We extract vehicle listings, Orange Book Value signals, inspection scores, and seller profiles from Droom. 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 Vehicle Listings objects from droom.in. All fields typed and schema-versioned.
"listing_id": "DRM1948291", "make": "Hyundai", "model": "Creta", "year": 2021, "trim": "SX Opt Diesel AT", "price": 1650000, "km_driven": 42500, "fuel_type": "Diesel", "transmission": "Automatic", "location": "New Delhi"
| # | listing_id | make | model | year | trim | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Orange Book Value objects from droom.in. All fields typed and schema-versioned.
"vehicle_id": "DRM1948291", "obv_good_price": 1610000, "obv_very_good_price": 1680000, "condition_metrics": "Good", "depreciation_curve": "Standard", "pricing_timestamp": "2026-05-12T10:15:00Z"
| # | vehicle_id | obv_fair_price | obv_good_price | obv_very_good_price | obv_excellent_price | condition_metrics |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for ECO Inspection objects from droom.in. All fields typed and schema-versioned.
"inspection_id": "ECO99281", "vehicle_id": "DRM1948291", "overall_score": 8.2, "engine_score": 8.5, "exterior_score": 7.9, "interior_score": 8.1, "tire_score": 7.5, "test_drive_score": 8.8
| # | inspection_id | vehicle_id | overall_score | engine_score | exterior_score | interior_score |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seller Profiles objects from droom.in. All fields typed and schema-versioned.
"seller_id": "SLR44912", "seller_name": "Delhi Motors", "seller_type": "Dealer", "pro_seller_badge": true, "active_listings": 42, "rating": 4.6, "review_count": 128, "location": "New Delhi"
| # | seller_id | seller_name | seller_type | pro_seller_badge | total_listings | active_listings |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Vehicle Specs objects from droom.in. All fields typed and schema-versioned.
"vehicle_id": "DRM1948291", "engine_cc": 1493, "max_power": "113 bhp", "max_torque": "250 Nm", "seating_capacity": 5, "boot_space": "433 Litres", "fuel_tank_capacity": "50 Litres", "ground_clearance": "190 mm"
| # | vehicle_id | engine_cc | max_power | max_torque | seating_capacity | boot_space |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Droom scraper extracts structural vehicle data, proprietary pricing models, and inspection reports across thousands of PIN codes. We handle location spoofing, API interception, and pagination limits automatically.
Capture make, model, trim, year, RTO details, and ownership history for cars, bikes, and commercial vehicles.
Extract Droom proprietary OBV pricing tiers (Fair, Good, Very Good, Excellent) to baseline market valuations.
Retrieve granular inspection scores for engine, transmission, exterior, and interior conditions.
Scrape Pro-Seller profiles, active listing counts, location data, and customer ratings.
Spoof location cookies to extract regional price variations across different Indian cities and states.
Pull deep technical metadata including engine displacement, ARAI mileage, and dimensions per trim level.
Monitor specific makes or dealer catalogues for new listings, price drops, or sold vehicles.
Capture high-resolution image URLs for exterior, interior, and documented damage points.
Extract available vehicle history report summaries including loan status and accident records.
Brief in. Clean data out.
Provide target categories, cities, makes, or dealer IDs. We design the extraction schema together.
We configure Scrapy crawlers, location cookie management, and API interception for droom.in.
Schema validation, null-rate checks, and OBV outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting accurate automotive data requires navigating complex frontend architectures and regional state management. Here is how we maintain data integrity.
Droom operates as a modern single-page application. Rather than scraping the DOM, our Playwright instances intercept the underlying network requests. This allows us to extract structured JSON responses directly, ensuring clean data for OBV and ECO scores without parsing brittle HTML.
Vehicle availability and pricing vary wildly by region. Our infrastructure injects specific geographic cookies and headers per request, allowing us to scrape the exact inventory visible to users in Delhi, Mumbai, or Bangalore simultaneously without cross-contamination.
Like many marketplaces, Droom truncates search results after a certain depth. We bypass these limits by dynamically injecting granular filters (price brackets, specific years, micro-locations) to reduce result sets below the truncation threshold, ensuring 100% catalogue coverage.
We route all traffic through Indian residential ISP proxies to avoid rate limits and IP bans. Request timing is randomised to mimic human browsing behaviour, keeping pipelines stable during high-volume daily runs.
User-generated listings often contain typos or inconsistent trim naming. We map extracted data against a normalised automotive taxonomy, ensuring 'Maruti Swift VXI' and 'Maruti Suzuki Swift VXi' resolve to the same database entity.
Dealerships and online auto platforms ingest OBV data to price their own inventory competitively.
Actuaries use depreciation curves and condition metrics to refine vehicle valuation models for total loss claims.
Analysts track inventory velocity, average days on market, and popular trim levels across different Indian states.
Machine learning teams use vehicle images, descriptions, and inspection scores to train computer vision models for damage detection.
NBFCs cross-reference requested loan amounts against scraped OBV and ECO inspection data to assess collateral risk.
OEMs monitor the used market to understand long-term resale value and depreciation rates of their fleet compared to rivals.
"Automotive pricing is highly regional and condition-dependent. Droom holds the deepest inspection and valuation dataset in India, providing signals you cannot find anywhere else."
Extracting this data requires handling complex single-page applications, regional cookie management, and intercepting undocumented APIs. DataFlirt manages the proxy rotation and schema maintenance so your data engineering team can focus on building valuation models rather than fixing broken scrapers.
Everything supported by our droom.in 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 retry logic. Playwright manages JavaScript rendering and API interception to capture structured data directly from network requests.
We maintain pools of Indian residential proxies. Rotation happens per-request while maintaining the specific geographic cookies required for accurate regional pricing.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state is stored in managed Postgres to track inventory changes.
Data delivered to where your team already works — no new tooling required.
About droom.in scraping, legality, and pipeline operations.
Ask us directly →Yes. We can extract the complete OBV pricing matrix including Fair, Good, Very Good, and Excellent tiers for any given make, model, year, and condition combination available on the platform.
Our infrastructure injects the appropriate geographic cookies and headers before loading the page or hitting the API. This ensures we capture the exact price and availability for the target RTO or city.
Yes. We extract the overall ECO score along with the granular sub-scores for engine, exterior, interior, and test drive metrics, plus any available inspector notes.
We can configure pipelines to run daily or weekly depending on your requirements. Our change-detection system ensures you only process new listings, price drops, or vehicles marked as sold.
We extract publicly visible seller details such as dealership name, location, and Pro-Seller status. We do not bypass OTP walls to extract private phone numbers.
Yes. We map raw listing strings against a standard automotive taxonomy, ensuring messy user input is converted into clean, queryable make, model, and trim fields.
Our smallest packages start at tracking specific categories or cities with weekly delivery. Contact us with your use case for a scoped quote based on volume and frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off extraction of the entire used car catalogue or a continuous feed of OBV pricing updates, we scope, build, and operate the pipeline. Tell us what you need.
