Kadoa provides the essential data infrastructure for AI agents that need to interact with the web. In the agent ecosystem, Kadoa sits at the sensing and extraction layer, translating the unstructured "world" of the internet into structured data that LLMs can reason over. By offering autonomous data sourcing, they enable developers to build agents that don't break when a target website changes its UI.
They are particularly relevant for those building specialized financial agents or market-monitoring tools. Instead of each agent developer building their own scraping logic, they can use Kadoa as a reliable API for web-to-data translation. This moves the agent stack forward by decoupling the "how to get data" problem from the "what to do with data" problem, a necessary step for the growth of autonomous research agents.
Kadoa is an entry into the evolving market of generative web data extraction. For years, web scraping was a technical challenge defined by fragile scripts and the constant maintenance of CSS selectors or XPath expressions. Developers wrote code that broke the moment a website updated its layout or changed its frontend framework. Kadoa replaces this manual maintenance with an autonomous system that uses large language models to understand the structure of a web page and extract relevant information without being told exactly where to look.
The company specifically targets investment firms that rely on alternative data for market analysis. In the financial sector, data accuracy and compliance are not optional. Kadoa emphasizes its ability to source data in a way that respects robots.txt files and legal boundaries while maintaining the frequency required for trading signals. This focus on compliant web data sourcing suggests a positioning that favors institutional reliability over the more aggressive scraping tactics of legacy tools. By narrowing their focus to finance, Kadoa can optimize its models for the specific entities and relationships—like ticker symbols, pricing data, and corporate filings—that analysts care about most.
Kadoa provides Python-based SDKs and a documented API, allowing users to integrate data streams directly into existing pipelines. Unlike older extraction tools that require a visual point-and-click interface, Kadoa prioritizes a developer-first experience. Its GitHub repositories contain SDKs that suggest the platform is designed to be embedded into larger applications rather than used as a standalone dashboard. This makes it a component of a larger data stack rather than a siloed tool. This approach reflects a broader trend in the data industry toward composable infrastructure where AI handles the messy task of normalization.
What distinguishes Kadoa from standard headless browser libraries is the layer of reasoning applied to the browsing session. When a website introduces a pop-up, a captcha, or a structural change, the Kadoa system is designed to navigate these obstacles in a manner similar to a human user. The autonomous label is important because it implies the system does not just follow a pre-defined path but can find a new one when the original is blocked. For an investment firm, this translates to fewer broken dashboards and more consistent data flow, reducing the human labor required to monitor automated collectors.
Based in Europe and supported by investors like VI Partners and FYRFLY Venture Partners, Kadoa recently closed a seed funding round in early 2025. This capital infusion arrives as the web becomes increasingly difficult for traditional bots to navigate. As large language models become more capable of navigating the web independently, the need for a specialized layer that manages these agents, ensures their output is structured, and handles the infrastructure of proxy management becomes clear. Kadoa is building that layer, positioning itself as the bridge between the chaotic public web and the structured needs of institutional finance.
Autonomous web data extraction for investment firms and developers.
Kadoa is hiring.