We extract restaurant profiles, menu items, delivery fees, and user ratings from Seamless. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Restaurant Profiles objects from seamless.com. All fields typed and schema-versioned.
"restaurant_id": "314982", "name": "Joe's Pizza", "cuisine_tags": "['Pizza', 'Italian']", "rating": 4.7, "review_count": 2401, "delivery_fee": 2.99, "pickup_available": true, "is_ghost_kitchen": false
| # | restaurant_id | name | url | cuisine_tags | address | latitude |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Menu Items objects from seamless.com. All fields typed and schema-versioned.
"item_id": "mi_849201", "restaurant_id": "314982", "category": "Specialty Pies", "name": "Pepperoni Slice", "price": 4.5, "currency": "USD", "popular_badge": true, "dietary_tags": "['Contains Dairy', 'Contains Gluten']"
| # | item_id | restaurant_id | category | name | description | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Delivery & Fees objects from seamless.com. All fields typed and schema-versioned.
"restaurant_id": "314982", "target_zipcode": "10014", "base_delivery_fee": 2.99, "service_fee_pct": 10.0, "small_order_fee": 2.0, "minimum_order_value": 10.0, "gh_plus_eligible": true, "surge_pricing_active": false
| # | restaurant_id | target_zipcode | base_delivery_fee | service_fee_pct | small_order_fee | minimum_order_value |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from seamless.com. All fields typed and schema-versioned.
"review_id": "rev_918237", "restaurant_id": "314982", "star_rating": 5, "review_text": "Best slice in the village. Arrived hot.", "review_date": "2026-03-14", "merchant_response": "Thanks for the love!", "helpful_votes": 12, "scraped_at": "2026-05-12T10:00:00Z"
| # | review_id | restaurant_id | author_name | star_rating | review_text | review_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from seamless.com. All fields typed and schema-versioned.
"search_term": "pizza", "position": 3, "restaurant_id": "314982", "name": "Joe's Pizza", "sponsored_badge": false, "delivery_time_est": "25-35 min", "promo_text": "$5 off your order of $20+", "scraped_at": "2026-05-12T10:05:00Z"
| # | search_term | location_lat | location_lng | position | restaurant_id | name |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Seamless scraper manages complex location spoofing, dynamic menu rendering, and fee calculation logic. We handle the session cookies and coordinate mapping to deliver accurate local data.
Seamless displays different restaurants and fees based on exact coordinates. We inject precise lat/long payloads into session cookies to extract hyper-local data.
Capture categories, item names, descriptions, dietary tags, calorie counts, and complex nested customisation options across thousands of menus.
Extract base delivery fees, service percentages, small order penalties, and minimum cart values specific to the target delivery zone.
Cross-reference addresses and operational data to identify virtual brands and ghost kitchens operating out of shared facilities.
Extract full review text, star ratings, order context, and merchant responses to track customer satisfaction at the store level.
Track organic versus sponsored ranking for specific cuisine searches across different postcodes and times of day.
Monitor store open/close status, scheduled operating hours, and temporary closures in real time.
Capture active promotional banners, threshold discounts, and loyalty program eligibility for competitive analysis.
Run extractions at specific times of day to capture lunch or dinner rush pricing dynamics and delivery time fluctuations.
Brief in. Clean data out.
Provide target postcodes, specific restaurant URLs, or cuisine keywords. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, location spoofing, session management, and proxy rotation for seamless.com.
Schema validation, null-rate checks, location accuracy verification, and sample menus before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Food delivery platforms rely on strict location tracking and dynamic pricing. Here is how we stay resilient and why teams choose managed infrastructure.
Seamless heavily restricts data without a valid delivery address. Our crawlers programmatically inject precise latitude and longitude coordinates into the session state, allowing us to map local restaurant availability exactly as a user in that postcode would see it.
Restaurant menus and customisation options load dynamically via complex API calls. We run full Playwright browser sessions to trigger lazy-loading and capture nested modifier groups that headless HTTP clients fail to extract.
Scraping thousands of menus triggers aggressive IP bans. We distribute requests across residential ISP proxies with realistic browser fingerprints and randomised request timing to bypass perimeter defences.
Restaurants format menus inconsistently. Our pipeline applies post-extraction normalisation to standardise dietary tags, price formats, and category names, ensuring clean data enters your warehouse.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, missing delivery fees, and coverage drops, responding before you notice any data gaps.
Restaurant groups monitor competitor menu prices, promotional offers, and delivery fees to optimise their own pricing strategies.
Ghost kitchen operators analyse cuisine saturation and review counts by postcode to identify underserved delivery zones.
Food brands track popular badges, dietary trends, and modifier structures to engineer higher-converting menus.
Aggregators and analysts track dynamic service fees and minimum order values across different platforms to compare unit economics.
Franchise operators monitor store-level reviews and ratings to enforce quality control and operational standards across locations.
Private equity firms track restaurant churn, review velocity, and promotional frequency to evaluate the health of food delivery markets.
"Seamless holds the operational footprint of thousands of restaurants and ghost kitchens, but accessing that menu data requires bypassing strict location walls."
Most engineering teams underestimate the complexity of scraping food delivery platforms. Extracting accurate pricing requires managing session cookies for precise lat/long coordinates, rendering heavy JavaScript, and bypassing location-based rate limits. DataFlirt handles this infrastructure so you can focus on analysis.
Everything supported by our seamless.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and deduplication. Playwright manages JavaScript rendering, location cookie injection, and interaction flows for complex menus.
We maintain pools of residential ISP proxies across US regions. Rotation happens per request with sticky sessions to maintain location state during menu extraction.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling for time-sensitive meal rush extractions. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About seamless.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from seamless.com is generally permissible under applicable law. DataFlirt targets only public, non-authenticated restaurant, menu, and pricing data. We do not extract personal user data or circumvent authentication walls. Clients should review terms of service and consult legal counsel for specific use cases.
We use precise latitude and longitude injection within our browser sessions. You provide the target postcodes or coordinates, and our crawlers simulate users in those exact locations to capture accurate delivery fees and local menu pricing.
Yes. Our Playwright integration renders the full menu interface, allowing us to extract complex modifier groups, required add-ons, and conditional pricing logic that standard HTTP scrapers miss.
We can schedule pipelines to run at specific times, such as immediately before the lunch or dinner rush, to capture dynamic delivery times and surge fees. Full market refreshes typically complete within a 4 to 8 hour window.
Yes. By cross-referencing restaurant addresses and operational data across the platform, we can flag multiple virtual brands operating from a single physical kitchen location.
Our minimum engagements typically start at a defined list of 500 to 5,000 restaurants or specific postcodes with weekly delivery. Contact us with your target volume for a precise quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off menu dump for a single city or continuous price tracking across 50,000 restaurants, we scope, build, and operate the pipeline. Tell us what you need.
