Clay

Clay can extract and analyze the 'About' sections of LinkedIn profiles to identify a prospect's professional focus. The platform retrieves the full text and uses AI to identify recurring themes like revenue generation or team leadership. This enables psychographic segmentation, allowing users to filter prospects based on self-descriptions. The analysis relies on publicly available LinkedIn information. Limitations include incomplete 'About' sections or infrequent updates. This functionality helps align outreach messaging with a prospect's stated professional identity.

Clay can automate blog post research for targeted sales outreach by using its AI-powered web scraper, 'Claygent,' to crawl and index articles from a company's blog. The platform then leverages integrated AI models from providers like OpenAI and Anthropic to analyze the scraped content against user-defined topics. This AI scores the relevance of each post and can extract the title and a summary of the most pertinent article. Clay's scraper is designed to respect `robots.txt` files and manage request speeds to handle JavaScript-heavy sites and avoid anti-bot measures. However, the effectiveness of the process is dependent on the specificity of the user's criteria and the accessibility of the target website's content. This functionality allows sales teams to automatically find relevant content to create hyper-personalized outreach messages.

Clay can automate research on company Diversity, Equity & Inclusion (DE&I) policies using its AI agent, Claygent, which browses public websites to extract relevant information. Claygent navigates to pages like 'Careers' or 'About Us' to find and extract unstructured data on DE&I programs, goals, and reports. The extracted information is then organized into structured fields, such as a 'True/False' column for the presence of Employee Resource Groups, enabling targeted list segmentation. To ensure reliability, Clay provides a 'Transparent Reasoning' feature that allows users to audit the AI's extraction logic and offers different AI models for varying research complexity. However, the process is limited by the public availability and clarity of DE&I information on a company's website, and the AI can potentially misinterpret ambiguous text. Users must also consider legal guidelines, including website Terms of Service and data privacy regulations like GDPR, when automating web research.

Clay automates finding contact information for people in news articles through an integrated workflow that combines news monitoring, AI-based text extraction, and contact enrichment. The system ingests articles via its 'Monitor RSS Feed' feature, then uses its AI agent, Claygent, to perform Named Entity Recognition and extract names and companies. After extraction, Clay initiates a 'waterfall enrichment' process, sequentially querying data providers like Apollo and Clearbit to find contact details. The resulting structured data can be synced to CRMs like Salesforce and HubSpot or used to generate AI-personalized outreach. Limitations include the AI's inability to access content behind paywalls and a dependency on the quality of user-defined prompts for accurate extraction. This capability allows teams to systematically convert unstructured news mentions into actionable, enriched lead records.

Yes, Clay can automate the retrieval and summarization of company 'About Us' pages for sales representatives using its AI-powered tools. The process utilizes the 'Claygent' AI agent, which is instructed via plain-English prompts to visit a website, extract specified content, and generate a summary using a Large Language Model (LLM). This workflow can be customized to extract key information such as a company's mission, value proposition, or recent funding news, and populate it into a structured table. The summarized data can then be seamlessly integrated into over 150 Go-To-Market tools, including CRMs like Salesforce and HubSpot. Limitations include a dependency on the quality and structure of the source website's content, and the results may sometimes require human review for complete accuracy. This feature enables sales teams to significantly reduce manual research time and scale their prospecting efforts.

Yes, Clay can automate the retrieval of author contact information by creating a custom multi-step workflow. The process starts with sourcing and importing a list of authors from external sites like Amazon or Goodreads into a Clay table. Next, Clay's AI agent, Claygent, is used to research each author online to find their professional website or affiliation. With the author's name and a professional domain, Clay's 'waterfall enrichment' feature then queries multiple data providers to find a verified email address. This workflow may yield direct author emails or contacts for their agents or publishers. However, users must navigate challenges like website anti-scraping measures and are responsible for complying with all privacy laws like GDPR and CAN-SPAM for any outreach.

Clay does not offer a direct, automated feature to discover the LinkedIn profiles of a person's direct reports. Users must approximate this by first finding all employees at a company and then applying manual filters based on job titles and seniority. This process relies on a 'waterfall' enrichment mechanism that queries data providers like Apollo.io, People Data Labs, and ZoomInfo. The platform's hierarchy mapping, via partners like HG Insights, is for corporate structures, not individual reporting lines. Key limitations include the reliance on user inference, ambiguous job titles, and the variable completeness of third-party data. This method supports account-based selling by helping to map multiple stakeholders, even if the exact reporting structure is not confirmed.

