Platform · Keeta

Keeta Data Scraping

A fast-moving entrant in Saudi delivery, and a bilingual catalogue where translating into the price field would break the join.

Keeta data scraping collects restaurant listings, menus, item pricing, delivery fees and availability from Keeta across Saudi Arabia and the wider GCC. Two things shape the work: store-level collection, because the same brand prices differently by branch, and bilingual menus where Arabic and English coexist and neither should be overwritten by a translation.

Two separate businesses asked us for Keeta data last quarter, one specifically for Saudi store menus. It is a young platform in a market where delivery competition is intense and moving fast.

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keeta_menus.jsonl LIVE FEED

{"store_id":"kt-44120","brand_id":"brand-882", "city":"Riyadh","area":"Al Olaya", "item_name_ar":"برجر دجاج", "item_name_en":"Chicken Burger", "item_price":25.00,"currency":"SAR", "modifier_groups":[{"name":"Size","is_required":true, "options":[{"label":"Regular","price":8.00}, {"label":"Large","price":14.00}]}], "min_realisable_price":33.00, "price_basis":"item + cheapest required modifiers", "delivery_fee":10.00} {"store_id":"kt-99021","brand_id":"brand-882", "area":"Al Malaz","item_price":22.00, "min_realisable_price":30.00, "note":"same brand, different branch, different price — brand average charges neither"} {"store_id":"kt-77410","serviceable":false, "store_open":"null", "caution":"area not served — distinct from a closed store"}

3 of 1,884,110 store-item rows stores: 4,120store level, both scripts retained · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Keeta or its owners. Keeta and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

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Keeta at a glance

How we handle Keeta specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Platform: Keeta — food and quick delivery, Saudi Arabia and GCC
Unit of collection: Store, not brand. A chain prices differently by branch
Language: Arabic and English coexist. Both retained, neither translated into the record
Menu structure: Item, modifier groups and options priced independently
Fees: Delivery, service and small-order fees separate from item price
Promotions: Platform and merchant-funded kept apart, since they behave differently
Coverage: Serviceability by area, distinct from a store being closed
Refresh: Daily standard; sub-daily where promotional intensity is the question

Platform specifics

What is specific to Keeta and to GCC delivery

These are the reasons a Keeta dataset needs its own handling rather than a shared retail schema.

Store-level, because brand-level averages a chain that does not price as one

A quick-service chain on a GCC delivery platform frequently runs different prices at different branches in the same city, driven by franchise ownership, location cost and local competitive pressure.

Collecting at brand level produces an average that no branch charges.
Menu availability differs by branch, so an item missing from one store is not delisted from the chain.
Franchise versus company-operated stores behave differently on promotions, and the distinction is not always exposed.

Every record carries store_id and the area it serves. We record brand_id alongside so a chain view is available, but it is a rollup you choose rather than the level we collect at. This is the same discipline our Carrefour work applies to franchise versus company-operated stores.

Arabic and English in one menu, and why we do not translate

GCC delivery menus mix Arabic and English constantly — sometimes an item name in one script and its description in the other, sometimes both on the same line.

What we do

Retain both scripts exactly as published, in separate fields where the platform separates them.
Record the dominant script so a downstream filter works.
Match items across stores on structure, price and position rather than name similarity, which fails badly across scripts.

What we do not do

We do not machine-translate into the item name field. Arabic dish names transliterate inconsistently, and a translated name in the same column as an original becomes indistinguishable within weeks — at which point a join against your own menu data silently starts failing.

Where you want translation it arrives as a separate field with the engine and date recorded.

Modifiers are where the real price lives

On a food delivery platform the headline item price is frequently not what a customer pays. Modifier groups — size, extras, sides, sauces — carry their own prices and their own required-or-optional logic.

A meal at 25 SAR with a required size selection starting at +8 has a real entry price of 33.
Required versus optional changes whether a modifier price is avoidable.
Modifier availability varies by store, so the same item costs differently at two branches for reasons the item price does not show.

