Frequently Asked Questions
We automate the boring stuff so u can scale. Got questions? We got answers.
This is your go-to guide for everything web scraping in India. No cap, we cover it all.
Legal & Compliance in India
Yes, web scraping is legal in India when done right. The Digital Personal Data Protection (DPDP) Act 2023 regulates how personal data is processed, but it doesn't ban scraping public data. If the data is publicly available on a website without any authentication or paywall, you're generally good to go. The key is to respect privacy - we never scrape personal info like emails, phone numbers, or addresses unless it's explicitly public business contact info.
We follow a strict compliance framework: we check robots.txt files, respect rate limits, and only scrape data that's accessible without login. The DPDP Act focuses on consent and data processing principles, so as long as you're not scraping personal data without consent or violating terms of service, you're operating within legal boundaries. Our clients include NGOs, research institutions, and businesses who need public data for legitimate purposes.
Bottom line: scraping public data for business intelligence, research, or competitive analysis is legal. Just don't scrape private data, don't bypass authentication, and always respect the website's terms. We've been doing this for years with zero legal issues because we play by the rules.
Short answer: yes, you should. Robots.txt is the standard way websites communicate which parts they don't want bots to access. While it's not legally binding in India, ignoring it can get your IP blocked or lead to legal trouble under the Information Technology Act. We always check robots.txt before starting any scraping project - it's just good practice and shows respect for the website's policies.
Here's how we handle it: we parse the robots.txt file, identify disallowed paths, and avoid scraping those areas. If a website has a blanket disallow for all bots, we reach out directly to get permission. Many sites are actually cool with scraping if you're transparent about your intentions and don't hammer their servers. We've built relationships with several e-commerce and travel platforms by being upfront about our data needs.
Pro tip: some sites use robots.txt to hide their API endpoints or admin panels, not because they don't want legitimate scrapers. We use common sense - if it's public product data, we scrape it. If it's behind a login or clearly marked as private, we stay away. This approach keeps us safe and our clients happy.
Generally, no. Scraping data behind authentication or paywalls crosses into gray territory and can violate terms of service, copyright laws, and computer fraud statutes. The Computer Fraud Act in India (Section 66 of the IT Act) can be invoked if you're accessing systems without authorization. We don't touch paywalled content or require users to provide login credentials - that's a hard boundary for us.
However, there are legitimate exceptions. If you have your own account with legitimate access (like a business subscription), scraping your own data is usually fine. Some clients ask us to scrape their own dashboards or internal tools - we do this by having them provide API access or export functionality, not by logging in with their credentials. We never store or reuse any authentication tokens.
If you need data that's behind a login, the best approach is to contact the website directly and ask for API access or a data partnership. Many platforms offer official APIs for businesses. We've helped clients negotiate data access agreements that are legal, scalable, and way more reliable than scraping.
GDPR applies if you're processing data of EU citizens, regardless of where your business is located. Even though we're based in India, many of our clients serve global markets, so we bake GDPR compliance into everything we do. The key principles are: data minimization (only scrape what you need), purpose limitation (use data only for stated purposes), and transparency (be clear about your data sources).
We implement several GDPR-friendly practices: we anonymize personal identifiers, we don't store raw data longer than necessary, and we provide data processing agreements (DPAs) to clients who need them. When scraping EU websites, we're extra careful about personal data - we exclude names, emails, and other PII unless it's clearly public business information.
For clients targeting EU markets, we recommend conducting a Data Protection Impact Assessment (DPIA) before starting large-scale scraping projects. We can help you document your data sources, processing activities, and compliance measures. It's not just about avoiding fines - it's about building trust with your customers and partners.
Yes, you can use scraped public data for commercial purposes in most cases. Facts and data aren't copyrightable - it's the creative expression that's protected. So scraping product prices, stock data, or public business listings and using them for commercial analysis is generally fine. Many businesses, including Google and Amazon, built their empires on scraped data.
However, there are nuances. You can't republish scraped content verbatim if it's creative work (like articles, reviews, or images). You also need to be careful about terms of service - some sites explicitly prohibit commercial use of scraped data. We always review the target website's terms and advise clients on the best approach.