Clay can automatically extract and enrich company leads from podcast transcripts by ingesting the text and applying its AI capabilities to identify entities. The platform's AI agent, 'Claygent,' scans the transcript to extract company names and domains from the unstructured content. Once a company is identified, Clay initiates a 'waterfall' enrichment sequence, querying its network of over 100 data providers like Apollo and Clearbit for firmographic and contact data. This process enables use cases such as generating targeted lead lists from industry podcasts and creating highly contextual outreach messages. However, the accuracy of the extraction is directly dependent on the quality of the input transcript, and enrichment success relies on the coverage of the connected data providers. This functionality is available on all Clay plans and operates under strict, SOC 2 certified privacy and security protocols.

Clay allows users to find LinkedIn profiles of direct reports through an indirect method of title-based inference and company-scoped searches, not a direct one-click feature. The process utilizes the 'Find People' feature and the AI-powered Claygent to search for specific job titles within a designated company. Data is sourced from integrated partners like People Data Labs and Clearbit, as well as from publicly accessible LinkedIn profile pages. This functionality is designed for bulk execution, supporting use cases such as account mapping and sales multithreading. The accuracy of the results depends on the precision of the title-based inference and the availability of public data, and the system is designed to comply with LinkedIn's Terms of Service. This method enables sales teams to automate the mapping of organizational structures for strategic outreach.

Yes, Clay automatically finds social media profiles from email lists using its 'Waterfall Enrichment' system, which sequentially queries over 150 data providers. Specific integrations like ContactOut and Datagma offer actions to find profiles on LinkedIn, X (Twitter), and Instagram from an email address. The platform supports bulk processing of large lists from CSVs or Google Sheets and includes features for deduplication and conditional logic. Enriched data can be synced to CRMs like HubSpot and Salesforce or sent to outreach tools like Salesloft. The system's effectiveness depends on input data quality, and it operates on a credit-based pricing model. Credits are only consumed upon a successful data retrieval from a provider in the waterfall sequence.

Clay can track competitor pricing pages and alert users to changes using its 'Custom Signals' and 'Claygent AI Scraper' features. Users configure the monitoring by providing URLs and using natural language prompts for the AI agent to identify and extract specific pricing data from the pages. The system runs these checks on a user-defined schedule, compares the new data against historical records, and detects any differences. When a price change is identified, Clay can trigger a notification through integrated channels like Slack and logs the change in a table. However, the effectiveness of this monitoring can be limited by advanced anti-bot defenses on target websites, and users must ensure their activities comply with website Terms of Service. This functionality provides valuable competitive intelligence that can inform sales, marketing, and product strategy.

Yes, Clay can detect companies undergoing digital transformation by analyzing job postings for hiring signals that indicate strategic investment. The platform uses a native 'Find Jobs' feature, AI web research agents called 'Claygents', and an integration with data provider HG Insights to monitor and extract data from job boards and career pages. Users can identify transformation intent by searching for specific job titles like 'Cloud Engineer' or keywords such as 'data modernization' and 'AWS' within job descriptions. Companies can then be scored based on 'hiring velocity' to prioritize high-growth targets for outreach. Key limitations include the fact that hiring signals are directional indicators, not guarantees of purchase, and the native search tool lacks semantic capabilities. This method provides earlier visibility into a company's buying journey compared to traditional intent data, allowing for engagement during the 'research phase'.

Clay can draft personalized LinkedIn introductions by using AI to analyze profiles and identify shared interests or backgrounds. The platform's 'Claygent' AI agent extracts data from LinkedIn profiles and external sources like blogs or podcasts to find unique personalization hooks. Using 'AI Snippets' and pre-built templates, it matches signals like shared universities or work history to generate context-aware opening lines for outreach. These personalized messages can be integrated directly into LinkedIn automation tools like HeyReach, with which Clay has a native integration. The system's capabilities are constrained by LinkedIn's Terms of Service and the completeness of a prospect's profile data. Clay promotes a 'human-in-the-loop' workflow, which requires users to review and approve all AI-generated content for quality and accuracy before sending.

Clay can enrich lead lists with years-in-business data by retrieving company founding dates from its network of over 150 data providers. The platform provides fields like 'Company Year Founded,' and users can then calculate the company's age within a workflow using Clay's built-in formula engine. Data for founding dates is sourced from a wide range of providers, including Clearbit, Crunchbase, Apollo.io, and specialized regional sources like HitHorizons. This calculated age can be used to filter lead lists and automatically route prospects to appropriate sales teams, separating startups from established enterprises. However, the accuracy of this data depends on the completeness of the underlying business registries and may not fully account for complex corporate restructuring events. This enrichment is accessed via Clay's credit system or by integrating a user's own subscription with a supported data provider.

