We extract local business profiles, service areas, user reviews, and ratings from Sulekha. 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 Business Profiles objects from sulekha.com. All fields typed and schema-versioned.
"business_id": "B-4928104", "business_name": "Urban Cleaners & Pest Control", "sulekha_score": 8.4, "primary_category": "Pest Control Services", "city": "Bengaluru", "verified_badge": true, "year_established": 2015
| # | business_id | business_name | sulekha_score | primary_category | sub_categories | city |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Services & Pricing objects from sulekha.com. All fields typed and schema-versioned.
"business_id": "B-4928104", "service_name": "Termite Control", "service_category": "Pest Control", "starting_price": 1200.0, "price_unit": "INR", "booking_type": "On-Demand", "warranty_provided": true
| # | business_id | service_name | service_category | starting_price | price_unit | service_description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from sulekha.com. All fields typed and schema-versioned.
"review_id": "REV-9923841", "business_id": "B-4928104", "rating": 4.5, "review_text": "Arrived on time and cleared the termite issue completely.", "date_posted": "2025-11-12", "helpful_votes": 14, "service_availed": "Termite Control"
| # | review_id | business_id | user_name | rating | review_text | date_posted |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Location & Contact objects from sulekha.com. All fields typed and schema-versioned.
"business_id": "B-4928104", "locality": "Koramangala", "city": "Bengaluru", "state": "Karnataka", "pincode": "560034", "public_phone": "+91-9876543210", "latitude": 12.9279
| # | business_id | street_address | locality | city | state | pincode |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from sulekha.com. All fields typed and schema-versioned.
"keyword": "pest control", "city": "Bengaluru", "rank_position": 3, "business_id": "B-4928104", "sulekha_score": 8.4, "sponsored_badge": false, "scraped_at": "2026-02-14T10:15:30Z"
| # | keyword | city | rank_position | business_id | business_name | sulekha_score |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Sulekha holds dense data on local service providers across India. We extract profiles, service lists, and reviews while handling location-based routing and pagination limits.
Business name, Sulekha score, verified status, year established, and description extracted at the profile level.
Capture exact street addresses, localities, pincodes, and geographic coordinates for spatial analysis.
Extract full review text, star ratings, helpful votes, and business responses to analyse service quality.
Extract publicly visible phone numbers, website URLs, and operating hours for lead generation.
Map businesses to primary and secondary service categories according to Sulekha's taxonomy.
Extract starting prices, service descriptions, and warranty details where listed by the provider.
Track organic versus sponsored positions for specific service keywords across different cities.
Run extractions across Tier 1, 2, and 3 cities in India using location-specific headers and cookies.
Identify new business registrations and track changes in Sulekha scores or review counts over time.
Brief in. Clean data out.
Provide target cities, service categories, or specific keywords. We map the extraction requirements.
We configure Scrapy crawlers, location-aware proxies, and JavaScript rendering for dynamic content.
Schema validation, null-rate checks, and data normalisation before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting data from Sulekha requires handling location-based dynamic routing, JavaScript-rendered contact details, and pagination walls.
Sulekha routes traffic and displays results based on IP geolocation and cookies. We use residential Indian proxies mapped to specific cities and inject precise location headers to extract accurate local SERPs.
Phone numbers and certain profile details often require user interaction or JavaScript execution to load. We use Playwright to trigger these elements and capture the underlying data.
Directory search results are often capped at a certain number of pages. We bypass these limits by iterating through granular sub-categories and micro-localities to ensure complete category coverage.
Local business addresses are frequently entered in non-standard formats. We parse and normalise street addresses, localities, and pincodes into structured database columns.
We distribute requests across thousands of IPs and introduce randomised delays to mimic human browsing behaviour, preventing 403 blocks and IP bans.
Sales teams extract newly registered businesses and contact details to build targeted outreach lists for software and financial products.
Analysts map service provider density across cities to identify underserved localities and demand trends.
Local service aggregators track competitor pricing, service offerings, and customer sentiment to optimise their own operations.
Reputation management platforms aggregate Sulekha reviews to provide businesses with a unified view of customer feedback.
Agencies track business visibility and keyword rankings on Sulekha to optimise local search performance for their clients.
Fintech lenders use business age, review volume, and Sulekha scores as alternative signals for SMB underwriting.
"Sulekha maps the fragmented landscape of Indian local services. Accessing this data at scale transforms raw directory listings into actionable market intelligence."
Building a reliable pipeline for Sulekha requires handling dynamic location routing, JavaScript-heavy pages, and strict rate limits. DataFlirt manages the proxy rotation, DOM parsing, and infrastructure, delivering clean, structured business data directly to your warehouse.
Everything supported by our sulekha.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 and deduplication. Playwright executes JavaScript to reveal contact details and handle dynamic routing.
We maintain pools of residential Indian IPs to ensure accurate geographic routing and bypass regional rate limits.
Pipelines run on AWS Lambda and ECS. Airflow manages scheduling and dependency trees, with all state stored in managed PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About sulekha.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Sulekha is generally permissible under Indian law. DataFlirt extracts only public business profiles, reviews, and visible contact details. We do not bypass authentication to access private leads or user accounts.
Yes, we extract phone numbers that are publicly listed on the business profile. We use headless browsers to trigger the JavaScript events required to reveal masked contact details.
We use Indian residential proxies and inject specific location headers and cookies for each request. This ensures the data reflects exactly what a user in that specific city or locality would see.
We can configure pipelines to run daily, weekly, or monthly depending on your requirements. Delta updates ensure you receive new businesses and review changes quickly.
Yes. We paginate through the entire review history of a business profile, capturing the text, rating, date, and any responses from the business owner.
Our minimum engagement typically starts at 10,000 business profiles or a specific set of categories and cities. We price based on data volume, update frequency, and pipeline complexity.
We deliver the raw category and sub-category strings as they appear on Sulekha. You can apply your own normalisation logic downstream, or we can build custom transformation steps into the pipeline for an additional fee.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full category dump or continuous monitoring of local competitors — we scope, build, and operate the pipeline. Tell us what you need.