The safest commercial use case is data analysis and insights. Instead of republishing scraped data, use it to power your internal dashboards, inform business decisions, or create derivative products. This is what most of our clients do - they scrape competitor pricing, analyze market trends, and build better products based on public data insights.
Technical Bypass & Anti-Bot
Cloudflare is the biggest challenge in modern scraping, but we've developed a multi-layered approach to handle it. First, we use residential proxies that rotate IP addresses to avoid detection. Cloudflare flags data center IPs, so residential proxies that look like real user connections are essential. We also implement browser fingerprinting evasion - randomizing user agents, screen resolution, timezone, and other browser characteristics.
For tougher protections, we use headless browsers like Puppeteer or Playwright that can execute JavaScript and handle Cloudflare's JavaScript challenges. We've built custom wait strategies that detect when Cloudflare's challenge page appears and wait for it to complete before proceeding. For the hardest cases, we integrate with third-party CAPTCHA solving services that use human workers or AI to solve challenges in real-time.
The key is persistence and adaptation. Cloudflare constantly updates their protection, so we continuously monitor and update our bypass techniques. We maintain a pool of tested proxies, rotate user agents, and implement smart retry logic. Our success rate against Cloudflare-protected sites is over 95%, and we're always improving.
We handle all major CAPTCHA types with high success rates. For text-based CAPTCHAs (reCAPTCHA v2), we use OCR (Optical Character Recognition) combined with machine learning models trained on millions of CAPTCHA images. Our success rate for text CAPTCHAs is around 85-90%, which is solid for most use cases.
For image selection CAPTCHAs (like "select all traffic lights"), we use computer vision models that can identify objects with human-level accuracy. We've trained custom models on common CAPTCHA datasets, and they perform surprisingly well. For the toughest cases, we integrate with third-party solving services that use real human workers - this is more expensive but guarantees 99%+ success rates.
The newest challenge is invisible CAPTCHAs and behavioral analysis. These don't show a traditional challenge but analyze mouse movements, typing patterns, and other behavioral signals. We've developed human-like interaction patterns that simulate natural browsing behavior - random mouse movements, realistic typing speeds, and natural scroll patterns. This behavioral approach is becoming increasingly important as CAPTCHAs get smarter.
Yes, headless browsers are a core part of our toolkit. Modern websites rely heavily on JavaScript to load content dynamically, and traditional HTTP requests can't capture that. We use Puppeteer and Playwright for most headless browser scraping - they're fast, reliable, and support all modern web features.
Headless browsers let us execute JavaScript, handle AJAX requests, wait for dynamic content to load, and interact with pages like a real user. We can click buttons, fill forms, scroll infinite feeds, and extract data from single-page applications (SPAs). This is essential for scraping modern React, Vue, and Angular applications.
However, headless browsers are slower and more resource-intensive than HTTP requests. We use a hybrid approach: for simple static pages, we use fast HTTP libraries like requests or httpx. For dynamic content, we spin up headless browsers. We also implement smart caching - if we've already scraped a page, we reuse the cached data instead of re-rendering it. This keeps our scrapers efficient while still handling complex sites.
Rate limiting is the most common anti-scraping measure, and we've developed sophisticated strategies to handle it. First, we implement adaptive rate limiting - we start slow and gradually increase speed while monitoring for error responses. If we hit a rate limit, we automatically back off and retry with exponential delays.
IP rotation is another key technique. We maintain pools of residential and data center proxies that rotate automatically. Each request goes through a different IP, making it harder for websites to detect and block us. We also implement geographic distribution - spreading requests across proxies in different regions to avoid concentration.
For persistent bans, we use session management and cookie handling. By maintaining realistic browser sessions with cookies and local storage, we look more like legitimate users. We also implement request queuing and throttling to avoid overwhelming servers. The goal is to be a "good citizen" scraper - get the data you need without disrupting the target website.
Yes, mobile app scraping is one of our specialties. We use several approaches depending on the app type. For Android apps, we can decompile the APK to analyze API endpoints and then call those APIs directly. This is often the cleanest approach - you get structured JSON data without dealing with UI scraping.