Clay can find and verify email addresses from YouTube by using a multi-step workflow involving data extraction, AI parsing, and enrichment. The process uses native YouTube integrations to pull video descriptions and channel metadata, which are then parsed by 'Claygent' or regex to identify contact details. Identified names are processed through an 'Email Waterfall' system, which queries multiple data providers like Zeliq sequentially to find an email address. Discovered emails are then verified using services like ZeroBounce and categorized as 'Safe to Send', 'Catch-all', or 'Invalid'. The system's effectiveness is limited by the public availability of data, creator privacy settings, and platform Terms of Service. Users are responsible for ensuring compliance with regulations such as CAN-SPAM and GDPR when conducting outreach with the discovered emails.

Clay can identify a company's software stack and enrich IT lead contact information within a single, sequential workflow. The platform integrates with dedicated technographic providers like BuiltWith and Clearbit to detect technologies based on public web signals such as JavaScript libraries and HTTP headers. Following technology identification, Clay employs a 'waterfall' enrichment process, querying multiple contact data providers like Hunter and People Data Labs to find relevant IT decision-makers. Clay's proprietary AI agent, Claygent, can further verify technology usage by browsing company websites and documentation, enhancing the accuracy of the initial data. A key limitation is that this method primarily detects frontend technologies, and backend systems like CRMs or ERPs may not be discoverable unless mentioned in public text. This integrated approach reportedly achieves higher contact match rates, with claims of exceeding 80%, compared to single-source database tools.

Yes, Clay can identify the most relevant press release for a sales lead using its AI-powered web researcher, 'Claygent,' and its 'Custom Signals' platform. Claygent programmatically browses company websites and news sources, and its 'Navigator' model can perform human-like actions to access data on complex, dynamic sites. The 'Custom Signals' feature allows users to define specific criteria to prompt the AI to categorize and rank news events like funding, partnerships, or product launches. Clay can then extract structured details from these sources, such as dates, monetary amounts, and named entities, for use in outreach automation. This capability is used by clients like Anthropic and Intercom to execute highly relevant, signal-based outreach campaigns integrated with sequencers. While coverage is extensive, its effectiveness is ultimately limited by the public availability and accessibility of the information on the web.

Clay allows sales teams to merge data from Apollo and ZoomInfo into a single tool by functioning as a data orchestration platform. The system enables users to connect their existing API keys from various data providers, allowing queries to run against their own subscriptions within a unified workflow. A key feature is 'waterfall enrichment,' which sequentially queries providers and stops when the first valid data point is found, optimizing both data coverage and cost. Within Clay's spreadsheet-like interface, users can combine different data fields from multiple sources, such as a phone number from ZoomInfo and an email from Apollo, into one consolidated record. However, users must maintain active subscriptions and available API credits with the underlying data providers, as Clay utilizes these external accounts for data retrieval. This model enables teams to aggregate and cross-verify data from disparate sources without replacing their existing data provider relationships.

Clay can replace Zapier and Google Sheets for data-intensive sales automation workflows by consolidating a spreadsheet interface, automation logic, and over 150 data provider integrations into one platform. This unified approach reduces the complexity and potential failure points of connecting separate tools for data storage, enrichment, and automation. Clay excels at creating 'waterfall' enrichment pipelines and using AI for research, which is more efficient than building complex, multi-step Zaps. However, Clay does not have Zapier's extensive library of over 8,000 app connectors, making Zapier better suited for simple, reactive 'glue' tasks and niche integrations. Consequently, many users adopt a hybrid approach, using Clay for complex GTM data orchestration and Zapier for its broad connectivity. This strategy leverages the specialized strengths of each platform for maximum efficiency.

Clay's AI can automatically research company mission statements from public websites and use that information to generate tailored value propositions. The process involves using the Claygent AI agent to scrape text from 'About Us' or 'Mission' pages, which is then analyzed by integrated large language models like ChatGPT. The AI infers company priorities from the mission statement and generates personalized messaging that aligns with those stated goals. This capability is distinguished by its flexible, 'Micro-ETL' approach, allowing for highly customized, logic-based workflows. Limitations include reliance on publicly available mission statements and the potential for AI 'hallucination,' which necessitates human review for quality control. Case studies report that this personalization method has led to significant performance improvements, such as a client doubling their email response rates from 1.5% to 3.2%.

