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Bright Data beat Elon Musk and Meta in court — now its $100M AI platform is taking on Big Tech

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Bright Data, the Israeli web scraping company that defeated both Meta and Elon Musk’s X in federal court, unveiled a comprehensive AI infrastructure suite Wednesday designed to give artificial intelligence systems unfettered access to real-time web data — a capability the company argues Big Tech platforms are trying to monopolize.

The announcement of Deep Lookup, Browser.ai, and enhanced data collection protocols represents a dramatic expansion for the decade-old company, which has transformed from a specialized web scraping service into what CEO Or Lenchner calls “a unique infrastructure layer for AI companies.” The move comes as artificial intelligence companies increasingly struggle to access current web information needed to power chatbots, autonomous agents, and other AI applications.

“The intelligence of today’s LLMs is no longer its limiting factor; access is,” Lenchner said in an exclusive interview with VentureBeat. “We’ve spent the last decade fighting for open access to public web data, and these new offerings bring us to the next chapter in our journey, one characterized by truly accessible data and the subsequent rise of contextually-aware agents.”

The launch follows Bright Data’s high-profile legal victories in 2024, when federal judges dismissed lawsuits from both Meta and X alleging the company illegally scraped their platforms. Those rulings established crucial legal precedent defining what constitutes “public data” on the internet — information that can be viewed without logging in and therefore can be legally collected and used.

The court cases revealed that both Meta and X had been Bright Data customers even while suing the company, highlighting the contradictory stance many tech giants have taken toward web scraping. The rulings have broader implications for the AI industry, which relies heavily on web data to train and operate language models.

“It was revealed in court that both of them were a Bright Data customer, because everyone needs data, everyone, especially those who are building models,” Lenchner explained. “We are the only company that has the financial resources, and I would even say the courage to do that.”

Judge William Alsup, who presided over the X case, wrote that giving social media companies “free rein to decide, on any basis, who can collect and use data” risks creating “information monopolies that would disserve the public interest.” The ruling established that data viewable without login credentials constitutes public information that can be legally scraped.

Bright Data has now filed a countersuit against X, alleging the platform violated antitrust laws by trying to create a data monopoly to benefit Musk’s AI company, xAI. “The only reason that X are trying to stop Bright Data from allowing its customers to scrape X is that they will be the only entity that can enjoy the relevant quality data that X produces,” Lenchner said.

Deep Lookup and Browser.ai target AI companies struggling with data access

The company’s new products address what Lenchner identifies as the three core requirements for AI systems: algorithms, compute power, and data access. While Bright Data doesn’t develop AI algorithms or provide computing resources, it aims to become the definitive solution for the third requirement.

Deep Lookup functions as a natural language research engine designed to answer complex, multi-layered business questions in real-time. Unlike general-purpose search engines or AI chatbots that provide summaries, Deep Lookup specializes in comprehensive results for queries beginning with “find all.” For example, users can ask for “all shipping companies that went through the Panama and Suez canals in 2023 whose Q3 revenues declined by over 2 percent.”

The system draws from Bright Data’s massive web archive, which currently contains over 200 billion HTML pages and adds 15 billion monthly. By next year, the archive is expected to exceed 500 billion pages. “It’s not just random web pages, it’s actually what the world cares about, because our 20,000 customers represent billions of internet users,” Lenchner noted.

Browser.ai represents what the company calls “the industry’s first unblockable, AI-native browser.” Designed specifically for autonomous AI agents, the cloud-based service mimics human behavior to access websites without triggering bot detection systems. It supports natural language commands and can perform complex web interactions like booking flights or making restaurant reservations.

The browser infrastructure already processes over 150 million web actions daily, according to the company. “Almost all of them are customers,” Lenchner said of AI agent companies that have raised significant funding. “Because what we figured out, and they figured out, is that we solve that problem of entering a website without being blocked and executing web actions on the website.”

MCP Servers (Model Context Protocol) provides a low-latency control layer enabling AI agents to search, crawl, and extract live data in real-time. The protocol allows developers to build AI systems that can act on current information rather than relying solely on training data.

