Collecting internal and external data along with further analysis and insights’ extraction simplifies real-time decision-making. In 2025, the main competitive advantages are not about accessing information, but getting and leveraging it before competitors. While for businesses this means higher ROI, in healthcare or surveillance such timing saves lives.
Traditional web scraping has become inadequate to the dynamic structure of target websites and their bot-detection protection, petabytes of information appearing yearly, and other features of the Information Age. That’s why two thirds of companies (65%) leverage AI-based techniques (or are close to it) in collecting and studying data.
Deploying artificially intelligent frameworks as routine scraping tools is not just about obtaining the information faster. It's about creating self-healing, adaptive systems which integrate seamlessly and ethically through geo targeted proxies into RAG-powered chatbots and LLMs. They turn raw web data into contextualized intelligence, easing critical decision-making and sparing time and resources.
From HTTP requests to headaches: what is scraping?
Web scraping requires a set of programs which automatically extracts data at scale through:
● Preliminary steps, such as buying residential and mobile proxies or setting up cloud storage.
● Active phase — sending programmatic HTTP requests, performing HTML parsing, gaining required content, and providing some structured content at the output.
A typical scraping workflow looks like this sequence:
HTTP Request → HTML Response → DOM Parsing → Data Extraction → Storage/Processing.
A configured framework (requests, axios, Scrapy, urllib3, ZenRows, etc.) sends GET/POST calls to target URLs. The target site checks queries’ authenticity and provides the required information, if the location meets metadata. To pass this check, use rotating geo targeted proxies. A free test of API and URL commands lets data experts find an ethical infrastructure for gathering complex data.
Then BeautifulSoup or Cheerio traverse the DOM (Document Object Model) using predefined selectors, acquire structured and unstructured info, and present it as datasets.
The global scraping market is estimated at more than $1 billion in 2025, with potential to double by 2030. The driver of such a rapid expansion is the need for tools, which can handle dynamic, JavaScript-heavy sites and unstructured data. There are niche-market products, but the most popular data collection solutions* are presented by BeautifulSoup, Selenium, Playwright, and Puppeteer.
*according to the Apify 2025 report.
9 common web scraping challenges: how to overcome them
Among the reasons explaining the scraping tools’ industry development is a desire to cope with challenges of finding and getting public internet information.
A scraping architecture faces two main bottlenecks:
- IP (account, hardware) freezing — when defensive systems associate automated activity with a single IP address. The best solution is to connect datacenter proxies, 4G/5G or residential IPs with dynamic addresses to the pipeline, and imitate real-user behavior.
- Selector breakage, caused by changes of site layout or HTML structure. XPath strategies and selectors with unique IDs, like data-* attributes, raise the success info collection rates up to 43.5% for BeautifulSoup users.
The whole info collecting pipeline relies heavily on fixed selector logic. The system looks for specific HTML elements through hard-coded CSS selectors or XPath expressions. And when a website updates its layout, even minor changes (e.g. renaming) can lead to silent failures or even halt data collection.
Tools like Playwright, Selenium, and Puppeteer:
- Automate recurrent tasks in single-page apps based on React, Vue, or Angular.
- Spoof User-Agent headers and fingerprints through stealth plugins.
- Operate dynamic or JavaScript-heavy sites.
- Access content rendered on a client side.
- Mimic real-user behavior.
- Maintain the requests frequency through time.sleep(), asyncio libraries.
To process data retrieval without limitations, restrictions or flagging, a pipeline relies on ethical and transparent solutions — from buying residential IP to scheduling tasks, tracking scraper health, and overcoming other obstacles. As the number of elements is growing, their limitations accumulate, and lead to budget and time losses.
Neural networks address non-AI constraints thanks to adaptive logic, intelligent error recovery, and seamless integration with supplementary software.
AI data collection: what is it?
AI-enabled data collection is regarded as a scraping pipeline with integrated machine learning (ML) tools. They don't just collect data, but understand it and adapt to the changing sites’ structures. Then intelligent systems enrich the information and transfer it into RAG frameworks if needed, or offer informed business decisions.
Artificial intelligence boosts data collection up to 40%, because NLP-driven systems:
- Self-diagnose pipeline malfunctions.
- Adapt to dynamic site structures.
- Provide seamless access to target platforms for automation scripts.
- Prioritize tasks and manage their sequence.
- Detect site’s elements to retrieve through ML cycles.
- Buy residential and mobile proxies according to the task, test, and maintain the intermediate infrastructure.
- Extract and clean raw HTML or JSON into structured formats.
- Check the information’s quality to eliminate the outliers.
- Run feedback loops for more accurate results and lesser time losses. Rising volume of investment in AI-based scraping solutions with projected $8 billion market size by 2032 makes sense.
Capabilities for AI and ML from Astro - Part 1
How does AI data scraping work: 5 basic stages
DeepSeek, Gemini, Claude, Perplexity, ChatGPT, and other models are in fact extensive projections of a human mindset. Used for info gathering purposes, NLP-enhanced agents pass the same workflow phases as non-AI projects do.