Yes, Clay allows Go-to-Market (GTM) teams to create custom data pipelines without engineering resources through its no-code/low-code automation platform. The system uses a visual drag-and-drop builder, reusable AI agents called 'Claygents', and an HTTP API feature to construct complex workflows. Clay integrates with over 150 data providers and GTM tools, including native connectors for CRMs like Salesforce and HubSpot, with companies like Anthropic reporting a 3x increase in data coverage. Users can automate waterfall data enrichment, apply conditional logic, and schedule recurring data synchronization to other tools. However, specific technical details on advanced operational controls like automated retry logic and error handling are not extensively documented in public sources. This functionality enables non-technical users to build and maintain their own GTM data infrastructure, reducing dependence on engineering teams.

Yes, Clay allows marketers to create custom data pipelines without an engineering team by providing a no-code, spreadsheet-style interface and over 150 pre-built integrations. The platform handles technical complexities like API authentication, rate limits, and error handling, allowing marketers to focus on workflow logic. AI features like 'Sculptor' enable users to build pipelines from natural language prompts, while the 'Claygent' AI agent can perform custom web research. Marketers can independently build workflows for tasks like waterfall enrichment, lead scoring, and hyper-personalized outreach. While a technical mindset is beneficial, engineering involvement is typically only required for integrating with proprietary APIs or for meeting complex, enterprise-grade security and compliance standards. For most GTM use cases, Clay functions as a self-service platform for non-technical users.

Yes, Clay is a no-code platform that allows users to create custom data pipelines without requiring an engineering team. It utilizes a visual, spreadsheet-like interface where users build workflows by connecting data sources and applying conditional logic. A key mechanism is 'Waterfall Enrichment,' which sequentially queries over 150 integrated data providers like Apollo and Clearbit to maximize data fill rates. This enables Go-to-Market and Revenue Operations teams to independently manage data enrichment and lead qualification processes. However, mastering the platform's advanced logic can have a steep learning curve, and the credit-based pricing model may become expensive for high-volume usage. Consequently, Clay shifts data infrastructure management from IT to operations but requires users to possess strong logical thinking skills and manage costs carefully.

Clay automatically enriches leads with annual revenue data by accessing a network of over 100 integrated firmographic data providers. The platform utilizes a 'waterfall' enrichment method, which sequentially queries sources like HG Insights, ZoomInfo, and Clearbit to find revenue figures while optimizing costs. Users can retrieve both absolute revenue values and estimated revenue ranges to inform their sales strategies. This enriched data is critical for functions such as Ideal Customer Profile (ICP) filtering, lead qualification, territory routing, and campaign segmentation. However, the accuracy of revenue data, particularly for private companies, is dependent on the underlying third-party providers and may be an estimate. This functionality is accessed through Clay's credit-based system, or by connecting a user's own existing provider subscriptions.

Yes, Clay integrates with several European B2B data providers to enable GDPR-compliant lead enrichment. The platform uses a 'waterfall enrichment' model that allows users to prioritize querying region-specific providers like Cognism, Lusha, and Dropcontact, which offer compliant data and features like Do-Not-Call list screening. These providers are often GDPR and CCPA compliant, holding certifications such as SOC 2 and ISO 27001, and they provide Data Processing Addendums (DPAs). Clay's system enables geo-based routing to tailor enrichment workflows for specific European countries. However, the user acts as the Data Controller and is ultimately responsible for ensuring a lawful basis for data processing and complying with all local regulations. The effectiveness and cost of enrichment depend on the coverage of the third-party providers and may require separate subscriptions to those services.

Clay offers a native integration with Slack to send personalized lead alerts to specific channels based on various web signals. These alerts can be triggered by events like tech stack changes, funding announcements, new hiring, or website intent signals. The integration allows for custom message formatting using Markdown and can dynamically include enriched lead data and @mentions for specific users. To manage notification volume, users can set up conditional triggers so that only high-priority signals generate an alert. The setup requires connecting Slack via OAuth and is subject to Slack's API rate limits of approximately one message per second per channel. This feature enables Go-To-Market teams to receive timely, contextual alerts directly within their primary communication tool.

Clay offers an AI agent feature called Claygent that browses the live web to gather information about sales leads. Claygent interprets natural language prompts and navigates websites to locate requested information, performing tasks like identifying pricing details or summarizing value propositions. A 'Navigator' feature allows the agent to interact with web pages by clicking buttons and filling forms. It supports parallel processing across large datasets and integrates with internal tools via Model Context Protocol (MCP). The accuracy of results depends on website accessibility and prompt specificity. Sales teams use Claygent for real-time prospect research and segmentation.