Patent portfolio and proxy network create competitive moat against blocking

Bright Data’s competitive advantage stems from what Lenchner describes as an “obsession” with overcoming website blocking mechanisms. The company holds over 5,500 patent claims on its technology and operates the world’s largest proxy network with more than 150 million IP addresses across 195 countries.

“We have such a good look into the internet,” Lenchner explained. “For a long time now, we have been mapping the internet, and for a long time now, we’re also archiving big chunks of the internet.”

The company’s approach involves sophisticated techniques to mimic human behavior, using real devices, IP addresses, and browser fingerprints rather than simple automated scripts. This makes detection and blocking extremely difficult for websites.

“The only way to block us, practically, is to put the data behind the login, then we won’t even try,” Lenchner said. “Sometimes there is a new blocking logic that we won’t solve immediately. It will take our research team 12 hours, three days that’s like the most it was, and we will unlock it.”

Revenue surpasses $100 million as AI demand explodes post-ChatGPT

While Bright Data remains privately held by a private equity firm, Lenchner confirmed with VentureBeat the company’s annual recurring revenue significantly exceeds $100 million. The business has experienced explosive growth since the launch of ChatGPT in late 2022, as AI companies scrambled to access training data and real-time information.

“Starting March 2023, which is pretty much when GPT-3 changed the world, the AI, or what we call the data for AI, use case just absolutely exploded for us as a company,” Lenchner said. “Everything else is also growing, because everyone needs more data, period. But this use case is just like nothing we’ve seen before.”

The company serves over 20,000 businesses, including Fortune 500 companies and major AI laboratories. Traditional customers include e-commerce platforms tracking competitor pricing, financial services firms seeking market intelligence, and enterprises conducting business research.

GDPR compliance and ethical practices differentiate from competitors

Bright Data has invested heavily in compliance infrastructure to address privacy concerns around data collection. The company follows European GDPR and California CCPA regulations, automatically notifying individuals when their personal information is collected from public sources and providing deletion options.

“The regulation and the legislation are clear since the European GDPR and at least California and CCPA regulations came to play,” Lenchner explained. “If we collected your email address, for example, we will automatically send you an email saying, ‘Hey, this is who we are. We collected your personal information from the public domain. Here’s a huge button you can click if you want to review it, and you can obviously ask to delete it.’”

The company maintains a large compliance team and extensive documentation of its practices, which proved valuable during court proceedings. “We enterprises especially love us because we have our ethical stand that was scrutinized in US courts twice,” Lenchner said.

Web access wars intensify as tech giants seek data monopolies

The battle over web data access reflects broader tensions in the AI industry about information control and competitive advantage. As AI systems become more sophisticated, access to current, comprehensive web data becomes increasingly valuable — and contentious.

Lenchner predicts the web will become “more closed” over time, similar to how Google maintains exclusive access to its web crawling capabilities while others must use alternative services. “A few tech giants are gonna get free access to every website with their agents,” he said. “The rest will need to use our infrastructure or someone else’s infrastructure.”

The company is also observing new trends, including businesses scraping AI chatbots for marketing purposes and the emergence of new protocols like MCP that enable AI agents to interact with web services more effectively.

“All of these guys that are consuming massive amounts of data, and all of us are using them, it’s all going towards building the brains of the robots,” Lenchner said. “It’s okay that you have a chatbot that is talking to a human, because that’s eventually what a robot will do.”

Robot brains and agent economy drive next phase of growth

Bright Data’s transformation from web scraping service to AI infrastructure provider reflects the rapidly evolving needs of the artificial intelligence industry. As companies rush to deploy AI agents and autonomous systems, access to real-time web data becomes as crucial as computing power and algorithmic sophistication.

The legal precedents established through Bright Data’s court victories may prove as significant as its technical innovations, potentially shaping how the entire AI industry accesses and uses web information. With major tech platforms increasingly restricting data access while simultaneously developing their own AI systems, independent infrastructure providers like Bright Data may become essential for maintaining competitive balance in the AI ecosystem.

“We’re an infrastructure company,” Lenchner emphasized. “We’re very talented engineers that hardly go anywhere, just sit with our computers and write code. We’re doing it well. We have no intentions to do anything else.”