We deliver modifier groups as structured records with is_required, the option prices and the minimum and maximum selections. We also compute a min_realisable_price — item plus the cheapest required modifiers — with the basis recorded, because that is the number comparable across stores.

A vendor delivering only the headline item price is giving you a figure no customer pays.

Scope

What we collect on Keeta, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

Store-level pricing with store_id and area on every record
Brand identifier alongside, so a chain rollup is available as a choice
Item names in both scripts as published, with dominant script recorded
Modifier groups as structured records with required flags and option prices
Minimum realisable price computed at a stated basis
Delivery, service and small-order fees as separate fields
Platform-funded and merchant-funded promotions kept apart
Serviceability by area, distinct from a store being closed
Rating and review counts without reviewer identity

❌ What we do not, and why

Machine translation written into the item name field
Sales, order volumes or store revenue, none of which is published
Rider, customer or order data
A brand-level price that averages branches charging differently
Anything behind a signed-in account

Core Keeta fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field	What it is on this platform
platform / country / city	Which service and where
store_id / brand_id	Collected at store level, rolled up to brand by choice
area / serviceable	Delivery area and whether it is served at all
item_id / item_name_ar / item_name_en	Both scripts retained as published
script_dominant	So a downstream filter works without guessing
item_price / currency	Headline price, which is rarely what is paid
modifier_groups	Array with is_required, min and max selections, option prices
min_realisable_price / price_basis	Item plus cheapest required modifiers, basis stated
delivery_fee / service_fee / small_order_fee	Each separately
promo_type / promo_funder	Platform-funded and merchant-funded behave differently
store_open / observed_at	Closed is a state distinct from unserviceable

Use cases

What teams do with Keeta data

Competitive menu pricing at branch level

Store-level records with minimum realisable price, so a chain's real price spread across a city is visible rather than averaged into one figure nobody charges.

KSA and GCC market entry pricing

Item and modifier pricing across competing brands and areas, which is the evidence a brand needs before setting a menu for a new market.

Promotional intensity by funder

Platform-funded and merchant-funded promotions tracked separately, because a platform running a campaign and a merchant discounting are different competitive signals.

Delivery fee competitiveness

Fees tracked as their own series by area, since on a delivery platform the fee stack frequently moves independently of menu price.

The 24-hour sample — run on your sources, not ours

Send us a Keeta item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

Real extraction from your actual sources
Returned within 24 hours
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Keeta is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Keeta data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what food & restaurant data covers, and a Keeta-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

Keeta data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

Because a chain on a GCC delivery platform frequently prices differently by branch, driven by franchise ownership and local competition. A brand-level figure is an average nobody charges.

We record store_id and the area, with brand_id alongside so a chain rollup is available as your choice rather than our collection level.

Not into the item name field. Arabic dish names transliterate inconsistently, and a translated name in the same column as an original becomes indistinguishable within weeks — at which point a join against your own menu data starts failing silently.

Both scripts are retained as published, and translation arrives as a separate field with the engine and date recorded if you want it.

Because the headline item price is frequently not what a customer pays. A 25 SAR item with a required size selection starting at +8 has a real entry price of 33.

We deliver modifier groups with required flags and option prices, and compute a minimum realisable price with the basis stated. A vendor giving you only the item price is giving you a number no customer pays.

No. No delivery platform publishes sales or order volumes. Ranking and review velocity are sometimes used as proxies and both are weak in a market where a single promotion moves position sharply.

We deliver what is observable and leave the demand inference to you, where you can put a confidence on it.

Same market, different platform, and they are usually taken together. GCC delivery competition is between platforms as much as between restaurants, so a single-platform view misses the comparison that matters.

Both use the same schema and the same area panel, so the records are comparable by construction rather than after a reconciliation.

We quote individually. Drivers are city and area count, store count, menu depth and refresh frequency — menu depth matters here because modifier groups multiply records well beyond the item count.

One scoping call, a free pilot within 24 hours, then a fixed monthly quote. Request a quote.

See real Keeta data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

Get a free sample Talk to an engineer

Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

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