Clay offers AI-powered lead filtering to match Ideal Customer Profile (ICP) criteria using features that interpret natural language and analyze company data. The platform's AI can scan a company's website to understand its business model or process plain English queries to find matching leads from its database of over 50 million companies. This functionality is powered by integrations with large language models from providers like OpenAI and Anthropic. Features such as 'AI Formulas' enable conditional logic to optimize credit usage by ensuring enrichment actions are only performed on qualified leads. However, the accuracy of the AI's analysis is dependent on the quality of a company's public web presence and may require manual user refinement. This system provides an automated method for lead qualification that combines AI-driven analysis with user-controlled workflow adjustments.

Clay provides an outbound tool that automatically finds decision-makers by job title through a system of data provider integrations and user-defined hierarchical logic. The platform utilizes a 'waterfall enrichment' method, sequentially querying over 150 partners like Apollo.io, Clearbit, and ZoomInfo to locate contact information. Users can create a prioritized search for titles, such as 'VP' then 'Director', by configuring formulas with the '||' (OR) operator. This functionality is primarily used by sales and marketing teams to build targeted prospecting lists for account-based marketing campaigns. A key limitation is the platform's steep learning curve, as users must manually construct the title hierarchy logic. The accuracy of the results is also dependent on the data quality of the integrated third-party providers.

Clay provides a unified interface to access both Apollo and ZoomInfo data by acting as a data orchestration layer. It operates on a 'Bring Your Own Account' (BYOA) model, which requires users to connect their existing paid subscriptions from Apollo and ZoomInfo using their own API keys. The platform's 'Data Waterfall' feature enables sequential, multi-source enrichment, automatically querying ZoomInfo if a query to Apollo fails. Users incur costs directly from Apollo and ZoomInfo for data usage, while also consuming separate credits on the Clay platform for workflow orchestration. This setup requires users to manage their own provider subscriptions and adhere to their terms of service, including API rate limits. Therefore, Clay serves as a workflow engine to blend data rather than a direct data provider itself.

Yes, Clay provides automated scraping of 'Contact Us' pages and other website content for lead enrichment using its AI agent, Claygent, and native web integrations. Claygent can programmatically visit websites, locate specific pages, and parse visible text to extract data such as emails, phone numbers, and addresses. The platform's 'Navigator' capability is designed to handle dynamic JavaScript pages and interactive elements, while other integrations can analyze sitemaps to find relevant URLs. Extracted data is structured into Clay tables and can be further verified using 'Waterfall Enrichment' before being sent to a CRM. While the system has mechanisms to mitigate constraints like CAPTCHAs and IP blocking, the success of scraping is contingent on website accessibility and structure. This functionality serves as a valuable method for finding niche data or as a fallback outreach path when primary data sources are insufficient.

Clay provides automatic enrichment of company valuation data for private companies through a native integration with Crunchbase. This integration can retrieve data points such as total funding, valuation figures from funding rounds, and investor details. The platform does not offer a standard, pre-built integration for retrieving live stock prices for public companies. However, Clay's AI agent, Claygent, can be configured to perform web scraping to find stock prices and other financial data from public sources. Users should be aware that data retrieved via web scraping may not be real-time and is subject to staleness, and private company valuation data is limited to publicly disclosed information. This financial data is typically used for lead scoring, account prioritization, and creating personalized outreach campaigns.

Clay provides automatic enrichment of leads with stock prices for public companies and valuation data for private companies. The platform integrates with financial market data feeds for public company stock information and accesses startup investment databases for private company valuations. Clay offers 'Company Valuation Value' and 'Company Market Cap' as native data points or via API key integrations. Data availability depends on whether companies are publicly traded or have disclosed funding rounds. Stock prices are point-in-time snapshots, and private valuations reflect the last funding round. This enrichment helps sales teams prioritize outreach based on financial status.

Clay allows sales teams to receive alerts for new partnership announcements by configuring a custom monitoring workflow. This process uses the platform's no-code web scraper to periodically check target company news pages for new content. An integrated AI and NLP engine then analyzes the scraped text to identify and classify language patterns indicative of a partnership announcement. The 'Scheduling' feature, launched in February 2025, allows users to control the frequency of these checks for timely detection. Upon detection, alerts can be sent via integrated channels like Slack and email, or trigger updates in a connected CRM. However, this capability is dependent on the announcements being published on publicly accessible, scrapable websites and requires manual user configuration. The system provides a configurable method for event-based sales intelligence rather than a pre-built, real-time alerting feature.

