We extract cross-category listings, pricing signals, seller profiles, and location hierarchies from Quikr. 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 General Listings objects from quikr.com. All fields typed and schema-versioned.
"ad_id": "384910294", "title": "Sofa Set 3+1+1 Good Condition", "category": "Furniture", "sub_category": "Sofas", "price": 8500.0, "city": "Bengaluru", "posted_date": "2026-05-11T14:22:00Z", "seller_type": "Individual"
| # | ad_id | title | category | sub_category | price | location |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for QuikrHomes objects from quikr.com. All fields typed and schema-versioned.
"property_id": "H9283741", "title": "3 BHK Apartment for Sale in Indiranagar", "property_type": "Apartment", "bhk": 3, "price": 25000000.0, "area_sqft": 1850, "furnishing": "Semi-Furnished", "location": "Indiranagar"
| # | property_id | title | property_type | bhk | price | area_sqft |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for QuikrCars objects from quikr.com. All fields typed and schema-versioned.
"ad_id": "C4829103", "make": "Hyundai", "model": "i20 Asta", "year": 2019, "km_driven": 42500, "fuel_type": "Petrol", "transmission": "Manual", "price": 680000.0
| # | ad_id | make | model | year | km_driven | fuel_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for QuikrJobs objects from quikr.com. All fields typed and schema-versioned.
"job_id": "J9284711", "title": "Delivery Executive", "company": "Logistics Pro", "role": "Delivery Boy", "experience_min": 0, "experience_max": 2, "salary_min": 15000, "salary_max": 22000
| # | job_id | title | company | role | experience_min | experience_max |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seller Profiles objects from quikr.com. All fields typed and schema-versioned.
"seller_id": "U8472910", "name": "Rahul Sharma", "member_since": "2021-03-14", "total_ads": 42, "active_ads": 3, "verified_email": true, "verified_phone": true, "seller_rating": 4.2
| # | seller_id | name | member_since | total_ads | active_ads | verified_email |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Quikr operates distinct verticals with entirely different DOM structures. Our scraper maps QuikrHomes, QuikrCars, and general classifieds into normalised, queryable schemas.
Capture property type, BHK, square footage, furnishing status, floor details, and exact geolocations for real estate listings.
Extract make, model, year, mileage, transmission, ownership history, and registration details for used vehicles.
Scrape salary brackets, experience requirements, role definitions, and hiring company details across local job markets.
Execute click-to-reveal interactions for phone numbers and email addresses, handling the associated rate limits and CAPTCHAs.
Normalise city, locality, and sub-locality data to build accurate geographic distributions of inventory.
Track seller history, active ad counts, verification badges, and account age to distinguish dealers from individual sellers.
Monitor asking prices, historical price drops, and negotiable flags across all active classified listings.
Capture high-resolution image URLs and associated alt-text for visual machine learning models or verification.
Maintain state to capture only newly posted ads, price changes, or delisted items without re-scraping the entire city catalogue.
Brief in. Clean data out.
Specify categories, cities, or specific search queries. We establish the extraction schema per vertical.
We configure category-specific Scrapy spiders, handle location cookies, and bypass contact-reveal rate limits.
Schema validation, null-rate checks on critical fields like price, and normalisation of unstructured seller descriptions.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Classified sites present unique scraping challenges: unstructured user inputs, aggressive bot protection on contact details, and infinite scroll pagination.
Quikr hides seller phone numbers behind network requests that require valid session tokens and frequently trigger CAPTCHAs. We use Playwright to execute these interactions, solve the challenges via 2Captcha, and extract the raw contact data.
A sofa listing has different metadata than a used car. Our pipeline detects the category context and applies the correct extraction schema, preventing null-field bloat and ensuring your database receives cleanly typed records.
Quikr caps pagination on broad searches (e.g., 'All Cars in Mumbai'). We programmatically slice searches by micro-localities, price brackets, and specific models to extract the entire catalogue without hitting hard pagination walls.
Quikr relies heavily on cookies to set the user's city context. Our scrapers manage persistent cookie jars per worker, ensuring that requests for Bengaluru real estate don't accidentally return results for Delhi.
Users frequently dump specifications into the main description box rather than using structured fields. We apply regex patterns to extract hidden phone numbers, exact dimensions, and unlisted features from raw text blobs.
PropTech companies aggregate QuikrHomes listings to build comprehensive market maps, track rental yields, and identify undervalued properties.
Auto marketplaces ingest QuikrCars data to train depreciation curves and algorithmic pricing models for used vehicles.
B2B service providers extract newly registered businesses and service listings to build targeted outreach lists.
Recruitment agencies track QuikrJobs to monitor blue-collar and grey-collar hiring trends, salary benchmarks, and competitor activity.
Marketplaces analyse seller profiles and inventory volume to distinguish professional brokers from individual sellers.
Financial institutions track duplicate listings, suspicious price anomalies, and blacklisted phone numbers across classified networks.
"Quikr holds a massive, fragmented dataset of India's informal economy and secondary markets — extracting it requires handling extreme schema variance across categories."
Scraping classifieds introduces unique entropy: inconsistent user inputs, dynamic click-to-reveal contact endpoints, and aggressive rate limiting on high-value categories like real estate. DataFlirt normalises this chaos into structured tables so your engineers avoid writing custom parsers for every sub-category.
Everything supported by our quikr.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.
Our Scrapy architecture uses dynamic item loaders that inspect the target URL and automatically route the response to the correct parsing schema for that specific Quikr vertical.
Playwright workers handle complex state machines for contact reveals, managing cookies, executing JavaScript, and routing CAPTCHA challenges to external solvers without blocking the main crawl.
Raw text fields are passed through Python regex and formatting pipelines before database insertion, ensuring dates, currency values, and phone numbers meet strict warehouse schema types.
Data delivered to where your team already works — no new tooling required.
About quikr.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We automate the click-to-reveal process required to view contact details. Note that this process is heavily rate-limited by Quikr and often triggers CAPTCHAs, which we handle via residential proxies and automated solvers.
Yes. We maintain specific schemas for major verticals like QuikrHomes, QuikrCars, and QuikrJobs, as well as a generalised schema for standard classifieds like electronics and furniture.
Quikr uses cookies to manage city context. Our scrapers are configured to inject the correct location cookies per request, ensuring we can scrape inventory across multiple cities simultaneously without cross-contamination.
Yes. By running continuous pipelines against a known set of ad IDs or seller profiles, we can detect 404s or 'Sold' status changes and emit state-change events to your warehouse.
For targeted localities or specific search parameters, we can run pipelines at hourly intervals. Full-city catalogue refreshes typically run on a 24-hour cadence due to total page volume.
We capture the current price at the time of the scrape. By maintaining a continuous pipeline, we build a time-series history of price drops or increases for long-standing listings from the day your pipeline activates.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily feed of real estate listings or continuous lead generation from local services — we build and operate the infrastructure. Tell us your target categories.