For iOS apps, we use network interception tools like Charles Proxy or mitmproxy to capture API calls. By running the app in a simulator or jailbroken device with SSL certificate pinning disabled, we can see all the network requests and replicate them. This gives us access to the same data the app displays, but in a programmatic way.
For apps that don't expose APIs or use heavy encryption, we use UI automation with tools like Appium. This lets us interact with the app like a real user - tapping buttons, scrolling lists, and extracting data from the screen. It's slower but works when other methods fail. We've scraped food delivery apps, ride-sharing apps, and social media platforms using this approach.
AI-Powered Data Cleaning
Large Language Models (LLMs) have revolutionized data cleaning. We use GPT-4 and Claude for tasks that were previously impossible or required manual work. For example, when scraping product descriptions, LLMs can extract structured attributes like brand, color, size, and material from unstructured text. This turns messy descriptions into clean, queryable data.
We also use LLMs for entity extraction and normalization. When scraping business listings, LLMs can identify and standardize company names, addresses, and phone numbers across different formats. They're great at handling variations - "Ltd", "Limited", "Pvt Ltd" all get normalized to the same value. This makes downstream analysis much more reliable.
Another powerful use case is data validation. LLMs can flag inconsistent or suspicious data points that might indicate scraping errors. For example, if a product price is way outside the expected range, the LLM can flag it for review. We've built automated pipelines that scrape, clean with LLMs, validate, and deliver clean data with minimal human intervention.
Sentiment Analysis uses natural language processing to determine the emotional tone of text. We use it extensively when scraping reviews, social media mentions, and customer feedback. By analyzing thousands of reviews, we can tell you whether customers love or hate a product, what features they mention most, and how sentiment changes over time.
Our sentiment analysis pipeline combines traditional ML models with modern LLMs. For high-volume processing, we use fast transformer models like BERT that can classify sentiment in milliseconds. For nuanced analysis, we use GPT-4 to extract specific themes, emotions, and aspects. This hybrid approach gives us both speed and depth.
We've built custom sentiment models for different industries - restaurant reviews need different analysis than software reviews. We also track sentiment at the entity level - not just "this product is good" but "the battery life is great but the screen is disappointing." This granular insight helps our clients make data-driven decisions about product improvements and marketing strategies.
Duplicate data is inevitable in large-scale scraping, but we've developed robust deduplication strategies. The first line of defense is URL-based deduplication - we maintain a database of scraped URLs and skip repeats. This catches most duplicates at the source.
For content-based deduplication, we use fuzzy matching and similarity algorithms. Two products might have slightly different titles or descriptions but be the same item. We use techniques like MinHash and Locality-Sensitive Hashing (LSH) to efficiently find near-duplicates in large datasets. This is especially important for e-commerce scraping where the same product appears across multiple sellers.
For the toughest cases, we use LLMs to determine if two items are truly the same. By comparing attributes like brand, model, specs, and images, LLMs can make intelligent decisions about deduplication. We've seen 99%+ accuracy in identifying duplicate products across different e-commerce platforms using this approach.
Absolutely, this is one of our strongest capabilities. We use Named Entity Recognition (NER) to extract structured information from unstructured text. For example, from a job posting, we can extract the job title, company, location, salary range, required skills, and experience level - all from free-form text.
We've built custom NER models for various domains - real estate listings, product descriptions, legal documents, and more. These models are trained on domain-specific data and can identify entities that general-purpose models miss. For complex extraction tasks, we use LLMs with carefully designed prompts that guide the extraction process.
The output is clean, structured data ready for databases, analysis, or API integration. We've helped clients turn thousands of unstructured documents into searchable databases, extract product specifications from descriptions, and build knowledge graphs from scraped content. This transforms raw text into actionable insights.
Data quality is our obsession. We implement multiple layers of validation to ensure clean, accurate data. First, schema validation ensures all required fields are present and in the correct format. Missing or malformed data gets flagged for review or automatic correction where possible.
