We extract comprehensive spec sheets, 91scores, multi-store pricing, and benchmark data from 91Mobiles. 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 Device Specs objects from 91mobiles.com. All fields typed and schema-versioned.
"device_id": "apple-iphone-15-pro", "brand": "Apple", "model": "iPhone 15 Pro", "processor": "Apple A17 Pro", "ram": "8 GB", "storage": "256 GB", "display_size": "6.1 inches", "battery_capacity": "3274 mAh"
| # | device_id | brand | model | form_factor | operating_system | processor |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Offers objects from 91mobiles.com. All fields typed and schema-versioned.
"device_id": "apple-iphone-15-pro", "platform": "Amazon", "price": 129900.0, "list_price": 134900.0, "stock_status": "In Stock", "bank_discount": "HDFC Credit Card EMI", "scraped_at": "2026-05-12T09:14:00Z"
| # | device_id | variant | colour | platform | seller_name | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Scores & Benchmarks objects from 91mobiles.com. All fields typed and schema-versioned.
"device_id": "apple-iphone-15-pro", "91score": 94, "expert_score": 9.0, "user_score": 4.6, "antutu_score": 1524389, "geekbench_single": 2914, "geekbench_multi": 7199, "dxomark_camera": 154
| # | device_id | 91score | expert_score | user_score | antutu_score | geekbench_single |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Verdicts objects from 91mobiles.com. All fields typed and schema-versioned.
"device_id": "apple-iphone-15-pro", "author": "91Mobiles Expert Team", "expert_rating": 9.0, "verdict": "The iPhone 15 Pro is a powerhouse with a titanium build and stellar cameras.", "pros": "['Titanium build', 'Excellent cameras', 'A17 Pro performance']", "cons": "['Slow charging speeds', 'Expensive']", "publish_date": "2023-09-21"
| # | device_id | review_url | author | publish_date | expert_rating | verdict |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Upcoming & Rumours objects from 91mobiles.com. All fields typed and schema-versioned.
"device_id": "samsung-galaxy-s25-ultra", "brand": "Samsung", "model": "Galaxy S25 Ultra", "expected_price": 134999.0, "expected_launch_date": "Jan 2025", "launch_status": "Rumoured", "probability_score": 85, "last_updated": "2024-11-12T10:00:00Z"
| # | device_id | brand | model | expected_price | expected_launch_date | launch_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our 91Mobiles scraper handles every layer of the platform: deep spec sheets, dynamic cross-store pricing widgets, benchmark scores, and expert reviews - with JavaScript rendering and anti-bot circumvention built in.
Processor, RAM, display tech, battery capacity, camera sensors, and every granular row in the detailed specification tables.
Extract pricing data from the dynamic comparison widgets showing Amazon, Flipkart, Croma, and Reliance Digital rates.
Capture the proprietary 91score alongside AnTuTu, Geekbench, and component-specific ratings for performance and camera.
Extract expert review text, pros, cons, verdicts, and sub-category ratings for battery, display, and design.
Monitor the upcoming phones section for expected launch dates, rumoured prices, and leaked specifications.
Map base models to their specific storage, RAM, and colour variants with associated price differentials.
Extract high-resolution image gallery URLs and 3D render assets associated with each device profile.
Collect user rating distributions, total review counts, and sentiment indicators from the community.
Run one-off bulk exports or configure continuous pipelines at daily cadences with change-detection diffing for price drops.
Brief in. Clean data out.
Provide brand lists, category URLs, or price brackets. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and widget rendering for 91mobiles.com.
Schema validation, null-rate checks, spec normalisation, and sample reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
91Mobiles employs aggressive caching and bot protection. Here is how we stay resilient - and why teams choose managed infrastructure over DIY.
91Mobiles routes traffic through aggressive CDN and bot protection layers. Our crawlers use residential ISP proxies with realistic browser fingerprints and TLS spoofing to bypass Cloudflare challenges without triggering blocks.
Cross-platform price comparison widgets load dynamically via AJAX after the initial page load. We run full Playwright browser sessions to trigger lazy-loading and capture the populated price data that static HTTP clients miss.
Specification tables vary wildly between smartphones, laptops, and wearables. Our extraction engine uses intelligent text-pattern matching and semantic parsing to normalise spec attributes, regardless of layout variations.
For large gadget catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs for price updates or spec corrections, reducing downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes in critical fields like processor or battery capacity, ensuring data completeness.
Smartphone OEMs and hardware brands track rival specifications, launch timelines, and pricing strategies.
Electronics retailers monitor cross-platform price parity to adjust their own discounting and bank offer strategies.
Analysts track feature adoption trends, such as fast-charging wattage or camera megapixel counts, across different price tiers.
Publishers build their own price comparison engines and recommendation tools using normalised spec data.
Machine learning teams use structured gadget specifications and expert review text to train domain-specific LLMs.
Supply chain analysts correlate 91scores and user rating velocity with expected sales volumes to optimise inventory.
"91Mobiles holds the most structured gadget specification and pricing dataset in India - but extracting it cleanly requires navigating complex dynamic widgets and strict bot protection."
Most teams underestimate the investment required: reliable 91Mobiles scraping requires residential proxies, full JavaScript rendering for price widgets, CAPTCHA handling, and daily selector maintenance for shifting spec tables. DataFlirt absorbs that complexity so your engineers can focus on the analysis - not the infrastructure.
Everything supported by our 91mobiles.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, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across IN regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
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 91mobiles.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from 91Mobiles is generally permissible under applicable law in India. DataFlirt targets only public, non-authenticated device specifications, pricing, and review data. We do not extract personal data or circumvent authentication walls.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour to bypass Cloudflare and rate-limiting systems.
We support mobile phones, laptops, tablets, smartwatches, fitness bands, televisions, and audio devices. The schema adapts to the specific attributes of each category.
Full catalogue refreshes at daily cadence complete within a 6-12 hour window depending on size. Faster cadences can be configured for a subset of highly tracked devices.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per device for multi-store pricing from the date your pipeline starts.
Our smallest packages start at a defined brand or category list with weekly delivery. For full catalogue extraction, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 devices as part of the pre-engagement scoping process, so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off gadget catalogue dump or a continuous price-monitoring feed across categories - we scope, build, and operate the pipeline. Tell us what you need.