The Deep Lookup beta launches Tuesday for business customers, with general public access available through a waitlist. Browser.ai and MCP Servers are already available to enterprise clients through Bright Data’s existing platform.



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Elon Musk Forms New Political Party

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Elon Musk says he’s formed a new political party called the America Party to “give you back your freedom.” President Donald Trump calls it “ridiculous.” Bloomberg’s Craig Trudell reports. (Source: Bloomberg)



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Martin Wiggen: Oil Inventories Still Low

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Since global oil inventories are still low, there is little risk that OPEC’s decision to increase production will lead to a supply shock says Nadia Martin Wiggen, Director at Svelland Capital. Nadia spoke to Francine Lacqua on ‘Bloomberg: The Pulse’. (Source: Bloomberg)



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VC-Backed Startups That Stitch AI And Fashion Together See Strong Investor Interest

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Venture capitalists, as a group, aren’t exactly notorious for their keen fashion sense, but many have taken a strong interest in backing startups that thread AI technology into the apparel industry.

Overall numbers are still relatively small for this emerging sector, but venture investment in fashion-related AI startups has risen or held steady in the past five-plus years. That’s even as VC funding generally has fallen from its pandemic-era highs, Crunchbase data shows. 

Funding to startups at the intersection of AI and apparel spiked to $162 million in 2022 — when China-based Zhiyi Tech, which helps clothing brands spot and predict fashion trends, raised $100 million alone — and has clocked in around $100 million annually since then.

Investors’ interest in fashion-related tech makes sense, given that as a species, we’re estimated to spend an astonishing $1.8 trillion globally each year on attiring ourselves. That figure is projected to climb to $2.3 trillion by 2030.

The true economic and environmental costs of the fashion industry are of course much higher, by the time you account for production waste and pollution, the resources that go into shipping clothes halfway across the world, and frequent returns and exchanges — not to mention concerns about labor conditions in garment factories.

We identified dozens of companies operating at the fashion-and-AI intersection that raised venture funding in recent years, many of them working on issues such as more efficient manufacturing or faster trend-spotting. Some offer AI-driven creative design tools, others are focused on AI-enabled demand prediction or manufacturing, and several companies offer personalized shopping or customized garments. Let’s take a closer look.

Predicting fashion (before it’s no longer fashionable)

The best-funded startup at the intersection of fashion and AI appears to be Zhiyi Tech. The Xiaoshan, China-based company, which works with many China-based apparel companies, searches the internet and social media for trending designs, and combines that with sales data from e-commerce platforms to help brands quickly capitalize on viral trends.

Investors appear to be particularly eager to back companies that tap AI to predict fashion trends.

In the U.S., another top funding recipient is Finesse, which has raised close to $45 million total from investors. The Los Angeles-based startup describes itself as the “first AI-led fashion house” and creates fast-fashion clothes based on social media votes, shopping data and viral trends spotted by its machine learning technology.

“I call it ‘Zara meets Netflix,’ ” CEO and co-founder Ramin Ahmari told Crunchbase News in 2021, when the company raised its $4.5 million seed round. “We all love fashion and the beauty industry, but fashion is a huge world largely untouched by technology. There are now new trends in efficiency and data, and Finesse is all about using data to reduce the tons of waste in fashion.”

While reviews of its clothes have been mixed, the company went on to raise a $40 million Series A led by TQ Ventures.

Another well-funded startup in the fashion demand prediction realm is Syrup Tech. The New York-based company has raised $25.1 million total for its AI-driven predictive software used by fashion brands.

AI fashion design and creation tools

Fashion designers are also increasingly using generative AI to help them design clothes and make 3D digital mockups of items before they ever go into production.

Along those lines, funded startups include Raspberry AI, which earlier this year raised $24 million in an Andreessen Horowitz-led Series A. The New York-based startup’s platform turns designers’ sketches into photorealistic renderings, showing in rich detail how products will look, fit and drape in real life.

Another AI fashion design tool is AI.Fashion. The Los Angeles-based startup makes AI-driven tools for virtual photoshoots and fashion content creation. It raised a $3.6 million seed round led by Neo in February 2024.

