How to Set Up Clay Scraper and Data Extraction Workflows

B2B CRM contact data decays at an annual rate of 22.5% to 30%, which means cold outreach lists quickly lose their accuracy without constant enrichment [HubSpot 2026 Database Report]. Go-to-market teams cannot rely on static databases if they want to avoid bounce rates that damage sender reputation. When a company relocates, changes its name, or hires new executives, static contact lists fail to capture the change, leading to wasted sales outreach.

To address this challenge, teams must switch to real-time data extraction. Scraping websites directly ensures that you capture current details like active job postings, updated pricing plans, and executive bios at the exact moment you need them. Low-code automation platforms like Clay provide GTM teams with the tools to scrape this data directly from target sites, eliminating the need to write custom Python scripts or maintain complex Selenium pipelines.

However, gathering this data is only half the battle. Teams must also decide where to store and organize these scraped records. Traditionally, developers write scraped data to local hard drives or shared Google Drive folders. These storage options quickly become chaotic, as they lack structured naming schemas and do not allow easy search. In contrast, Fast.io provides an intelligent workspace designed to coordinate agent and human operations. By dropping raw scraping CSV outputs into a Fast.io folder, the files are automatically indexed for semantic search, making them immediately accessible via the Fast.io API or Model Context Protocol (MCP) server. You can check the Fast.io LLM onboarding guide for more details on agent configuration.

For quick, manual list extraction, the Clay Chrome extension provides a direct path to capture structured data from directory pages, tables, and search results. The extension identifies list structures automatically, allowing you to scrape multiple rows of data from a page in a single click.

Setting up the Chrome extension involves a few straightforward steps:

• Install the extension from the Chrome Web Store and connect it to your Clay account.

• Navigate to the target webpage containing the list you want to scrape, such as a company directory, job board, or events list.

• Click on the extension icon to activate the auto-detect feature. The tool analyzes the document object model (DOM) to locate repeating patterns, highlighting the columns it can extract.

• If the auto-detect tool does not capture the exact data points you need, click on "Select Data to Add from Page" to enter recipe-building mode. Hover over the elements you want to capture, click to select them, and assign them typed attributes like Text, Image, or URL.

• Once you complete the recipe, name and save it, then export the scraped records to your clipboard, download them as a CSV, or import them directly into a new or existing Clay table.

At this point in the workflow, persistent storage becomes a critical consideration. Storing these lists in local folders or Amazon S3 buckets makes it difficult for the rest of the team to find them, and it does not offer version history. In contrast, storing these CSV files in a Fast.io workspace organizes them automatically. Using Fast.io's URL Import feature, you can pull scraped lists directly from Google Drive or Dropbox via authenticated connections, keeping your data pipeline clean and preserving full file version history. You can learn more about workspace setup on the workspace storage guide.

While Chrome extensions work well for structured directories, they struggle with unstructured websites where information is scattered. Claygent, Clay's proprietary AI-powered web research agent, solves this last-mile data collection problem. Claygent behaves like an autonomous researcher, navigating websites, reading pages, and extracting custom data points based on natural language instructions.

To set up Claygent, open your Clay table, click "Add enrichment," and select the tool under the AI section:

• First, choose the underlying AI model. You can select Claygent Neon for fast, standard tasks, or choose models like GPT-4o or Claude for complex reasoning.

• Second, write a clear mission prompt. For example, if you want to find pricing details, you can write: "Visit the company's website, locate their pricing page, and extract the monthly cost of the starting tier." The configuration panel also includes a "Help me" meta-prompter that helps refine your instructions.

• Third, decide whether to toggle web search on or off. Turn web search on to let the agent search the web for company news or hiring signals.

• Finally, you can upload specific reference documents, such as your ideal customer profile (ICP) or product documentation, to guide Claygent's decision-making. Before running the agent on thousands of rows, use the Claygent Builder to test your prompt on a small sample, reviewing the outputs to ensure consistency.