Data scraping with AI includes 5 stages of work:
- Finding Target URLs.
- Inspecting the platform for required information.
- Sending requests.
- Extracting the info.
- Storing and cleaning the results.
With machine learning aboard, an AI-based info obtaining workflow follows the scheme:
Let us explain the diagram, mentioning examples of the best datacenter proxies and other SaaS and standalone solutions:
Companies already prefer AI-based tools for enterprise-grade data collection to non-ML ones. By 2034, the former market is estimated at $38.44 billion, which is four times larger than the latter, according to the Market Research Future (MRFR) Report. Additional free proxy tests, trial access to SaaS, integration frameworks, and other components of a ML-oriented workflow let businesses spare even more time, checking setups before scraping sessions.
Scraping with AI: top 7 reasons to use machine learning for data collection
Collecting public data on the internet with AI saves time and eliminates the traditional bottleneck between data discovery and actionable intelligence.
Reasons to use machine learning for scraping are:
1. Adaptability. LLM-powered scripts simplify choosing CSS/XPath selectors through:
a. Processing natural language prompts
b. Auto-updating selectors when target layouts/class/ID changes
c. Choosing attributes unlikely to change (e.g., data-testid) or generating robust selectors via chatbots.
2. Hybrid methodology. AI-directed models choose info extraction scenarios relying on their previous “experience” to:
a. Process static and dynamic content within a single session. The former requires direct HTTP requests, and headless browsers assist in navigating JavaScript-driven platforms.
b. Prioritize API to HTML where applicable to collect web insights faster up to 20–40%. Without need to render JS or search HTML for desired elements, scraping robots address requests directly to servers.
c. Accept manual verification and API calls, which will fill missing or inaccurate page elements.
3. Advanced navigation. Intelligent systems identify data fields — text snippets, HTML tags, UI elements, page structure, and so on — and obtain structured, labeled data. The AI-based technologies are:
a. Computer Vision for layout recognition.
b. NLP for classifying texts and entities as prices, names, locations, dates, and other.
Act ethically on each data collection stage, be it buying residential and mobile proxies from AML/KYC-compliant service or storing datasets.
4. Semantic understanding of unstructured, raw informational assets. LLM deploys:
a. Entity recognition.
b. Context-aware search.
c. Semantic filters for recognizing text elements.
5. Advanced JS rendering. Optimizes requests to JavaScript layouts, applying:
a. Server-Side Rendering (SSR) — fully rendered HTML generated before JavaScript execution.
b. Client-Side Rendering (CSR) with Hydration — instead of HTML rendering, JS builds the full DOM.
c. Selective Script Execution — AI refuses from non-essential scripts (ads, analytics), images and fonts, reducing bandwidth and CPU load.
d. Backend API Querying — cognitive architectures replace obtaining rendered HTML with reverse-engineer API calls. So LLM connects to the web insights’ sources directly.
e. Incremental Static Regeneration (ISR) — static content gets updates only when necessary. Cloud storage like AWS3 or datacenter proxies is the best solution for frameworks like Next.js to cache the info locally.
AI-driven scraping boosts customer analytics and overall performance by 37%, McKinsey claims, and solves other data collection issues.
6. Anomaly detection — AI-fueled constructs identify unusual or unexpected patterns to restore missing data, correct errors, or evade throttling by target sites.
7. Access management — supplement of recurrent “request–answer” TCP connections, relying on:
a. Machine-learned detectors of entry limitations, CAPTCHA solvers, etc. Google reCAPTCHA challenges took more than 800 million hours from users around the globe without proven effectiveness. Instead of spending time on business tasks, personnel have been looking for hydrants and buses (check the “Dazed & Confused: A Large-Scale Real-World User Study of reCAPTCHAv2” for details.
b. Uninterrupted concurrent threading through a KYC-verified geo targeted proxies’ infrastructure — with IP rotation and targeting precision at ISP/carrier-levels.
Old-school methods for gathering public internet insights are forced to navigate JS-heavy platforms and operate encrypted API calls. These technologies face anti-automation web shields (reCAPTCHA, Cloudflare, Akamai, fingerprint checks, etcetera), as cybersecurity Imperva experts believe 37% of global traffic to be the automated requests.
Scraping with AI, being a multi-layered, developed version of traditional info acquaintance, sidesteps its predecessor on corporate-grade levels.
Scraping with AI vs. Traditional web data collection
Data-driven decision-making increases productivity rate to 63%, while LLM technologies free maintenance teams, turning the scripted algorithms into self-healing projects. Pros and cons of non-AI and machine learning scraping approaches are shown in the table below.
Scraping pipeline’s maintenance consumes from 20% to 50% of working hours, while machine learning saves this time. Just as the backend community moved from managing servers to describing desired states in Kubernetes, the internet data obtaining is now transitioning from rule-based extraction to adaptive info acquisition. And the neural networking evolution covers all business spheres, suiting all use cases.
