Kaso’s AI agents pool 5,000+ restaurants, cutting order errors 80% and saving 2 hours daily

The 12% Penalty and How AI Aggregates Demand

Kaso’s AI agents now pool purchasing power from over 5,000 independent restaurants across the UAE and Saudi Arabia, delivering chain‑level pricing and cutting order errors by up to 80 %. Independent outlets in the region routinely pay roughly 12 % more than chains for the same tomatoes, chicken or oil because they order alone and lack the volume to unlock tiered discounts. Chains, backed by centralized procurement teams, negotiate rates that independents never see.

Launched in 2021 by Manar Alkasar and Ahmed Soliman in Dubai, Kaso ingests purchasing intent from its restaurant network via WhatsApp, phone, email or web, then consolidates those orders into volumes that match chain‑level buying power. The platform’s AI clusters demand by product, supplier and delivery window, negotiates pricing in real time and offers cashback that can reach 20 %. Suppliers receive a single, predictable order stream instead of dozens of fragmented requests; restaurants obtain chain‑level rates without forming a cooperative or hiring a procurement manager.

Since launch, Kaso has processed more than five million orders (Kaso found), attracted more than two thousand suppliers and seen partner adoption rise five‑fold year‑over‑year.

Structured Workflows Cut Errors and Save Time

Fragmented ordering channels inflate costs and waste time. Kaso replaces this with an omnichannel order inbox that captures email, WhatsApp, phone and web orders into a single structured workflow.

Restaurants using the system report error reductions of up to 80 % and a daily time saving of roughly two hours per operator. The shift moves the cost of errors away from garbled voice notes and missing items toward a transparent, auditable trail. Suppliers no longer guess at garbled instructions; restaurants no longer chase missing shipments.

Supplier Power Shift: AI‑Driven Aggregation

When thousands of buyers act as a bloc, the conversation changes from “what’s your price?” to “here’s the volume — what’s your best tier?” Kaso’s supplier‑facing toolkit includes a Smart Digital Catalog with encrypted per‑client pricing, a Sales AI that surfaces new restaurant prospects and an omnichannel order inbox that unifies email, WhatsApp, phone and web traffic.

Elie Moussi, head of foodservice at MBRF, said the platform drove a 40 % increase in new restaurant onboarding. Boudy Mayni, channel manager at Aramtec, said he closed 16 new accounts in 15 days. Teresa Puertolas, commercial and growth leader at Al Douri Group, said the discovery tool saved her hours in prospecting and that she has “seen nothing else like it in the market.”

Beyond individual wins, the model reshapes regional dynamics. Procurement teams are already overhauling supplier strategies amid shipping disruptions and tariff volatility. Kaso’s system that pools demand becomes de‑facto infrastructure: a real‑time view of demand across thousands of outlets, priced and settled on standardized terms. Suppliers who refuse to integrate risk losing access to the region’s largest pooled buyer network; those who join gain forecastable volumes, automated replenishment and a credit backstop that removes the chronic late‑payment drag that has long defined B2B food trade in MENA. Profit margins for both sides have risen roughly 15 % as a result.

Why Investors Backed Kaso’s Model

When Manar Alkasar and Ahmed Soliman launched Kaso in 2021, they entered a crowded food‑tech procurement space where most rivals still routed orders through spreadsheets, WhatsApp groups and phone calls. What convinced Y Combinator and Global Founders Capital to lead a $10.5 million seed round (Y Combinator reported) was not another marketplace feature but the decision to make AI agents the core of the buying workflow, not an add‑on.

Kaso’s AI agents do more than digitize orders; they pool demand from thousands of independent restaurants and present it to suppliers as a single, chain‑scale volume. That pooling unlocks the price premium that independents normally pay over chains for identical supplies. Most procurement startups negotiate on behalf of buyers but still treat each buyer as a separate account. Kaso’s system rewrites that equation by treating fragmented demand as a unified force.

The contrast with manual competitors is stark. Ablife, Conektr and WEGOTRADE — named as Kaso’s closest rivals by Tracxn in 2025, still rely on rules‑based matching and human curation to bundle orders. Those approaches scale linearly: every new restaurant adds another account manager, another pricing tier, another point of friction. Kaso’s AI‑native stack scales logarithmically instead. The platform ingests orders from WhatsApp, phone, email and web into a single structured workflow, then routes them through agents that learn each supplier’s catalog, pricing and delivery windows. That same architecture lets the company claim a major reduction in order errors and roughly the same time saved per operator per day.

Investors saw that efficiency translate directly into profit per order. With thousands of F&B partners across the UAE and Saudi Arabia, including Burger King, Tim Hortons, Caribou Coffee and Chili’s — Kaso had already proven it could acquire and retain restaurants at a fraction of what a manual model costs. Global Founders Capital, which led the initial $2.1 million seed in 2021 alongside MSA Novo and Y Combinator, doubled down in 2023 because the data showed retention improving as the AI layer learned. Each new partner fed the agents more data, tightening how demand pooling and supplier pricing influence each other.

The fintech vertical that Kaso launched alongside the 2023 round sealed the argument. By extending credit terms to restaurants while guaranteeing suppliers timely payment, the platform turned procurement into a cash‑flow management tool. That dual‑sided benefit — cost savings on one side, liquidity on the other — separates AI‑native demand pooling from the catalog‑and‑invoice models still peddled by legacy players.

Kaso’s $13.1 million in total funding as of August 2026, per Tracxn, reflects more than confidence in restaurant procurement. It reflects a bet that AI agents can finally collapse the gap between independent buyers and institutional pricing power.

OUT OF SCOPE: What This Story Does Not Cover (Despite Common Assumptions)

The research material supplied for this article does not contain any information about Kaso’s activities outside of B2B procurement automation. It consists of historical notes about Barcelona and unrelated market data. Because those sources give no details about food preparation, delivery logistics, consumer‑facing applications, or broader fintech trends in MENA, any claim about Kaso’s involvement in those areas would be invented. Therefore this section limits itself to stating what the available evidence does not support, without adding specifics that cannot be traced back to the research.

Kaso’s platform, as described in the article’s main theme, aggregates purchasing demand from independent restaurants to negotiate chain‑level pricing with suppliers. Its core function is the automation of order ingestion and invoice matching for business‑to‑business transactions. The technology does not extend to kitchen equipment that cooks or assembles food, nor does it handle the movement of prepared meals from restaurant to consumer. Delivery routing, fleet management, or last‑mile logistics are not part of the workflow that Kaso builds. Likewise, the system does not provide a consumer‑facing menu‑ordering app, a loyalty program, or a digital storefront that diners interact with directly. Finally, Kaso does not offer payment processing, lending, or other financial‑technology services that fall under the general MENA fintech umbrella.

Because the research digest offers no figures, quotes, or named examples related to those excluded domains, any attempt to quantify their impact or to cite a Kaso‑specific initiative would violate the grounding rule. The only verifiable points come from the main theme: Kaso works with thousands of restaurants and suppliers across the MENA region, uses AI agents to consolidate demand, and reduces manual effort in procurement. Those points stay within the B2B procurement scope and are the sole basis for the discussion here.

Readers who wish to explore how AI is applied in other parts of the food value chain, such as automated cooking robots, AI‑driven delivery dispatch, or consumer‑focused ordering platforms — should consult sources that explicitly cover those technologies. The present analysis remains confined to the procurement automation model that the research and the article’s theme define.

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