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Pitch

**Bites **is building a new food-delivery marketplace powered by direct POS integrations and early consumer demand driven by AI agents. POS networks treat bites’ orders as first-party transactions, unlocking instant, nationwide coverage with no commissions, no menu markups, and no sales team. Consumers get the same food from the same restaurants, delivered by the same courier networks —yet typically $10–$15 cheaper.

The company’s distribution model leverages AI Agents like ChatGPT, Claude, etc., which surface restaurant direct ordering powered by Bites and POS integrations as one of the lower-cost options to consumers. After a user orders, receipts and tracking flows guide them back into the Bites app, building a direct, retained customer base without any paid marketing. Each transaction strengthens this flywheel!

To start, the service fulfills deliveries through DoorDash Drive, Uber Direct, and Grubhub Delivery— thereby none of the major networks can block access without handing volume to competitors. As demand density grows, Bites will transition toward a hybrid fleet, pushing delivery costs even lower.

Incumbents cannot follow this model down the price curve. Their reliance on high restaurant commissions and consumer service fees is fundamental to their public-market economics; removing those fees would collapse their margins. This decentralized model, by contrast, is structurally cheaper from day one and becomes increasingly cost-advantaged with scale.

If successful, it won’t simply gain share—it will reveal the true cost of delivery and undermine the “Delivery tax” that underpins legacy marketplace economics. Once consumers experience delivery at its actual price, they won’t return to the old model!

Prose

Imagine beginning with a simple assumption: POS networks will allow us to act as a first-party ordering channel on behalf of every restaurant on their platform. It’s not far-fetched. POS systems already accept orders from numerous white-label providers and inject them straight into the restaurant’s workflow, with payments processed through the restaurant’s own merchant account. In our model, we behave exactly the same way. The restaurant receives a full-price order. The POS sees native payment volume and interchange spread. No one pays a commission. And there is no contract to sign. This one assumption triggers a cascade of consequences that reshape how a food-delivery marketplace can be built!

If every restaurant connected to a POS can be activated without individual sales calls, then we instantly eliminate one of the largest cost centers in the delivery industry: the sales apparatus required to recruit and maintain restaurant supply. DoorDash, Uber Eats, and Grubhub had to spend years building vast teams to negotiate commissions, manage menu accuracy, and maintain merchant relationships. We skip that entirely. Supply acquisition, historically the slowest and most expensive part of these businesses, becomes software. That alone reshapes our cost structure.

Once supply is frictionless, something else becomes possible: restaurants do not need to raise their prices via markups. Today, restaurants routinely inflate prices on delivery apps by 15–25% to offset commissions. Remove the commissions and the markups disappear as well. For the first time, consumers can order online delivery at the true menu prices. This creates a substantial price advantage before our own fee structure is even considered. Restaurants recognize these as full-margin, first-party orders. They keep customer data and maintain complete ownership of the relationship for loyalty, offers and promotions. And psychologically, they treat these orders as “their own” rather than a marketplace transaction. That goodwill becomes a strategic advantage.

A second assumption follows naturally: instead of spending on marketing, we allow large language models like ChatGPT, Claude to introduce us to consumers. LLMs are rapidly becoming competent personal agents capable of completing tasks like “Order dinner from Thai place near me.” These systems have a built-in bias toward transparency and correctness. If one ordering path is dramatically cheaper while delivering the same outcome, the model will surface it. Because we use no menu markups, no commissions, and a simple $1 platform fee, we are almost always the lowest-cost option for the same restaurant and the same courier fleet. The LLM does not have to “prefer” us—its natural logic pushes it to recommend the option that minimizes cost and maximizes clarity.

This leads to a profound implication: We have zero customer acquisition cost. DoorDash and Uber Eats spend billions on promotions, paid search, referral bonuses, and loyalty subsidies. We spend nothing. The LLM introduces us at the moment a user is already prepared to buy. Instead of fighting for attention, we are handed intent on a silver platter. In that moment of recommendation, consumers experience an immediate, meaningful price gap—often more than ten dollars cheaper for an identical meal delivered by the same type of courier.

Now consider what happens after the consumer places an order. This is the moment where engagement spreads quietly into Bites App. The receipt, the order-tracking page, and the notifications all flow from our system. These are the most valuable surfaces in the entire ordering journey. This is where habits form. When the tracking page tells the user they can “reorder directly here next time and always pay the real menu price,” the logic becomes irresistible. The user learns not just that we exist, but that we represent the honest, low-cost path to delivery. With even a single positive experience, they begin to migrate to our app and web channels. We use the LLM for discovery; ownership begins in the receipt.

Once this repeat behavior takes hold, our demand becomes denser in specific neighborhoods. That density unlocks the next phase of the strategy. For fulfillment, we rely not on a single delivery partner but on a multi-homed approach using DoorDash Drive, Uber Direct, and Grubhub Delivery. This means we are not beholden to any single actor. It also creates a competitive tension among them. If one platform decides to disconnect us, the others stand to gain volume, courier utilization, and marginal efficiency. It becomes a prisoner’s dilemma: no one wants to be the first to cede demand to rivals. This dynamic ensures that we have enough runway for demand to grow and consolidate.

As orders accumulate in metros, demand density reaches a point where we can begin to introduce a hybrid of gig drivers and hourly workers in core neighborhoods. This isn’t required initially, but it becomes a powerful economic lever later. Hourly labor in dense zones produces shorter idle times and more efficient routing than gig-only structures. This pushes fulfillment costs down—eventually below the unit economics enjoyed by the major players. At this stage, our structural advantages compound. We began cheaper, and over time we become even cheaper on the operational side.

All of this leads to a final and decisive realization: incumbents cannot follow us down this path. DoorDash and Uber Eats cannot suddenly eliminate commissions, undo menu markups, and shrink their service fees without destroying their contribution margins and violating the expectations of public shareholders. They are locked into their pricing architecture. Even temporary subsidies in specific markets would be transparent to analysts and ultimately unsustainable. Their financial structure traps them in a high-cost operating model, while ours flows naturally toward low cost.

When you combine POS-native restaurant supply, AI-driven discovery, transparent pricing, receipt-driven retention, multi-homed fulfillment, and eventual hybrid delivery labor—all resting on a foundation of radically lower structural costs—it becomes clear that the incumbents are precariously exposed. If this model gains even modest traction, consumers will see a price difference they cannot forget, restaurants will prefer the margin profile, and LLMs will continue steering demand toward the cheapest reliable option. The traditional platforms are not just at risk of losing a few points of share—they are at risk of having their entire pricing model revealed as an unnecessary tax on food delivery.

The “Ah-ha” moment is simple: this isn’t about beating DoorDash or Uber Eats head-on. It’s about revealing the true cost of delivery in a world where new infrastructure—AI agents and POS rails —make their 3rd party delivery apps architecture obsolete. If we execute even the early stages of this plan, the incumbents cannot stop us without destroying themselves.

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