We extract new bike specifications, city-level on-road pricing, used two-wheeler inventory, and dealership networks from BikeDekho. 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 Bike Specifications objects from bikedekho.com. All fields typed and schema-versioned.
"model_id": "BD-RE-CL350", "brand": "Royal Enfield", "model_name": "Classic 350", "variant_name": "Dual Channel ABS", "engine_displacement_cc": 349.34, "mileage_kmpl": 41.55, "kerb_weight_kg": 195, "emission_norm": "BS6 Phase 2"
| # | model_id | brand | model_name | variant_name | engine_displacement_cc | power_bhp |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for On-Road Pricing objects from bikedekho.com. All fields typed and schema-versioned.
"model_id": "BD-RE-CL350", "city": "Bengaluru", "ex_showroom_price": 220990.0, "rto_charges": 44198.0, "insurance_cost": 11845.0, "on_road_price": 277033.0, "emi_starting": 6345.0, "price_timestamp": "2026-05-12T09:14:00Z"
| # | model_id | variant_name | city | ex_showroom_price | rto_charges | insurance_cost |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Used Inventory objects from bikedekho.com. All fields typed and schema-versioned.
"listing_id": "UB-849210", "brand": "Honda", "model": "Activa 6G", "year_of_manufacture": 2021, "km_driven": 14500, "owner_type": "First", "asking_price": 55000.0, "location_city": "Delhi"
| # | listing_id | brand | model | year_of_manufacture | km_driven | owner_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Dealerships objects from bikedekho.com. All fields typed and schema-versioned.
"dealer_id": "DLR-BAJ-084", "dealer_name": "Khivraj Bajaj", "brand": "Bajaj", "city": "Chennai", "locality": "Adyar", "latitude": 13.0012, "longitude": 80.2565, "rating": 4.2
| # | dealer_id | dealer_name | brand | dealer_type | city | locality |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for User Reviews objects from bikedekho.com. All fields typed and schema-versioned.
"review_id": "REV-99214", "model_id": "BD-RE-CL350", "rating_overall": 4.5, "rating_comfort": 5.0, "review_title": "Perfect for long rides", "review_text": "The new J-series engine is incredibly smooth...", "review_date": "2026-04-18", "views_count": 1240
| # | review_id | model_id | user_name | rating_overall | rating_mileage | rating_comfort |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our BikeDekho scraper navigates complex technical specification tables, city-specific pricing cookies, and infinite-scroll used inventories to deliver structured automotive intelligence.
Engine details, dimensions, chassis, suspension, electricals, and features scraped across all ICE and EV variants with unified schemas.
Automated location spoofing to extract exact ex-showroom, RTO, and insurance costs across 400+ Indian cities and towns.
Monitor the secondary two-wheeler market. Track asking prices, depreciation curves, and inventory velocity across major metros.
Extract OEM showroom and service centre locations, contact details, and user ratings to map brand footprints.
Capture battery capacity, claimed range, charging time, motor power, and FAME subsidy details for the growing electric segment.
Extract the exact feature deltas between base, mid, and top variants to feed product planning and competitor analysis models.
Pull user reviews, expert verdicts, pros, cons, and granular ratings for comfort, performance, and maintenance.
Capture default loan assumptions, interest rates, and down payment requirements presented to buyers.
Extract high-resolution image URLs, 360-degree view assets, and available colour palettes per variant.
Brief in. Clean data out.
Provide specific brands, segments, target cities for pricing, or used bike filters. We design the extraction schema together.
We configure Scrapy crawlers, handle location cookies for accurate pricing, and manage pagination logic for bikedekho.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Automotive portals employ complex front-end architectures and strict rate limits. Here is how we build resilient pipelines for BikeDekho.
BikeDekho defaults to a generic location or asks for user input to display on-road prices. Our pipeline injects specific city coordinates and location cookies at the session level, iterating through a defined list of RTO codes to extract accurate local pricing.
Electric vehicles and internal combustion bikes have fundamentally different specification tables on BikeDekho. We maintain dynamic schema mappers that recognise the powertrain type and normalise the output, preventing null fields when battery specs replace engine displacement.
The used bike section relies heavily on infinite scroll and XHR requests that often truncate after a few hundred results. We intercept the underlying API calls and manipulate the offset parameters directly to ensure 100% extraction of available inventory.
Aggressive IP blocking protects BikeDekho's pricing databases. We route all requests through Indian residential proxies, matching the geographic origin to the requested pricing city to avoid triggering security anomalies.
Values like '41.55 kmpl' or '349.34 cc' are mixed with strings. Our pipeline applies regex-based cleaning post-extraction, separating numeric values from units to deliver strictly typed integers and floats ready for database ingestion.
Two-wheeler manufacturers track competitor pricing, variant launches, and feature additions across specific city markets to optimise their own positioning.
Auto finance and insurance companies ingest secondary market asking prices to build depreciation models and calculate accurate insured declared values (IDV).
Automotive strategists map the density of rival brand showrooms and service centres across tier-2 and tier-3 cities to identify expansion gaps.
Analysts track the rapidly changing electric two-wheeler landscape, comparing claimed ranges, battery capacities, and pricing against traditional ICE scooters.
Product teams aggregate thousands of user reviews to identify common mechanical faults or ergonomic complaints in competitor vehicles.
Insurance aggregators and loan providers use structured on-road price data to calculate accurate premium quotes and EMI schedules for their own users.
"BikeDekho holds the most accurate reflection of the Indian two-wheeler market - from exact local RTO taxes to real-world used bike depreciation curves."
Extracting this data requires navigating location-gated pricing, complex specification tables, and aggressive rate limits. DataFlirt manages the proxies, the JavaScript rendering, and the schema normalisation so your data science teams can focus on building valuation models and market analysis.
Everything supported by our bikedekho.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.
We use Scrapy for high-throughput crawling, heavily relying on intercepting BikeDekho's underlying APIs rather than scraping the DOM, ensuring faster and more resilient data collection.
Our infrastructure maintains persistent sessions with specific location headers and cookies, allowing parallel extraction of pricing data across hundreds of Indian cities simultaneously.
Pipelines run on AWS Lambda (burst) and ECS (sustained). 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 bikedekho.com scraping, legality, and pipeline operations.
Ask us directly →Yes. You provide a list of target cities or RTO codes, and our pipeline will iterate through them, setting the appropriate location cookies to extract the exact ex-showroom, RTO, and insurance costs for each variant in every target city.
Our extraction schema is dynamic. When the pipeline detects an electric vehicle, it maps battery capacity, claimed range, and motor power. For ICE vehicles, it maps engine displacement, cylinders, and fuel capacity, ensuring a clean, normalised dataset without schema errors.
No. BikeDekho gates seller phone numbers behind an OTP verification process. We only extract publicly visible data such as asking price, km driven, year of manufacture, and general location.
We can configure pipelines to run daily or sub-daily depending on your requirements. The used vehicle market moves quickly, so frequent delta-extractions are recommended to track inventory velocity.
Yes. We extract OEM dealership and service centre data, including addresses, latitude/longitude coordinates, brand affiliation, and publicly listed contact numbers across all available cities.
We clean unstructured text strings during the pipeline process. A field displaying '249.5 cc' on the site is delivered as a typed float '249.5' in the JSON or Parquet file, ready for immediate database ingestion.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily feed of used bike prices or a complete database of EV specifications across India - we scope, build, and operate the pipeline. Tell us what you need.