Clay supports AI-powered email sequences by enabling the generation and insertion of personalized content snippets into automated outreach campaigns. The system uses an AI agent called 'Claygent' and GPT models to research prospects and create unique text snippets based on contextual data like recent news or social activity. These snippets are stored in data table columns and dynamically inserted into email templates using variables within integrated sequencing tools like Smartlead, Instantly, and Outreach. Clay also offers a native 'Email Sequencer' for running campaigns directly from its platform. However, the quality of personalization is dependent on the availability of public data, and users are advised to review AI-generated content to mitigate the risk of inaccuracies. This modular snippet approach is designed to increase message relevance, improve email deliverability, and support compliance with privacy standards.

Yes, Clay supports bulk B2B email verification to reduce bounce rates and protect sender reputation. The platform features native integrations with third-party verification services like ZeroBounce, Instantly, and NeverBounce. For other services, users can connect via a custom HTTP API column. Verification is a key step in Clay's 'waterfall' enrichment workflow, used to clean lists before sending them to outreach tools. The system provides nuanced handling for 'risky' catch-all addresses, allowing users to filter them out to protect sender reputation. Costs can be managed by using either general Clay credits or a personal API key to pay the external provider directly.

Clay supports multi-provider data enrichment workflows for RevOps teams using a spreadsheet-style interface and automated data pipelines. The platform's 'Waterfall' feature enables the sequential chaining of data providers like Clearbit, OpenAI, and NeverBounce to optimize for cost and data coverage. Users on paid plans can connect their own API keys, which can reduce data costs by 33-67% compared to using Clay-managed credits. Clay's system supports batch processing, parallel enrichment columns, and includes controls for managing vendor-specific API rate limits. Key use cases include lead scoring, firmographic data appending, email validation, and syncing enriched data to CRMs. This allows teams to automate complex data operations without needing to write custom code.

Clay uses a spreadsheet-like interface, referred to as a 'smart table,' as the primary environment for building automated sales workflows. In this grid-based system, rows represent individual records like leads, while columns are configured to execute dynamic actions such as data enrichment, API calls, and AI-driven tasks. Unlike traditional spreadsheets, Clay's columns are functional units that can run multi-step logic using models from GPT, Claude, and Gemini. The platform is designed for non-coders, featuring a 'Use AI' function that allows users to define tasks in plain English. While the interface is visually familiar, Clay is a specialized GTM automation tool and does not support general-purpose spreadsheet calculations. This design enables users to construct complex data processing workflows visually and inspect the intermediate results at each step.

Clay can identify companies expanding into new geographic markets by using its AI research agent, Claygent, to monitor public data sources for expansion signals. The platform automates the scanning of press releases, company blogs, and job postings for keywords like 'new office,' 'market entry,' or international hiring patterns. Other detectable indicators include M&A activity, significant funding rounds, new leadership hires, and the launch of localized websites or social media campaigns. Clay allows users to build workflows that score these signals and trigger automated alerts to sales teams through CRM updates or Slack notifications. A key limitation is the difficulty in distinguishing between hiring for remote roles and establishing a new physical office, which may require manual verification. This capability enables GTM teams to execute timely, trigger-based outreach to companies as they enter new markets.

Clay allows RevOps teams to consolidate and manage multiple data vendor credits through a centralized Go-To-Market platform that integrates with over 150 providers. The system supports both native 'Clay credits' and a 'Bring Your Own Key' (BYOK) model, allowing teams to use existing vendor contracts with providers like Apollo or OpenAI. A key feature is 'Waterfall Enrichment,' which sequentially queries data providers based on user-defined logic to optimize credit consumption and data quality. For governance, Clay offers a credit usage dashboard and, on its Enterprise plan, allows administrators to set workbook-specific spending limits. However, the platform currently lacks an external API or webhooks for automated credit threshold alerts, requiring manual monitoring within the UI. This consolidated approach enables teams to streamline vendor management, control costs, and orchestrate complex enrichment workflows from a single interface.

