We extract vendor profiles, real wedding metadata, bridal attire catalogues, and editorial trends from Loverly. 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 Vendors & Venues objects from loverly.com. All fields typed and schema-versioned.
"vendor_id": "vnd_9281", "name": "Brooklyn Botanic Garden", "category": "Venue", "location": "Brooklyn, NY", "price_tier": "$$$", "rating": 4.8, "review_count": 142
| # | vendor_id | name | category | location | price_tier | rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Real Weddings objects from loverly.com. All fields typed and schema-versioned.
"wedding_id": "rw_4921", "title": "Modern Industrial Brooklyn Wedding", "season": "Autumn", "colour_palette": "['Burgundy', 'Gold', 'Navy']", "style": "Industrial", "budget_tier": "$$$$"
| # | wedding_id | title | location | season | colour_palette | style |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Bridal Attire objects from loverly.com. All fields typed and schema-versioned.
"product_id": "dr_8832", "designer": "Maggie Sottero", "silhouette": "A-Line", "neckline": "Sweetheart", "fabric": "Lace", "price": 2400.0
| # | product_id | designer | collection | silhouette | neckline | fabric |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Editorial & Advice objects from loverly.com. All fields typed and schema-versioned.
"article_id": "ed_1092", "title": "10 Trending Wedding Colours for 2026", "author": "Jane Doe", "category": "Planning", "tags": "['Colours', 'Trends', '2026']", "read_time": "4 min"
| # | article_id | title | author | publish_date | category | tags |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Registry & Gifts objects from loverly.com. All fields typed and schema-versioned.
"item_id": "rg_551", "title": "KitchenAid Artisan Stand Mixer", "brand": "KitchenAid", "category": "Kitchen", "price": 399.99, "retailer": "Crate & Barrel"
| # | item_id | title | brand | category | price | retailer |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Loverly scraper handles every layer of the platform: vendor directories, infinite-scroll image galleries, and structured editorial content. We manage the JavaScript rendering and pagination logic entirely.
Extract venues, planners, and photographers with contact information, location data, and pricing tiers normalised into strict schemas.
Extract seasonal trends, colour palettes, budget tiers, and linked vendor teams from comprehensive gallery posts.
Scrape dress designers, silhouettes, fabrics, necklines, and external retail links across the entire attire database.
Parse high-resolution image URLs mapped to specific wedding styles, themes, and vendor portfolios.
Extract wedding planning advice, checklists, and trend reports including full text, author metadata, and publication dates.
Target vendors and venues by specific city, state, or region to build localised market intelligence datasets.
Capture vendor ratings, review text, and reviewer metadata to quantify vendor reputation at scale.
Track popular registry items, brands, and external retailer links featured across inspiration boards.
Run one-off bulk exports or configure continuous pipelines to capture new vendor additions and seasonal content.
Brief in. Clean data out.
Provide target categories, geographic regions, or content types. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and DOM parsing for loverly.com.
Schema validation, null-rate checks, location accuracy, and sample output review before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Scraping visual-heavy wedding platforms presents unique challenges. Here is how we maintain pipeline stability.
Loverly relies heavily on lazy-loaded image galleries. We intercept network requests and parse JSON state objects to extract the original high-resolution image URLs rather than compressed thumbnails.
Vendor directories and real wedding feeds use infinite scroll mechanisms. We run full Playwright browser sessions to trigger lazy-loads and capture complete lists that standard HTTP clients miss.
Vendor contact formats and pricing tiers vary wildly. Our pipelines apply regex and NLP-based parsing to normalise unstructured text into clean, typed fields for your database.
High-concurrency catalogue crawls trigger rate limits. We use residential ISP proxies with realistic browser fingerprints and randomised request timing to maintain uninterrupted access.
For large vendor catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Marketplaces and directories acquire structured venue and planner data to bootstrap their own networks across new geographic regions.
Fashion brands and planners analyse real wedding colours, fabrics, and styles to predict upcoming seasonal trends.
Wedding tech platforms monitor vendor pricing, availability, and service offerings across different local markets.
B2B service providers extract vendor contact details and social links for targeted outreach campaigns.
Bridal publishers enrich their own editorial platforms with structured dress catalogues and gallery metadata.
ML teams use tagged wedding images, colour palettes, and descriptions to train visual recommendation engines.
"Loverly holds the most structured mapping of wedding vendors, styles, and seasonal trends - but extracting that visual and relational data requires purpose-built infrastructure."
Scraping wedding platforms involves navigating infinite-scroll image galleries, unstructured vendor profiles, and heavy JavaScript rendering. DataFlirt handles the proxy rotation, DOM parsing, and schema normalisation so your data science team can focus on trend analysis and vendor matching.
Everything supported by our loverly.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, deduplication, and retry logic. Playwright handles JavaScript rendering, infinite scroll, and interaction flows.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required to prevent IP bans.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. State stored in Postgres.
Data delivered to where your team already works — no new tooling required.
About loverly.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Loverly is generally permissible. DataFlirt targets only public, non-authenticated vendor directories, inspiration galleries, and editorial content. We do not extract personal user data or circumvent authentication walls.
We do not download the physical image files to save bandwidth, but we extract the direct, high-resolution source URLs from the page's underlying JSON state or API responses, bypassing the low-quality thumbnails.
Yes. If a vendor lists their email, phone number, website URL, or social media handles publicly on their Loverly profile, our pipeline extracts and normalises that data.
Yes. We can configure continuous pipelines that monitor the bridal attire categories for new additions and output only the newly detected dresses using hash-based change detection.
Loverly tags real wedding galleries with specific metadata. We extract these structured tags, including colour palettes, styles, seasons, and budget tiers directly from the page source.
Our minimum engagement typically starts with a defined category crawl, such as all venues in a specific country or all dresses from specific designers, delivered on a weekly schedule.
Absolutely. We provide a sample run of up to 500 vendor profiles or real wedding galleries during the scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off vendor directory dump or continuous tracking of bridal fashion trends - we scope, build, and operate the pipeline. Tell us what you need.