Often, Claygent will find and download unstructured files like PDF flyers, product brochures, or scanned sheets. Storing these files in standard OneDrive folders leaves them as unreadable blobs that are difficult to search. Fast.io addresses this challenge with Metadata Views, which you can learn more about on the Document Data Extraction product page. Metadata Views turn unstructured documents into a live, queryable database. You simply describe the fields you want to extract using natural language, and Fast.io's AI automatically designs a typed schema (including Text, Integer, URL, or JSON) to parse PDFs and images. You can add new columns to your view at any time without reprocessing the files, making the raw scraped material immediately searchable and usable.

When scraping at scale, you will eventually encounter websites that block automated requests. Standard scraping tools fail when faced with CAPTCHAs, bot protection screens, or dynamic JavaScript rendering. Clay addresses this obstacle through its native integration with ZenRows, a specialized web scraping engine.

ZenRows handles JS rendering and proxy rotation automatically, serving as Clay's engine to bypass anti-bot walls on protected sites like Crunchbase, Zillow, or ZoomInfo. The service rotates IP addresses, solves CAPTCHAs, and mimics human browsing behavior to ensure high-volume data extraction remains uninterrupted.

To configure ZenRows within a Clay table, add a new column and select the ZenRows integration. You can use Clay's managed account or connect your own ZenRows API key. In the configuration panel, enter the target URL column as the input. Next, configure the scraping parameters:

• Toggle JS rendering on for sites that load content dynamically.

• Set a wait time (in milliseconds) to allow the page to fully load.

• Enable premium residential proxies if the site employs aggressive IP blocking.

• Enable "Autoparse" to let ZenRows automatically clean and structure the raw HTML response.

Scraping at scale generates large volumes of HTML or JSON data. Storing these raw outputs in local databases or AWS S3 buckets requires maintaining complex, expensive database infrastructure. Fast.io offers a simpler, more secure solution. The platform's API supports chunked uploads for large data files, and its per-file version history ensures that if a script accidentally overwrites a lead list, you can restore prior versions with a single click. You can also set up webhooks to receive real-time notifications when new scraping outputs are saved to your workspace, triggering automated downstream actions.

Persist and search your Clay scraper data extraction records

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The final stage of a data extraction workflow is handing the structured leads off to your sales team. A common bottleneck occurs when developers build scrapers, but sales representatives cannot access or understand the resulting files due to a lack of shared workspaces.

Traditionally, teams share lists by emailing spreadsheet files or posting them in messaging chats. This leads to outdated versions, broken formatting, and zero access control. Fast.io resolves this by serving as a collaborative layer where humans and AI agents work in the same environment.

Using Fast.io's granular permissions, administrators can assign access levels at the organization, workspace, folder, or file level. Data engineers can write raw scraping results into a restricted folder, while sales representatives read from a curated folder. The platform supports Collaborative Notes, allowing team members and AI agents to co-edit notes, discuss lead quality, and coordinate outbound strategy directly alongside the files.

For technical teams, Fast.io provides action-based Model Context Protocol (MCP) tooling. An AI agent can read and write files directly within the workspace using the Fast.io MCP server. Below is an example of an agent using the MCP server to save a scraped CSV file to a shared workspace folder:

{
  "ServerName": "fast-io",
  "ToolName": "write_to_file",
  "Arguments": {
    "TargetFile": "/workspaces/marketing-leads/scraped_leads_2026.csv",
    "Overwrite": true,
    "CodeContent": "Company,Website,EmployeeCount",
    "Description": "Save scraped B2B lead list from Clay table export",
    "ArtifactMetadata": {
      "Summary": "Scraped B2B lead list containing company names, websites, and employee counts.",
      "UserFacing": true,
      "RequestFeedback": false
    }
  }
}

Fast.io offers three simple subscription plans: Starter at $29/mo, Business at $99/mo, and Growth at $299/mo. Every organization starts with a 14-day free trial, which requires a credit card. An AI agent can sign up for free, configure the workspace, set up folders, import initial data, and then transfer organization ownership to a human team member. Once the human takes ownership and starts the paid subscription, the agent retains administrative permissions to keep the data pipeline running. This ensures a clean transition from automated scraping to human outbound execution. You can view full plan details on the Fast.io pricing page.