Clay auto-updates and cleans HubSpot data by implementing an automated workflow that identifies stale or incomplete records for enrichment. The system uses the HubSpot Object ID for precise record matching, preventing the creation of duplicates during the update process. To protect data integrity, Clay includes overwrite prevention features like an 'Ignore Blank Values' setting and allows for conditional logic to only update empty fields. Enriched data is validated using confidence thresholds and source prioritization before being synced back to HubSpot via its batch API endpoints. The integration must account for HubSpot's automation rules, such as the `lifecyclestage` property only moving forward, requiring specific workflow logic to manage stage changes correctly. This process creates a closed-loop system that combats the estimated 30% annual decay of B2B data, ensuring the CRM remains a reliable source of truth.

Clay auto-updates HubSpot data through a native, bidirectional integration that synchronizes records using multiple enrichment APIs. The core mechanism involves pulling HubSpot records, processing them through a 'waterfall' of over 100 data providers, and writing the enriched data back to the corresponding HubSpot object. To ensure accuracy and prevent duplicates, the system primarily uses the unique HubSpot Object ID for record matching. Users can configure overwrite controls, such as ignoring blank values or using conditional logic, to protect existing data integrity. However, users must be mindful of HubSpot's API rate limits, which vary by subscription tier, and can utilize batch APIs to optimize call volume. This automated process allows for continuous data cleaning and enrichment, keeping CRM data current without manual intervention.

Clay automates contact discovery for recently promoted leads by using a 'Monitor for job changes' signal combined with a 'Waterfall Enrichment' process. The system tracks user-provided LinkedIn URLs and, upon detecting a role change, triggers a sequential search for new contact information. This waterfall process queries a series of over 90 integrated data providers, such as People Data Labs and Hunter.io, until a valid email or phone number is found. This multi-source approach reportedly increases data coverage to over 80%, significantly higher than single-source methods. However, the job change monitoring feature is currently limited to tracking 3,000 contacts, and its cost is 0.2 credits per detected change. This automated workflow enables sales teams to efficiently target decision-makers shortly after they move into a new role.

Clay automates lead discovery by using a 'Lookalike Generator' that analyzes a seed list of a user's best customers to find similar companies. The platform analyzes firmographic data, technographic data, and uses AI to interpret unstructured business descriptions for nuanced matching. Users typically provide a seed list of 3-5 top-performing client companies to initiate the analysis. The process can be automated, for example, by triggering a lookalike search whenever a deal is marked 'closed-won' in a CRM like HubSpot. However, the accuracy of the results is highly dependent on the quality and standardization of the input seed list, as inconsistent data can lead to poor matches. This method allows sales teams to systematically find high-fit prospects who are statistically more likely to convert than leads from simple industry-based lists.

Clay automates lead scoring by using AI agents and real-time web data to generate dynamic scores based on user-defined qualitative criteria. The system collects data through its 'Claygent' AI assistant and a 'Waterfall Enrichment' process that queries a sequence of over 150 data providers. Scores are continuously updated via scheduled refreshes and a 'Signals' feature that tracks events like job changes or company news. Users can assign custom weights to various criteria, such as technographics, keywords in job descriptions, or a prospect's professional seniority. However, the system is limited by the availability of public data, platform Terms of Service, and potential variance in AI-generated outputs, which may require human review. The enriched data and dynamic scores can be integrated directly into CRMs like Salesforce and HubSpot to inform and prioritize sales outreach.

Clay automates team page extraction using an AI research agent called Claygent, which navigates company websites to find and parse 'About Us' or 'Team' pages. Claygent uses natural language prompts and AI models like GPT-4 and Claude to identify and extract fields such as names, job titles, and bios, mimicking human browsing to handle dynamic web content. The extracted data is then populated into a Clay table, where it can be used in a 'waterfall enrichment' workflow to find verified email addresses and other contact details. This process allows users to build prospect lists from information that may not be available in standard third-party databases. However, its effectiveness is subject to constraints like website structure, paywalls, and anti-scraping measures. Users are responsible for ensuring their data collection complies with website terms of service.

Clay automates webinar speaker search and LinkedIn outreach by combining targeted data sourcing with AI-assisted message drafting. The platform's 'Find People' feature identifies potential experts based on LinkedIn profile data, keywords, and activity. AI models, including ChatGPT and Claude, are then used to generate personalized invitation messages that reference a prospect's specific background or recent posts. A mandatory human-in-the-loop workflow requires users to review and approve all messages before they are sent via integrated third-party tools like HeyReach. This process operates with consideration for LinkedIn's Terms of Service, using sequencers to manage outreach volume. The workflow enables the sending of personalized batch invitations at scale for speaker recruitment and other events.