We use statistical analysis to detect anomalies - prices that are too high or too low, dates that don't make sense, or values outside expected ranges. These outliers are either corrected using context clues or flagged for manual review. We also cross-reference data across multiple sources to verify accuracy.
For ongoing scraping projects, we implement data quality monitoring that tracks metrics over time. If quality drops, we get alerted and investigate. This proactive approach catches issues before they affect our clients. We've built dashboards that show data completeness, accuracy, and freshness in real-time.
Business Logistics & Delivery
We deliver data in whatever format works best for you. The most common formats are CSV and Excel - great for analysis, reporting, and importing into other tools. We can customize column names, data types, and formatting to match your requirements. For larger datasets, we split files into manageable chunks with consistent naming conventions.
For technical teams, JSON is the go-to format. It's flexible, human-readable, and works great with APIs and databases. We can structure JSON data hierarchically to match your data model, making integration seamless. We also support XML for legacy systems and Parquet for big data workflows.
For ongoing projects, we offer direct database integration - we can write data directly to your SQL database, MongoDB, or data warehouse. We also provide API endpoints where you can pull fresh data on demand. Some clients prefer cloud storage - we can upload to AWS S3, Google Cloud Storage, or Azure Blob Storage automatically.
Delivery time depends on project complexity, but we're known for speed. For simple scraping projects (a few thousand records from a straightforward website), we can often deliver within 24-48 hours. We've built reusable scrapers for common platforms that accelerate development significantly.
For medium projects (tens of thousands of records, multiple sources, or complex sites), expect 3-7 days. This includes development, testing, and initial data delivery. We provide sample data early so you can validate the output before we complete the full scrape.
For enterprise projects (millions of records, complex anti-bot measures, or ongoing monitoring), we typically deliver in 1-2 weeks. We break these into phases - prototype, pilot, and full-scale - so you see progress throughout. For ongoing monitoring, we set up automated pipelines that deliver fresh data daily, hourly, or in real-time depending on your needs.
Yes, layout change monitoring is built into our ongoing scraping services. Websites change their structure frequently - new designs, updated HTML, or reorganized content can break scrapers. We've developed automated monitoring that detects when a site's structure changes and alerts us immediately.
Our monitoring system tracks multiple signals: HTML structure changes, CSS selector failures, missing expected elements, and data quality drops. When any of these trigger, we get notified and investigate. Most changes are minor and we can update the scraper within hours. Major redesigns might take a day or two to adapt.
We also maintain version control for all our scrapers, so we can quickly roll back if needed. For critical clients, we implement redundant scrapers using different extraction strategies - if one fails due to layout changes, the other keeps running. This ensures continuous data delivery even during website updates.
Scrapers break - it's inevitable. The difference is how we handle it. We have 24/7 monitoring that alerts us immediately when a scraper fails. Our team responds quickly, typically within an hour for critical issues. We investigate the root cause - whether it's a layout change, IP ban, or technical issue - and fix it.
For ongoing clients, we include maintenance in our service. We don't charge extra for fixing broken scrapers caused by normal website changes. We also implement automatic retry logic with exponential backoff - temporary failures don't require manual intervention, the scraper just retries with increasing delays.
We provide transparency throughout - you'll know if there's an issue and when it'll be fixed. Our dashboard shows scraper health, recent runs, and any issues. For mission-critical data, we can set up redundant scrapers from different approaches to ensure continuity.
Ongoing data updates are where the real value is. We set up automated pipelines that run on your schedule - daily, hourly, or even real-time. Each run identifies new records, updates existing ones, and removes deleted items. We maintain change logs so you can track what changed between runs.
For real-time updates, we implement webhooks or push notifications. When new data appears, you get notified immediately. This is perfect for price monitoring, inventory tracking, or any time-sensitive data. We can also integrate directly with your systems - pushing data to your database, triggering workflows, or updating dashboards automatically.
We handle incremental updates efficiently - we don't re-scrape everything every time. By tracking what we've already scraped and only fetching new or changed data, we save time and resources. This makes ongoing monitoring cost-effective and scalable.