because SMEs struggle to access bank credit due to long approval times, collateral requirements and weak credit history, models based on platform data come to the fore.

The critical nuance is this: embedded finance doesn't mean the software company becomes a bank. Software carries the data and the experience; the licensed institution makes the credit decision and the disbursement. In a properly built model the user sees a single flow, while the separation of authority is preserved in the background.

In Türkiye, SMEs form the backbone of the economy in terms of business count and employment. Yet access to finance is regularly cited among the biggest barriers; public reports also treat access to finance as a separate problem heading.

A recent, prominent view reframes the problem: the real bottleneck isn't access to credit, it's being unable to make the business's financial structure transparent and manageable for financial institutions — that is, financial visibility. A business that doesn't keep regular records or track its income-expense flow systematically looks risky even with strong revenue.

This finding explains why software and financing need to sit in the same place. Visibility can't be produced afterwards on request; if daily operations themselves are recorded, it forms naturally. In a restaurant, that record starts at the POS.

In chains, profitability is less about a single branch's performance and more about managing deviation. When recipe grammage, portion, waste and purchase price diverge from branch to branch, the central report looks correct while the field cost slips.

Sector data shows cost pressure being offset by pricing: sales grow, but real growth stays limited after inflation adjustment. This means price increases are a solution up to a point; the rest comes from operational discipline.

It's no coincidence that in the same period operators began using AI most in back-office functions like reporting, planning and stock forecasting. The gain comes from seeing the two biggest cost lines — food and staff — earlier.

The sustainability of a free model comes down to a single question: as the number of users grows, does cost grow or does revenue potential? In classic enterprise software every new customer brings setup, training and support cost; that's why the licence becomes mandatory.

We built the cost side differently from the start. Setup was reduced to a wizard the business can run itself; first-level support is handled by AI. Setup, training and dedicated support that require human effort sit in a separate, optional package — whoever wants it takes it, whoever doesn't keeps using it free.

On the revenue side there are two scaling lines: the are8 OS setup that multi-branch chains need, and the embedded finance a business uses when it needs it. Both gain meaning as the business grows. So our revenue comes from the customer's growth, not its hardship — the incentives point the same way.

The embedded lending market advancing with double-digit growth projections shows this model isn't a claim unique to us: finance embedded inside software is accepted as a sustainable revenue architecture for platforms.