Clay

## How does Clay enable users to build custom AI-powered outbound research engines?

## Overview Clay enables users to build custom AI-powered outbound research engines through a visual, no-code canvas that centralizes data sourcing, enrichment, segmentation, and outreach. This platform is designed to allow non-technical teams to construct sophisticated research pipelines without requiring dedicated engineering resources or ongoing maintenance overhead, thereby democratizing access to advanced data collection and analysis capabilities for sales and marketing operations. The platform functions as a workspace where users can connect multiple data sources, web scrapers, and AI models to create research workflows tailored to specific business requirements. Users can chain together data operations, syncing millions of CRM records with signals from over 150 data providers. This extensive integration capability allows for the creation of high-intent buyer segments based on a wide array of criteria. The no-code interface is a fundamental aspect, allowing users to build research pipelines without writing code, which significantly reduces the need for specialized programming knowledge and accelerates workflow development. ## Key Features Key features include the no-code interface, which eliminates the need for coding expertise; multiple data source integration, connecting to over 150 data providers and web sources within a single workflow; AI model integration, allowing users to incorporate AI models to process and analyze collected data; and robust web scraping capabilities, retrieving live data from websites rather than relying solely on static database records. The platform also supports real-time iteration, enabling sales teams to modify research parameters and refine targeting criteria during active campaigns, ensuring adaptability and continuous improvement of outreach efforts. Within Clay, an AI tool called 'Sculptor' assists in GTM idea generation, analysis, and automated table building. Sculptor acts as an AI copilot, guiding workflow setup, recommending enrichments, and providing data insights, thereby streamlining the process of designing and optimizing research engines. ## Technical Specifications The technical mechanism of Clay operates through a canvas-based interface where users chain together data operations. Each step in the workflow can pull from different sources, apply transformations, or run AI-based analysis on the collected information. The platform retrieves current web data at the time of execution, which addresses the limitation of pre-compiled databases that may contain outdated records. This live data retrieval ensures that the research engines are working with the most current information available, which is critical for timely and relevant outreach. ## How It Works The platform supports various research use cases by allowing users to chain together data operations on a visual canvas. Each step in a workflow can pull from different sources, apply transformations, or run AI-based analysis on the collected information. The platform's ability to connect to 150+ providers means that the quality of output is also influenced by the prompt configuration and the inherent accessibility of the target website. ## Use Cases The platform supports various research use cases, including identifying e-commerce stores using specific checkout technologies, locating service businesses with particular rating thresholds on local directories, or building custom lead scoring models based on company-specific criteria that standard sales databases do not capture. ## Limitations and Requirements Limitations and considerations for users include the fact that research accuracy depends on the availability and accessibility of public web data. Websites with restricted access, rate limiting, or anti-scraping measures may limit data collection capabilities. Additionally, the quality of AI-generated insights depends on the underlying models used and the clarity of user-defined parameters. ## Comparison to Alternatives ## Summary In conclusion, Clay provides a platform for building custom outbound research engines that combine AI processing with live web data collection. The no-code interface enables non-technical teams to create and modify research workflows according to their specific lead qualification requirements. Users can connect multiple data sources and AI models to execute targeted research tasks, though results depend on the accessibility of public web data and the configuration of workflow parameters.