Introducing Radar Researcher: An AI tool for exploring Internet data in plain language
Cloudflare Radar Researcher is an AI-powered assistant that lets users explore Radar’s Internet data through natural-language questions. It replaces manual API queries, filters, and chart hunting with interactive visualizations, explanations, follow-up questions, and auditable analysis. The beta tool is designed for both casual users and technical researchers, while grounding responses in real data from Radar’s API. ## Why Cloudflare Built Radar Researcher - Cloudflare Radar publishes global Internet data covering: - DNS queries from the 1.1.1.1 resolver - HTTP traffic across Cloudflare’s network - Internet quality measurements from Cloudflare Speed Test - Network outages and other datasets - Radar serves a wide audience, from journalists and curious users to network operators and academics. - AI lowers the barrier to using complex datasets by allowing users to ask questions without knowing a dataset’s structure, terminology, or API. - The tool is especially useful for people working under time pressure, such as journalists investigating current Internet disruptions. ## Natural-Language Data Exploration - Radar Researcher is available from every Radar page in a docked panel. - Users can: - Ask questions in plain language. - Receive short answers or more detailed reports. - View real, interactive Radar charts alongside explanations. - Continue with suggested follow-up questions. - Use text, voice input, or Radar’s search bar. - Conversations are saved in searchable history, can be pinned, and can be shared through links that expire after 30 days. - Users can inspect how the assistant interpreted their question, which datasets it queried, and how it derived its answer. ## Explaining Existing Charts - An “Explain with AI” action lets users start a conversation from any Radar visualization. - The assistant receives: - A screenshot of the chart for visual context. - The exact underlying data from Radar’s API. - The current location, date range, and applied filters. - This combination lets the model explain the exact chart being viewed while preserving numerical accuracy and context. ## Example: Internet Quality in Portugal - A user can ask about home Internet quality in Portugal without manually selecting API parameters or searching Radar pages. - Researcher queries the Internet quality API and presents the results through familiar interactive charts. - Users can ask follow-up questions, such as comparing Portugal with Spain or another neighboring country. - The assistant can also suggest related investigations, including common Internet outages. ## Example: Investigating an Internet Shutdown - Researcher can support more open-ended investigations for engineers, researchers, journalists, and network operators. - For Iran’s 2026 government-directed shutdowns, it: - Retrieved recorded outage events. - Collected corresponding HTTP traffic data. - Explained the timeline in natural language. - The analysis described traffic falling from approximately 0.58 on January 7 to nearly zero by January 9, partially recovering around January 17, and approaching normal levels by January 27. - It displayed the findings as an annotated traffic chart and a table of outages, including another shutdown beginning February 28. - Suggested follow-ups included comparing Iran’s traffic with neighboring countries. ## Cloudflare’s Technical Implementation - The application runs entirely on Cloudflare’s developer platform. - A Cloudflare Worker uses the Cloudflare Agents SDK. - Each conversation is stored in a stateful Durable Object with its own SQLite database. - Chat history, titles, and streaming responses persist even if users leave the page during generation. - Workers AI runs open models, including Kimi K2.7. - An ordered fallback chain across three model families helps maintain availability if one model provider reaches capacity. - Requests are routed through AI Gateway. Radar Researcher makes Cloudflare’s extensive public datasets easier to use without sacrificing precision or transparency. It is particularly valuable when users need fast, context-specific analysis backed by interactive charts and verifiable source data.
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