We extract product configurations, pricing signals, material specs, dimensions, and reviews from Thuma.co. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery 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 Variants objects from thuma.co. All fields typed and schema-versioned.
"sku": "TH-BED-WAL-Q-DSG", "product_name": "The Bed", "wood_finish": "Walnut", "fabric_colour": "Dark Stone", "bed_size": "Queen", "price": 1195.0, "availability": true
| # | sku | product_name | wood_finish | fabric_colour | bed_size | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Dimensions & Specs objects from thuma.co. All fields typed and schema-versioned.
"sku": "TH-BED-WAL-Q-DSG", "length_cm": 214.6, "width_cm": 163.8, "height_cm": 88.9, "clearance_cm": 22.8, "frame_material": "Upcycled Wood", "joinery_type": "Japanese Joinery"
| # | sku | length_cm | width_cm | height_cm | clearance_cm | weight_kg |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Customer Reviews objects from thuma.co. All fields typed and schema-versioned.
"review_id": "REV-849201", "sku": "TH-BED-WAL-Q-DSG", "rating": 5, "date_posted": "2026-02-14", "review_title": "Quiet and solid", "verified_buyer": true, "helpful_votes": 14
| # | review_id | sku | author | rating | date_posted | review_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Shipping & Inventory objects from thuma.co. All fields typed and schema-versioned.
"sku": "TH-BED-WAL-Q-DSG", "stock_status": "In Stock", "shipping_estimate": "1-3 business days", "box_count": 3, "ships_from": "US Warehouse", "return_policy": "100-day trial"
| # | sku | stock_status | shipping_estimate | box_count | box_1_dimensions | box_2_dimensions |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Add-ons & Bundles objects from thuma.co. All fields typed and schema-versioned.
"bundle_id": "BND-BED-NS-2", "base_sku": "TH-BED-WAL-Q-DSG", "addon_name": "The Nightstand", "addon_price": 395.0, "bundle_discount": 0.0, "total_price": 1590.0
| # | bundle_id | base_sku | addon_name | addon_sku | addon_price | bundle_discount |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our scraper handles dynamic variant rendering, headless commerce APIs, and third-party review integrations to deliver a clean dataset of Thuma.co products.
Extract all permutations of wood finishes, PillowBoard colours, and bed sizes mapped to their specific SKUs and prices.
Capture base prices, add-on costs for nightstands or under-bed storage, and any bundle configurations available at checkout.
Extract exact measurements, clearance heights, and weight specifications for all furniture pieces and shipping boxes.
Paginate through embedded review widgets to extract text, star ratings, verified buyer badges, and submission dates.
Monitor lead times and shipping estimates per variant to track supply chain velocity and out-of-stock windows.
Extract high-resolution image URLs for every product configuration and lifestyle shot.
Parse structured data regarding upcycled wood usage, joinery techniques, and fabric compositions.
Map primary products to their compatible accessories, tracking cross-sell pricing strategies.
Run pipelines daily to capture only what changed: new reviews, updated shipping estimates, or price adjustments.
Brief in. Clean data out.
Provide the Thuma.co categories or product URLs. We design the extraction schema together.
We configure Playwright crawlers, proxy rotation, and headless API interception for variant hydration.
Schema validation, null-rate checks, and sample review extraction before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
DTC brands use dynamic rendering and third-party APIs. Here is how we extract data reliably.
Thuma.co uses modern frontend frameworks where variants and prices load via asynchronous API calls. We run full Playwright browser sessions to trigger these state changes, capturing data that basic HTTP clients miss.
We use residential ISP proxies with realistic browser fingerprints and full cookie session management to bypass commercial bot protection layers common on high-traffic DTC sites.
Our selector strategy uses multiple fallback chains per field, intercepting backend API JSON responses directly when DOM structures change, ensuring your pipeline remains stable.
Customer reviews are often hosted by third-party providers like Yotpo or Okendo. We isolate these network requests to extract the raw review corpus without parsing complex HTML.
We maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Furniture brands track Thuma.co pricing, bundle discounts, and shipping fees to adjust their own positioning.
Design teams analyse review sentiment regarding assembly, joinery quality, and fabric durability to guide new product iterations.
Analysts monitor shipping lead times and out-of-stock statuses across variants to estimate inventory turnover and supply chain health.
Firms track DTC brand catalogue expansion, noting when new wood finishes or complementary furniture pieces are introduced.
Machine learning teams use high-quality furniture images, dimensions, and material descriptions to train interior design models.
Agencies track review volume and average ratings over time to measure the impact of marketing campaigns on customer satisfaction.
"Thuma.co holds critical pricing and configuration data for the premium DTC furniture market, but extracting it requires navigating dynamic variant rendering."
Most teams underestimate the investment required to scrape modern headless commerce sites. Reliable extraction requires residential proxies, full JavaScript rendering for variant hydration, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our thuma.co 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, variant hydration, and API interception.
We maintain pools of residential ISP proxies to bypass commercial bot protection, rotating IPs per request.
Pipelines run on AWS Lambda and ECS. 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 thuma.co scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data or circumvent authentication walls. Clients should review applicable ToS and consult legal counsel.
We use Playwright to execute JavaScript and intercept backend API responses. This allows us to extract structured JSON data directly from network requests rather than relying on brittle HTML parsing.
Yes. We iterate through all available combinations of wood finishes, fabric colours, and sizes to ensure every SKU and price point is captured.
Pipelines can be configured to run daily or weekly depending on your requirements. Change detection ensures you only process updated records.
Yes. We paginate through the review widgets to extract the full historical corpus, including ratings, text, and verified buyer status.
Yes. We provide a sample run during the pre-engagement 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 product catalogue extraction or continuous review monitoring, we scope, build, and operate the pipeline. Tell us what you need.