What are AI use cases: examples of applying LLM for scraping
Autonomous, intelligent info collecting streams have broad industry relevance. While the market assessments vary, the development of AI-based scraping solutions’ CAGR is estimated at 19.93% by 2034.
Supplementary techniques are developing at similar rates:
- A pipeline needs to spread the load, create consistent digital fingerprints, access geo-targeted content, etc. That is why enterprises buy residential IPs, datacenter and 4G/5G/LTE addresses, developing the proxy market, size of which is estimated at $7.2 billion by 2031.
- The Data-as-a-Service (DaaS) industry is also growing, with prospects to multiply seven times in ten years.
The most common use cases for ML-introduced info obtaining include:
*According to the Mordor Intelligence Report.
We mentioned the most trending use cases of LLM-linked data collection, because most businesses leverage scraping at various scales, from chatbots and search engines to brick-and-mortar shops.
Building the future: where to buy proxies for AI-enabled scraping
The path from traditional HTTP-based online info retrieving to AI-enabled scraping means more than a technological upgrade. We are witnessing a fundamental shift, where delegating routine tasks, such as finding and getting required information with further enrichment and patterning is only a step to comprehensive artificial superintelligence (AGI).
Generative artificial intelligence is becoming a full-cycle system, which finds, downloads, and processes the required web insights according to natural language prompts and maintains the pipeline’s elements. Finding ethical and reliable components and performing an initial setup is still a human role though.
https://www.youtube.com/watch?v=IuA38Ag73SI
Citations:
[1] Top 10 Data Analytics Trends for 2025, Kanerika Blog, March 2025.
[2] Understanding Structured and Unstructured Data with AstroProxy, AstroProxy Blog, 2025.
[3] Web Scraping Market – Growth, Trends, and Forecasts (2024–2029), Mordor Intelligence, 2024.
[4] The State of Web Scraping in 2024, Apify Blog, January 2024.
[5] XPath, Wikipedia, last updated June 2025.
[6] User-Agent Header, Wikipedia, last updated June 2025.
[7] The Rise of AI in Web Scraping, ScrapingAPI Blog, 2024.
[8] AI-Driven Web Scraping Market Report, Future Market Insights, 2024.
[9] How AI is Changing the Web Scraping Game, YouTube – ScrapingAPI, 2024.
[10] Reinforcement Learning, Wikipedia, last updated July 2025.
[11] KYC and AML in the Proxy Domain, AstroProxy Blog, 2025.
[12] AI-Driven Web Scraping Market Forecast 2024–2032, Market Research Future, 2024.
[13] The Data-Driven Enterprise of 2025, McKinsey & Company – QuantumBlack, March 2025.
[14] Server-Side Rendering, CIO Wiki, 2025.
[15] Five Facts: How Customer Analytics Boosts Corporate Performance, McKinsey & Company, 2024.
[16] Zeng et al., Big Data Framework for Dynamic Web Data Extraction, arXiv preprint arXiv:2311.10911, November 2023.
[17] Bad Bot Report 2025, Imperva, April 2025.
[18] Top Data Analytics Statistics 2025, Edge Delta Blog, 2025.
[19] BARC Survey: Data Sources 2025, BARC.com, June 2025.
[20] Proxy Server Market Report 2024–2032, Verified Market Research, 2024.
[21] Data-as-a-Service (DaaS) Market Forecast 2025–2032, Future Market Insights, 2025.
[22] Artificial General Intelligence, Wikipedia, last updated July 2025.
[23] Artificial General Intelligence Explained with Visuals, YouTube – ColdFusion, 2024.
Top comments (1)
Yo! I wanna comment this part of the article: "9 common web scraping challenges: how to overcome them", SO:
This is a well-structured and insightful breakdown of key web scraping challenges and their solutions. You’ve effectively highlighted two major pain points — IP blocking and selector breakage — while providing actionable strategies to mitigate them.
IP Management: The recommendation to use rotating datacenter, residential or mobile proxies is crucial for avoiding detection. Pairing this with realistic request patterns (e.g., randomized delays, human-like navigation) enhances success rates.
Selector Stability: The emphasis on unique attributes like data-* and adaptive XPath strategies is spot-on. Many scrapers fail silently due to minor HTML changes, so robust selector logic is essential.
Dynamic Content Handling: Mentioning tools like Playwright and Selenium for SPAs and client-side rendering is valuable, especially with the rise of JavaScript-heavy sites.
A few additional considerations could further strengthen the discussion:
CAPTCHAs & Anti-Bots: How do you recommend handling advanced defenses like Cloudflare or reCAPTCHA?
Legal/Ethical Compliance: Briefly expanding on "ethical solutions" (e.g., respecting robots.txt, rate limits) would help users avoid legal risks.
Scalability: Could you elaborate on cost-efficient ways to scale proxies and storage for large datasets?
Overall, this is a highly useful guide for both beginners and experienced practitioners. Thanks for sharing these practical insights!