We extract restaurant catalogues, menu items, delivery fees, preparation times, and promotional data from Bolt Food. 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 Listings objects from boltfood.com. All fields typed and schema-versioned.
"restaurant_id": "bf_938471", "name": "Burger Joint Central", "rating": 4.7, "review_count": 1204, "delivery_fee": 1.5, "min_order_value": 10.0, "is_promoted": true, "estimated_prep_time": "15-25 min"
| # | restaurant_id | name | rating | review_count | delivery_fee | min_order_value |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Menu Items objects from boltfood.com. All fields typed and schema-versioned.
"item_id": "mi_8472910", "restaurant_id": "bf_938471", "name": "Classic Cheeseburger", "price": 8.5, "original_price": 10.0, "section_name": "Mains", "dietary_tags": "['Halal']", "is_available": true
| # | item_id | restaurant_id | name | description | price | original_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Delivery & Fees objects from boltfood.com. All fields typed and schema-versioned.
"restaurant_id": "bf_938471", "base_delivery_fee": 1.0, "distance_fee": 0.5, "total_fee": 1.5, "dynamic_pricing_active": false, "surge_multiplier": 1.0, "currency": "EUR", "timestamp": "2026-05-12T10:30:00Z"
| # | restaurant_id | base_delivery_fee | distance_fee | small_order_fee | total_fee | dynamic_pricing_active |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Promotions objects from boltfood.com. All fields typed and schema-versioned.
"restaurant_id": "bf_938471", "promo_type": "PERCENTAGE_DISCOUNT", "discount_pct": 15, "free_delivery": false, "condition_text": "On orders over €20", "badge_text": "-15%", "valid_until": "2026-05-31T23:59:59Z"
| # | restaurant_id | promo_type | discount_pct | discount_abs | free_delivery | condition_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from boltfood.com. All fields typed and schema-versioned.
"keyword": "pizza", "city": "Tallinn", "position": 1, "restaurant_id": "bf_10293", "name": "Napoli Pizzeria", "is_sponsored": true, "delivery_time": "20-30 min", "scraped_at": "2026-05-12T10:35:12Z"
| # | keyword | city | latitude | longitude | position | restaurant_id |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Bolt Food scraper queries geospatial endpoints, reverse-engineers mobile APIs, and handles dynamic pricing variables to deliver accurate local data.
Extract names, ratings, review counts, categories, and exact coordinate locations for every restaurant in a defined radius.
Capture item names, descriptions, current prices, original prices, section mapping, and dietary tags.
Track base fees, distance modifiers, small order fees, and dynamic surge pricing in real time.
Identify active discounts, free delivery thresholds, and promotional badge text applied to specific menus.
Inject specific latitude and longitude coordinates to reveal hyper-local restaurant availability and delivery variables.
Extract estimated preparation and delivery windows presented to the user at the time of query.
Parse vegan, vegetarian, halal, and gluten-free identifiers directly from menu item metadata.
Capture standard opening times, closing times, and temporary closures for every restaurant location.
Maintain a hash index of menu prices and emit only updated records to reduce downstream processing load.
Brief in. Clean data out.
Provide target cities, coordinate bounding boxes, or specific restaurant URLs. We design the extraction schema.
We configure API reverse-engineering, proxy rotation, coordinate injection, and session management.
Schema validation, null-rate checks, and geospatial accuracy verification 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 complex mobile APIs and strict regional blocking. Here is how we maintain reliable extraction.
Bolt Food content is entirely dependent on the user location. We programmatically inject specific latitude and longitude coordinates to simulate users across a city grid, ensuring complete capture of local restaurants and accurate distance-based delivery fees.
Rather than parsing heavy DOM structures, our pipeline reverse-engineers Bolt Food mobile API endpoints. This provides cleaner, structured JSON responses, reduces latency, and exposes metadata not always visible in the web client.
Bolt Food employs strict rate limiting and regional blocking. We route requests through ISP-grade residential proxies matching the target country, rotating IPs per request to prevent bans and ensure uninterrupted pipeline execution.
Restaurant menus vary wildly in structure, with complex modifier groups and nested options. Our parsers normalise these variations into a consistent, flat schema, ensuring your database receives uniform records regardless of the source restaurant.
Menu items rarely change, but delivery fees and availability fluctuate constantly. We use hash-based diffing to emit records only when a price, fee, or status alters, keeping your storage costs low.
Rival delivery platforms and dark kitchens monitor menu prices and delivery fees to maintain competitive positioning.
Franchises analyse restaurant density, category saturation, and average ratings to identify underserved neighbourhoods.
Logistics teams track surge pricing patterns and distance modifiers to optimise their own rider compensation models.
Marketing teams monitor competitor discount structures, free delivery thresholds, and campaign durations.
B2B suppliers extract high-performing restaurant details to build targeted sales lists for wholesale ingredients or packaging.
FMCG brands track the rise of specific dietary tags, ingredients, and dish types across urban centres.
"Bolt Food holds hyper-local pricing and delivery dynamics for thousands of cities globally, but accessing this data requires precise geospatial querying and mobile API emulation."
Most teams underestimate the complexity of scraping food delivery platforms. Reliable Bolt Food extraction requires precise coordinate injection, mobile API reverse engineering, and residential proxies to bypass regional blocks. DataFlirt handles this infrastructure natively so your engineers can focus on core analysis.
Everything supported by our boltfood.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 API orchestration, coordinate grid generation, and retry logic. We map entire cities into overlapping radii to ensure 100% restaurant coverage without duplicate records.
We maintain pools of residential ISP proxies localised to target countries. Rotation happens per-request to bypass rate limits and geographic access restrictions natively.
Pipelines run on AWS Lambda for burst scaling during peak meal times. Airflow handles scheduling and dependency management. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About boltfood.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Bolt Food is generally permissible. DataFlirt targets only public, non-authenticated restaurant, menu, and pricing data. We do not extract personal user data or circumvent authentication walls.
We generate a grid of latitude and longitude coordinates covering your target city. Our crawlers inject these coordinates into the API requests, simulating users at different locations to capture accurate delivery fees and restaurant availability.
Yes. We extract nested menu structures, including mandatory add-ons, optional modifiers, and size variations, normalising them into a flat or nested JSON schema depending on your warehouse requirements.
Delivery fees and surge pricing can be tracked in near real-time. Streaming pipelines achieve sub-15-minute latency for specific high-priority coordinate locations.
Yes. Our primary extraction method for Bolt Food relies on reverse-engineering their mobile application endpoints, which provides highly structured data and avoids the fragility of DOM scraping.
We can extract data from any city where Bolt Food operates globally. You provide the city names or bounding boxes, and we configure the coordinate grids.
Yes. We provide a sample run of up to 50 restaurants in a specific city as part of the pre-engagement scoping process, allowing you to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off restaurant catalogue dump or continuous delivery fee tracking across 50 cities, we scope, build, and operate the pipeline. Tell us what you need.
