We extract product specifications, ingredient lists, volume pricing tiers, and store locations from sees.com. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your schedule.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Product Listings objects from sees.com. All fields typed and schema-versioned.
"sku": "500334", "name": "Assorted Chocolates", "category": "Chocolates", "price": 31.0, "currency": "USD", "weight_oz": 16.0, "piece_count": 26, "seasonal_flag": false, "in_stock": true
| # | sku | name | category | sub_category | price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Ingredients & Allergens objects from sees.com. All fields typed and schema-versioned.
"sku": "500334", "ingredients_list": "Dark chocolate, milk chocolate, sugar, corn syrup, butter, heavy cream...", "allergens_present": "['Milk', 'Soy', 'Tree Nuts']", "allergens_facility": "['Peanuts', 'Egg', 'Wheat']", "kosher_certified": true, "gluten_free": true, "calories_per_serving": 150
| # | sku | name | ingredients_list | allergens_present | allergens_facility | kosher_certified |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Pricing & Volume objects from sees.com. All fields typed and schema-versioned.
"sku": "503120", "base_price": 28.5, "currency": "USD", "volume_tier_1_qty": 50, "volume_tier_1_price": 26.0, "volume_tier_2_qty": 100, "volume_tier_2_price": 24.5, "corporate_discount_eligible": true, "last_updated": "2026-05-12T10:00:00Z"
| # | sku | base_price | currency | volume_tier_1_qty | volume_tier_1_price | volume_tier_2_qty |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews objects from sees.com. All fields typed and schema-versioned.
"review_id": "REV_94821", "sku": "500334", "rating": 5, "review_date": "2026-04-12", "review_title": "Classic favourite", "verified_buyer": true, "helpful_votes": 12
| # | review_id | sku | reviewer_name | rating | review_date | review_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Store Locations objects from sees.com. All fields typed and schema-versioned.
"store_id": "CA_SF_01", "name": "Market Street", "city": "San Francisco", "state": "CA", "zip_code": "94103", "latitude": 37.7845, "longitude": -122.404, "hours_mon_fri": "10:00 AM - 7:00 PM"
| # | store_id | name | address_line_1 | city | state | zip_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our scraper handles the complexities of the See's Candies platform: dynamic custom mix configurators, unstructured ingredient lists, volume pricing matrices, and store locator APIs.
Extract SKUs, titles, descriptions, weights, piece counts, and imagery across all categories including chocolates, lollipops, and gifts.
Tokenise complex ingredient strings and extract structured allergen warnings, Kosher certifications, and gluten-free flags.
Execute JavaScript to render the Custom Mix tool and extract available individual pieces, weights, and constraints.
Capture base prices alongside corporate gifting volume tiers and discount eligibility rules.
Poll the backend store locator API to extract all physical retail locations, operating hours, and available services.
Monitor limited-time holiday items, seasonal packaging changes, and out-of-stock statuses across the calendar year.
Paginate through product reviews to capture ratings, text, verified buyer flags, and helpful vote counts.
Parse nutritional fact tables to structure calories, macros, and serving sizes for dietary analysis.
Run recurring pipelines that only emit updated records when prices, ingredients, or stock levels change.
Brief in. Clean data out.
Provide target categories, specific SKUs, or request a full site crawl. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for sees.com.
Schema validation, null-rate checks, and ingredient list normalisation before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting structured data from a modern eCommerce frontend requires handling dynamic hydration and API rate limits. Here is our approach.
The Custom Mix configurator relies heavily on client-side JavaScript. We run full Playwright browser sessions to hydrate the application state and extract the individual piece inventory.
Ingredient lists on sees.com are often unstructured text blocks. Our pipeline applies regex tokenisation to split ingredients and normalise allergen warnings into queryable arrays.
Physical store data is hidden behind a locator search interface. We bypass the frontend and query the underlying location APIs directly, iterating through US zip codes to build a complete retail footprint.
The See's Candies catalogue changes drastically during holidays. We maintain a hash index of all active SKUs, emitting diffs when seasonal items are introduced or retired.
To prevent IP bans during full catalogue crawls, we route requests through US-based residential proxies, matching our crawl rate to standard user behaviour.
Confectionery brands monitor base prices, weight-to-price ratios, and volume discount tiers to benchmark their own product lines.
Food scientists and market analysts track ingredient changes, allergen profiles, and nutritional shifts across the product catalogue.
Real estate analysts extract store locations and operating hours to map See's physical presence against demographic data.
Supply chain teams track the introduction and sell-out rates of holiday-specific SKUs to model seasonal confectionery demand.
B2B sales teams analyse volume discount tiers and corporate gift box configurations to optimise their own enterprise offerings.
Marketing teams aggregate product reviews to identify customer preferences for specific flavour profiles and packaging types.
"Confectionery market intelligence requires precise tracking of seasonal SKUs, ingredient profiles, and volume pricing tiers across the entire See's Candies catalogue."
Extracting data from sees.com involves handling dynamic custom box configurators, parsing unstructured ingredient lists, and polling store-level inventory APIs. DataFlirt manages the JavaScript rendering, proxy rotation, and schema normalisation so your analysts get clean datasets ready for immediate query.
Everything supported by our sees.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 orchestrates the catalogue traversal while Playwright handles JavaScript execution for dynamic components like the Custom Mix tool.
We route requests through US residential IPs, rotating per request to avoid rate limits while polling store locator APIs and product pages.
Pipelines execute on AWS Lambda and ECS. Airflow manages scheduling and dependency trees, with all pipeline state stored in managed PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About sees.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product, pricing, and location data is generally permissible. DataFlirt targets only public, non-authenticated endpoints. We do not extract personal user data or circumvent authentication walls. Clients should review applicable terms of service and consult legal counsel.
We use Playwright to execute the client-side JavaScript required to render the Custom Mix configurator. This allows us to extract the individual pieces, associated weights, and box constraints that are not present in the static HTML.
Yes. We apply regex tokenisation to the raw ingredient text blocks, separating individual ingredients and structuring allergen warnings into distinct, queryable JSON arrays.
Full catalogue crawls typically execute daily or weekly depending on your requirements. Store location APIs can be polled at higher frequencies if tracking specific retail footprint changes.
Yes. Our change detection system maintains a hash index of the catalogue. When holiday items are added or removed, the pipeline emits a diff highlighting the seasonal inventory shifts.
Yes. We capture base prices alongside any visible volume discount matrices, including minimum purchase quantities required for corporate gifting rates.
We offer managed pipelines starting with weekly deliveries of the full product catalogue and store locations. Contact us with your specific data requirements for a scoped proposal.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off extraction of store locations or a continuous feed of product pricing and ingredients, we build and operate the pipeline. Tell us your requirements.