BLNG, meanwhile, applies generative AI to jewelry design, converting sketches or text prompts into photorealistic 3D renders. The Los Angles-based company has raised $4.5 million, including a $3 million seed round in April, per Crunchbase.

Discovery and personalization

AI is also changing how consumers discover clothing and footwear. Startups in this category use machine learning to personalize recommendations, improve product tagging and offer smarter shopping experiences for consumers to help them better find what they want.

Among the most high-profile recently funded companies in this cohort is Daydream, an AI-powered shopping platform founded by e-commerce veteran Julie Bornstein, who previously founded The Yes and sold it to Pinterest three years ago. Her new startup makes personalized fashion recommendations through a chat-based interface. The San Francisco-based company raised a $50 million seed round in June from investors including Forerunner Ventures and Index Ventures.

Other companies in this subsector include Lily AI. Its platform translates retailer product attributes into more consumer-friendly language, with the aim of improving site search and personalization. The Mountain View, California-based startup has raised $71.9 million to date.

Other funded fashion discovery startups include:

  • Tel Aviv-based Karma, which has raised $34 million in funding to date for its browser-based shopping tools;
  • London-based Hey Savi, which raised $2.85 million in pre-seed funding last year for its AI fashion search engine; and
  • Shoppin, an India-based startup that helps users discover clothing using prompts and images, raised $1 million in a pre-seed funding in January.

Virtual try-ons, precision fit and customization

Tracking down what looks like the perfect dress to wear to that summer wedding reception is one thing. Knowing it will actually fit and look good on you when it arrives is another.

Companies tackling that problem include several virtual try-on startups that aim to make it easier to gauge how a garment will fit before you buy it online — both to reduce buyer frustration and to reduce the chances of costly returns for retailers.

Along those lines, virtual try-on and social shopping app Doji last month raised $14 million in a seed round led by Thrive Capital. The San Francisco-based company’s app lets users create avatars for virtual try-ons of clothing.

Similarly, Paris-based Veesual offers diverse AI-generated virtual models to showcase how clothes look on different bodies. The startup has raised $7.6 million to date, mostly in a seed round last year led by AVP and Techstars.

A smaller subset of startups is working on actually personalizing the size and fit of shoes and clothes. Among the most notable of the bunch is New York-based IAMBIC, which uses AI to make precision-fit footwear. The company, whose completely custom sneakers were named to TIME’s Best Inventions list in 2023, has raised $1.3 million through research grants.

Another is New York-based Laws of Motion, a seed-funded DTC brand that has raised $10.2 million on the promise of precision-fit clothing for women through virtual body scans and AI technology.

Smart manufacturing and supply chain optimization

Other startups are turning to AI to improve the way garments are made. Funded companies in this group include those working on demand forecasting, advanced textiles and material optimization, process automation, and textile recycling.

For example, Smartex.ai installs AI and computer vision technology into textile factories to help them automatically detect textile defects. The Portugal-based startup has raised $27.6 million in funding, per Crunchbase.

Several startups focused on fashion-related sustainability have also raised funding in recent years. They include Matoha Instrumentation, which builds AI-enabled infrared scanners for rapid textile sorting to support recycling. The London-based startup raised £1.5 million in an April seed round.

Refiberd, meanwhile, uses AI and hyperspectral imaging to enable intelligent sorting in textile-to-textile recycling. The Cupertino, California-based company has raised $2.7 million total from venture rounds and grants.

There’s also some funding in the area of new textile technologies developed with the help of AI. One example is Solena Materials, which raised a $6.7 million seed round in May. The startup, also based in London, uses AI-driven protein sequence design to engineer new biodegradable fibers produced by microbes.

Looking ahead: AI will stay on trend

With AI overall en vogue with investors, startups weaving that technology into the fashion industry seem poised for more growth. We expect that as clothing brands continue to battle supply-chain pressures, consumer churn and shifting online behavior, AI tools will remain on trend in coming seasons.

Related Crunchbase query:

Related reading:

Illustration: Dom Guzman


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