Bright Data—the company already powering 100+ million daily AI agent actions—is upping the ante. Today, it announced a new suite of AI-powered tools designed to solve one of the thorniest problems in modern AI: reliable, real-time access to the open web. The centerpiece is Deep Lookup (Beta), a natural language research engine designed to help large language models (LLMs) and autonomous agents answer complex, multilayered questions with structured, cited, and actionable results.
Let’s get one thing straight: the limiting factor for AI isn’t how smart models are. It’s how blind they are. “The intelligence of today’s LLMs is no longer its limiting factor; access is,” said Bright Data CEO Or Lenchner. That’s where Deep Lookup and its supporting tools—Browser.ai and the Model Context Protocol (MCP) server—come in.
Deep Lookup (Beta): Structured Answers, Real-Time Insights
At its core, Deep Lookup is like an AI-native search engine on steroids. Instead of just retrieving pages or hallucinating guesses like some general-purpose LLMs, it delivers verified insights from across petabytes of structured and unstructured data. Think of it as GPT meets Google Scholar meets a private intelligence brief.
You can ask Deep Lookup layered questions (e.g., “Compare supply chain bottlenecks in lithium vs. cobalt across U.S. and China”) and get back not just prose—but structured, cited data you can act on instantly. It’s like having a research assistant that never sleeps, never guesses, and always links to sources.
Currently available to businesses, Deep Lookup opens to the public via a waitlist.
Browser.ai: An Unblockable AI Browser for Agentic Automation
The second showstopper is Browser.ai—an unblockable, AI-native browser that acts more like a stealth human than a bot. It runs in the cloud, bypasses CAPTCHAs and bot defenses, and is optimized for AI agents tasked with scraping, monitoring, and interacting with dynamic content.
Where traditional scrapers get stuck, Browser.ai glides through, mimicking human behavior to operate at industrial scale. It’s a major leap forward for autonomous agents working in e-commerce intelligence, competitive monitoring, and real-time content analysis.
MCP Server: Real-Time Control for Multi-Agent Systems
Tying the suite together is MCP (Model Context Protocol)—a control layer built for developers creating Retrieval-Augmented Generation (RAG) pipelines and multi-agent systems. MCP allows agents to search, crawl, and extract live web data on the fly, maintaining context and relevancy while drastically reducing latency.
It’s like giving your LLM-powered tools reflexes, not just memory.
Why It Matters
The timing couldn’t be better. As LLMs get smarter, the need for live, high-fidelity, and citation-backed data is hitting a breaking point. Hallucination remains a major problem for commercial applications. Meanwhile, industries like finance, research, government, and cybersecurity need agents that don’t just read or regurgitate but observe, reason, and act.
Bright Data’s offerings are a foundational layer for what some in the AI space are calling the agentic internet—a web navigated, queried, and understood not by humans, but by AI on our behalf.
With its 200B+ HTML page Web Archive, 15B new pages added monthly, and now this agent-native suite, Bright Data is positioning itself as the backbone for a new AI ecosystem—one that’s less about model size and more about contextual relevance and real-world responsiveness.
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