We extract product catalogues, variant-level stock depth, pricing signals, and store availability from Love, Bonito. Delivered as clean JSON, CSV, or Parquet to S3 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 Product Metadata objects from lovebonito.com. All fields typed and schema-versioned.
"product_id": "LB-DR-9382", "name": "Kalila Fit & Flare Dress", "category": "Clothing", "subcategory": "Dresses", "collection": "The Staples", "fabric_composition": "95% Polyester, 5% Spandex", "care_instructions": "Machine wash cold", "product_url": "https://www.lovebonito.com/intl/kalila-fit-flare-dress.html"
| # | product_id | name | description | category | subcategory | collection |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Variants & Inventory objects from lovebonito.com. All fields typed and schema-versioned.
"sku": "LB-DR-9382-NVY-S", "product_id": "LB-DR-9382", "colour": "Navy", "size": "S", "stock_status": "IN_STOCK", "low_stock_warning": true, "price": 49.0, "currency": "USD"
| # | sku | product_id | colour | size | stock_status | stock_quantity |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Categories & Taxonomy objects from lovebonito.com. All fields typed and schema-versioned.
"category_id": "cat_dresses_midi", "name": "Midi Dresses", "parent_category": "Dresses", "product_count": 342, "position": 2, "seo_title": "Midi Dresses for Women | Love, Bonito", "url": "https://www.lovebonito.com/intl/clothing/dresses/midi"
| # | category_id | name | url | parent_category | product_count | banner_image_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Store Availability objects from lovebonito.com. All fields typed and schema-versioned.
"store_id": "st_sg_ion", "store_name": "ION Orchard", "region": "Singapore", "sku": "LB-DR-9382-NVY-S", "stock_status": "LIMITED_STOCK", "last_checked": "2026-10-12T08:15:00Z"
| # | store_id | store_name | region | product_id | sku | size |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from lovebonito.com. All fields typed and schema-versioned.
"review_id": "rev_849201", "product_id": "LB-DR-9382", "rating": 5, "fit_rating": "True to size", "quality_rating": "Excellent", "verified_buyer": true, "review_date": "2026-09-28"
| # | review_id | product_id | author_name | rating | title | body |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Love, Bonito scraper handles product listings, variant-level inventory grids, dynamic pricing, and category taxonomy with automated session management and anti-bot circumvention built in.
Title, fabric composition, care instructions, fit notes, and high-resolution image URLs extracted for every product.
Capture stock status and low-stock warnings across every size and colour permutation on the platform.
Monitor original prices, sale prices, and discount percentages across different regional storefronts.
Extract full category trees including LB:GO, Maternity, and Workwear collections with accurate product counts.
Track in-store inventory status for specific SKUs across physical retail locations.
Scrape customer reviews including specific fit feedback and quality ratings to inform product development.
Support for international sites including Singapore, Malaysia, Indonesia, and global storefronts.
Run daily or hourly pipelines that emit only changed records, reducing downstream processing costs.
Capture CDN links for all product gallery images, campaign banners, and colour swatches.
Brief in. Clean data out.
Provide target categories, regions, or specific product URLs. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for lovebonito.com.
Schema validation, null-rate checks, and inventory status verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, Snowflake stage, or via Webhook on agreed cadence.
Apparel sites use aggressive caching and dynamic inventory grids. Here is how we maintain data integrity.
Love, Bonito loads size and colour availability dynamically via JavaScript. We use Playwright to execute these scripts and intercept the underlying API responses, ensuring 100% accurate variant-level stock states.
High-traffic apparel sites cache product pages heavily at the CDN layer. Our crawlers inject specific cache-busting headers and query parameters to ensure we extract the true database state, not a stale HTML snapshot.
Pricing and catalogue availability change based on the user IP. We route requests through region-specific residential proxies (e.g., Singapore, Malaysia, US) to capture accurate localized data without triggering geo-blocks.
Fashion retailers frequently update site layouts for seasonal campaigns. We use multiple fallback chains per field, targeting structured data, internal APIs, and stable DOM elements to prevent pipeline breakage.
We monitor total product counts and out-of-stock ratios per category. If a site update causes a sudden 50% drop in extracted SKUs, our system halts delivery and alerts our engineers immediately.
Fashion retailers track Love, Bonito pricing tiers and promotional discounting to adjust their own merchandising strategies.
Merchandising teams analyse category depth, colour availability, and sizing distribution to identify gaps in their own product lines.
Analysts monitor which silhouettes, fabrics, and colours sell out fastest to predict upcoming seasonal consumer preferences.
Retail strategists study how long items remain at full price before hitting the sale section, informing their own markdown cadences.
Operations teams track restock frequencies and out-of-stock durations to benchmark their own inventory velocity against industry leaders.
Brands expanding into Southeast Asia use regional pricing and catalogue availability data to model entry strategies.
"Love, Bonito represents the benchmark for Asian fit and omnichannel retail data, but tracking their variant-level inventory requires dedicated extraction infrastructure."
Most apparel brands update their inventory grids and pricing dynamically via complex frontend frameworks. Scraping this reliably requires headless browsers, cache-busting headers, and residential proxies. DataFlirt manages this pipeline end-to-end so your team can focus on merchandising analysis.
Everything supported by our lovebonito.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 handles JavaScript rendering and interaction flows for dynamic inventory grids.
We maintain pools of residential ISP proxies across target regions. Rotation happens per request to bypass rate limits and capture local pricing.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and SLA alerting. All pipeline state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About lovebonito.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible under applicable law. DataFlirt extracts only public product, pricing, and inventory data. We do not extract personal data or circumvent authentication walls to access LBCommunity+ member data.
Love, Bonito loads size and colour availability via client-side JavaScript. We use headless Playwright browsers to execute these scripts and intercept the underlying network responses, ensuring we capture the exact stock state for every SKU.
Yes. We route requests through geo-targeted residential proxies to access the Singapore, Malaysia, Indonesia, and International storefronts, capturing the correct localized pricing and currency for each region.
We can run full catalogue sweeps daily, or configure high-frequency pipelines to check specific high-velocity SKUs hourly. Our cache-busting techniques ensure we retrieve the latest database state.
Yes. We extract the omnichannel stock status for physical retail locations, allowing you to track inventory distribution between online and offline channels.
Yes. We provide a sample extraction of up to 200 products across multiple categories during the scoping phase, allowing you to validate the schema and variant mapping before signing a contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily catalogue sync or continuous inventory monitoring — we scope, build, and operate the pipeline. Tell us what you need.
