We extract part catalogues, OEM numbers, vehicle compatibility matrices, and pricing from Boodmo. 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 Part Details objects from boodmo.com. All fields typed and schema-versioned.
"part_number": "58101M1A00", "oen": "58101-M1A00", "title": "Front Brake Pad Set", "brand": "Hyundai", "category": "Brakes", "sub_category": "Brake Pads", "origin": "OEM", "weight": "1.2 kg"
| # | part_number | oen | title | brand | category | sub_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Offers objects from boodmo.com. All fields typed and schema-versioned.
"part_number": "58101M1A00", "price": 2450.0, "mrp": 2800.0, "currency": "INR", "discount_pct": 12.5, "dispatch_days": 2, "seller_name": "AutoParts India", "in_stock": true
| # | part_number | price | mrp | currency | discount_pct | delivery_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Compatibility Matrix objects from boodmo.com. All fields typed and schema-versioned.
"part_number": "58101M1A00", "vehicle_make": "Hyundai", "vehicle_model": "Creta", "engine_type": "1.5L CRDi", "fuel_type": "Diesel", "production_year_start": 2020, "production_year_end": 2023, "variant": "SX Opt"
| # | part_number | vehicle_make | vehicle_model | engine_type | fuel_type | production_year_start |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Substitutes & Aftermarket objects from boodmo.com. All fields typed and schema-versioned.
"part_number": "0986494833", "original_oen": "58101-M1A00", "substitute_brand": "Bosch", "substitute_part_number": "0986494833", "price_diff_pct": -35.0, "in_stock": true, "rating": 4.6
| # | part_number | original_oen | substitute_brand | substitute_part_number | price_diff_pct | compatibility_score |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Category & Search objects from boodmo.com. All fields typed and schema-versioned.
"keyword": "brake pads", "category_path": "Brakes > Brake Pads", "position": 1, "part_number": "58101M1A00", "title": "Front Brake Pad Set", "price": 2450.0, "brand": "Hyundai", "scraped_at": "2026-05-12T09:14:33Z"
| # | keyword | category_path | position | part_number | title | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Boodmo scraper navigates deep category trees, XHR-loaded compatibility matrices, and aftermarket cross-references to extract clean automotive data.
Capture primary part numbers, OENs, and manufacturer codes across all listed components.
Extract make, model, year, and engine variant matrices for every part to build comprehensive fitment databases.
Monitor MRP, selling price, and discount percentages dynamically updated by sellers.
Map OEM parts to aftermarket alternatives with price differentials and availability status.
Extract seller ratings, origin locations, dispatch timelines, and return policies per listing.
Navigate the complete category tree from primary system down to specific sub-assemblies.
Capture high-resolution part images and schematic diagrams linked to specific part numbers.
Track real-time inventory status and dispatch estimates across multiple third-party sellers.
Run daily diffs to track pricing changes, stock fluctuations, and new catalogue additions.
Brief in. Clean data out.
Provide category URLs, make/model parameters, or OEN lists. We design the extraction schema together.
We configure Scrapy crawlers, intercept XHR responses for compatibility data, and manage rate limits.
Schema validation, null-rate checks, and cross-reference verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting deep automotive catalogues requires handling complex XHR payloads, strict rate limits, and nested category structures.
Boodmo enforces strict rate limits and IP reputation checks. Our crawlers use India-based residential ISP proxies with randomised request timing to prevent IP bans and maintain high throughput.
Automotive parts catalogues are deeply nested. We map the entire taxonomy tree systematically, ensuring no sub-category or variant is missed during full catalogue crawls.
Vehicle fitment data and substitute parts often load dynamically via XHR. We intercept these backend API payloads directly, bypassing DOM parsing for faster and more accurate extraction.
E-commerce platforms update their DOM structures frequently. Our selector strategy uses multiple fallback chains so a layout change does not break your data pipeline overnight.
For massive catalogues, we maintain a hash index of last-seen values. Subsequent runs only push diffs for updated prices or stock, reducing compute cost and downstream processing load.
Aftermarket brands and retailers monitor OEM pricing and discounts to optimise their own pricing strategies.
Parts manufacturers map their inventory to OEM numbers and competitor parts to expand their compatibility databases.
B2B auto repair platforms populate their internal catalogues with accurate OENs and fitment data.
Insurtech companies use real-time spare part pricing to automate and validate damage repair estimates.
Distributors track dispatch timelines and seller origin locations to identify supply gaps in specific regions.
Analysts track new part additions and brand availability to monitor market share across automotive segments.
"Boodmo holds the most comprehensive vehicle compatibility matrix and OEM cross-reference database in India, but accessing it requires custom pipeline infrastructure."
Most teams underestimate the complexity of automotive catalogues. Extracting reliable compatibility matrices and OEN mapping requires handling deep navigation trees, dynamic variant loading, and strict rate limits. DataFlirt absorbs that complexity so your engineers can focus on analysis.
Everything supported by our boodmo.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 and interaction flows when required.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required to avoid rate limits.
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 boodmo.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We intercept the backend XHR payloads to extract the complete list of compatible makes, models, years, and engine variants for any given part number.
Yes. We extract substitute part listings and map them to the original OEN, including brand names, substitute part numbers, and price differentials.
We can configure pipelines to run daily or weekly diffs on a targeted list of part numbers to track dynamic pricing and inventory changes.
Yes. We capture the URLs for high-resolution part images and schematic diagrams available on the listing pages.
We use India-based residential ISP proxies with intelligent request throttling and randomised delays to avoid triggering rate limits or IP bans.
No. We only target public, non-authenticated catalogue data. We do not scrape user-specific garage data or gated B2B wholesale pricing.
Yes. We provide a sample run of up to 500 part numbers as part of the pre-engagement scoping process to validate schema fit and data quality.
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 1M parts - we scope, build, and operate the pipeline. Tell us what you need.
