We extract product catalogues, finish variations, pricing signals, spec sheets, and reviews from Build.com. 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 Product Specs objects from build.com. All fields typed and schema-versioned.
"sku": "K-3999-0", "title": "Highline Comfort Height Two-Piece Elongated Toilet", "brand": "Kohler", "manufacturer_model": "3999-0", "category": "Bathroom", "finish": "White", "material": "Vitreous China", "spec_sheet_url": "https://s1.img-b.com/build.com/mediabase/specifications/kohler/12345/k-3999-spec.pdf"
| # | sku | title | brand | manufacturer_model | category | sub_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Inventory objects from build.com. All fields typed and schema-versioned.
"sku": "K-3999-0", "price": 314.25, "retail_price": 419.0, "discount_pct": 25, "in_stock": true, "lead_time_days": 2, "shipping_cost": 0.0, "currency": "USD", "price_timestamp": "2026-05-12T09:14:00Z"
| # | sku | price | retail_price | discount_pct | in_stock | lead_time_days |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from build.com. All fields typed and schema-versioned.
"review_id": "REV-982374", "sku": "K-3999-0", "rating": 5, "review_title": "Excellent flush power", "review_body": "Installed this in our guest bath. Very clean look and flushes perfectly.", "review_date": "2026-04-18", "verified_buyer": true, "recommended": true
| # | review_id | sku | reviewer_name | rating | review_title | review_body |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Variants & Finishes objects from build.com. All fields typed and schema-versioned.
"parent_sku": "K-3999", "variant_sku": "K-3999-96", "finish_name": "Biscuit", "price_modifier": 45.0, "stock_status": "In Stock", "upc": "885612345678", "collection_name": "Highline"
| # | parent_sku | variant_sku | finish_name | finish_image_url | price_modifier | stock_status |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Search Results objects from build.com. All fields typed and schema-versioned.
"keyword": "kitchen faucet", "position": 1, "sku": "MZ-4567-CH", "brand": "Moen", "price": 249.99, "rating": 4.8, "review_count": 1432, "best_seller_badge": true, "scraped_at": "2026-05-12T09:14:33Z"
| # | keyword | position | sku | title | brand | price |
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Our Build.com scraper handles every layer of the platform: product specifications, finish variations, dynamic pricing, and inventory levels across the Ferguson network, with anti-bot circumvention built in.
Title, brand, manufacturer model, dimensions, weight, material, and every specification field Build.com surfaces, scraped at the SKU level.
Extract all colour and finish variations for a given product, capturing specific pricing, stock status, and imagery for each variant.
Capture base price, retail price, discount percentages, and clearance badges, timestamped per crawl.
Monitor stock availability, estimated lead times, and shipping costs across the Ferguson distribution network.
Capture direct URLs to PDF specification sheets, installation guides, and warranty documents linked on product pages.
Full review text, star ratings, helpful vote counts, verified buyer flags, and recommendation status, paginated across all reviews.
Group SKUs by brand and specific collections to map out complete hardware suites and product families.
Track organic position for any keyword or category page, capturing best seller badges and filter parameters.
Run one-off bulk exports or configure continuous pipelines at hourly or daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide SKU lists, category URLs, keyword sets, or brand names. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for build.com.
Schema validation, null-rate checks, price-outlier detection, and sample variants before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Build.com uses aggressive bot protection and heavily nested variant structures. Here is how we maintain data integrity.
Build.com actively blocks data center IPs and headless browsers. Our crawlers use US-based residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management.
Hardware and plumbing fixtures often have dozens of finish and size combinations, each with distinct pricing and stock. We execute full Playwright sessions to trigger these state changes and capture the true variant data.
Product specification tables vary wildly between categories. Our extraction logic normalises these nested tables into a flat, predictable schema, using fallback chains to ensure data flows even when the DOM changes.
For large hardware catalogues, we maintain a hash index of last-seen values per SKU. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, and coverage drops, and respond before you notice.
Home improvement retailers and distributors monitor pricing and clearance events to optimise their own pricing strategies.
Hardware brands audit Build.com listings to ensure Minimum Advertised Price compliance and track unauthorised discounting.
Analysts track brand representation, category saturation, and finish trends to identify consumer preferences.
Manufacturers compare their product specifications, warranties, and pricing against competing brands in the same category.
Supply chain teams correlate review velocity and stock depth indicators with sales trends to improve procurement models.
ML teams use structured hardware catalogues and specification sheets to train domain-specific recommendation engines.
"Build.com holds the most structured hardware and plumbing catalogue available online, but extracting the nested finish and spec data requires serious infrastructure."
Most teams underestimate the investment required: reliable Build.com scraping requires residential proxies, full JavaScript rendering for variant matrices, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our build.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, cookie sessions, and interaction flows for complex variant matrices.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request with sticky sessions where required to bypass aggressive bot protection.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About build.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Build.com 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 terms of service and consult legal counsel for specific use cases.
We use US residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for rate spikes in real time and trigger pool rotation automatically.
No. Trade Pro pricing is gated behind authenticated contractor accounts. We only extract public retail pricing and publicly visible discounts.
We execute JavaScript to trigger the state changes for each finish and size combination on a product page, capturing the specific price, stock status, and image URL for every variant under the parent SKU.
Real-time streaming pipelines achieve sub-60-minute latency for price and availability signals on a defined SKU set. Full category refreshes at daily cadence complete within a 6-12 hour window depending on size.
Yes. We extract the direct URLs to the PDF specification sheets, installation guides, and warranty documents linked on the product pages.
Our smallest packages start at a defined SKU list or category set with weekly delivery. For larger catalogues or custom schema requirements, we price based on volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous price-monitoring feed across 500K SKUs, we scope, build, and operate the pipeline. Tell us what you need.