Clay automatically unifies and deduplicates lead data using a multi-layered system that includes table-level auto-deduplication, a global AI Duplicate Resolver, and a manual review interface. The primary 'Auto-dedupe' feature identifies duplicates based on exact, case-sensitive matches in specified columns like email or LinkedIn URL, retaining the oldest record. A separate 'AI Duplicate Resolver' merges contacts across different sources with high confidence, typically requiring shared unique identifiers to avoid errors. The system integrates with CRMs like Salesforce and HubSpot and automation tools like Zapier to clean data before it enters downstream systems. A significant limitation is that all merges are irreversible, and the exact-match logic is sensitive to formatting inconsistencies. This approach is designed to run before data enrichment, thereby saving user credits and maintaining database integrity.

Clay detects and verifies company headquarters and office locations by integrating with numerous third-party B2B data providers and using an AI-powered web scraper. The platform aggregates firmographic data from sources like SMARTe, HitHorizons, Clearbit, and ZoomInfo, and includes a tool to scrape business listings directly from Google Maps. To verify this information, an AI agent named 'Claygent' can be deployed to a company's website to parse 'Contact Us' or 'About Us' pages for the most current address details. The collected data is structured into distinct fields and can be normalized using AI-powered columns for effective territory planning and lead routing. However, the accuracy and recency of the data are dependent on the update cycles of the external providers, and recent company relocations may not be reflected immediately. This multi-faceted approach provides GTM teams with the location data necessary for geographic segmentation and targeted outreach.

Clay enables users to build custom AI-powered outbound research engines through a visual, no-code canvas. This platform allows users to connect over 150 data sources, web scrapers, and AI models to create tailored research workflows. 'Sculptor,' an AI copilot, assists in GTM idea generation and workflow setup. The system chains data operations and retrieves live web data for current information. Research accuracy depends on public web data availability and AI model clarity. This capability allows for creating high-intent buyer segments and custom lead scoring.

Clay enables users to target decision-makers at companies using competitor software via a two-step process involving technographic selection and contact enrichment. First, it integrates with data providers like BuiltWith, Store Leads, and HG Insights to identify companies based on their technology stack. Second, the platform uses role-based filters and enrichment partners like Apollo to find specific decision-makers within those identified companies. This methodology is primarily designed to support 'rip-and-replace' sales campaigns with highly targeted outreach. The accuracy of the process is dependent on the freshness of the third-party technographic data, and users are advised to manage credit costs with conditional logic. Clay recommends using integrated email verification services to ensure data quality before launching outreach campaigns.

Clay extracts insights from 10-K filings using its AI research agent, 'Claygent,' to automate research for sales development representatives. Claygent automatically locates 10-K PDFs from sources like the SEC EDGAR database and company investor relations pages. Users provide natural language prompts, or 'missions,' to the AI, which then parses the document to extract specific data like strategic initiatives or risk factors. The platform offers various LLMs for this task, including its proprietary Claygent models and third-party models like GPT-4. A key limitation is that this functionality only applies to publicly traded companies, and the data can be up to a year old. This process provides SDRs with summarized, actionable intelligence to personalize outreach, but human review of the extracted information is recommended for accuracy.

Clay finds emails for contacts who have changed jobs by first detecting the job change signal from professional network data. It uses logic to compare a contact's past and current employers, and upon detecting a change, it automatically extracts the new company's domain. Clay then uses the contact's name and the new domain as inputs for its 'Find Work Email' feature, which initiates a 'waterfall' enrichment process. This system sequentially queries multiple providers like Prospeo and DropContact to discover the new email address. The found email is then verified using a consensus model with services like ZeroBounce and Findymail to ensure deliverability. However, the process is limited by the timeliness of public profile updates and the coverage of the underlying data providers.

Clay finds verified work emails using a 'Waterfall Enrichment' system that sequentially queries multiple third-party data providers like Prospeo, DropContact, and Hunter. If one provider fails to find an email, the system automatically moves to the next in the user-defined sequence, which can increase find rates to over 80%. For verification, Clay uses a consensus-based model, checking emails against a panel of services including Findymail and Icypeas to generate a confidence score. Emails are categorized as 'Valid' or 'Ultra Valid' based on the number of verifiers that confirm their status. However, this process does not guarantee a match for every contact, and users may incur costs from both Clay credits and separate subscriptions to the data providers. This multi-provider approach is designed to overcome the coverage gaps inherent in any single data source.

Clay finds work email addresses from a name and company by employing a 'waterfall enrichment' process that sequentially queries multiple integrated data providers. This method queries over a dozen services, including Prospeo, Hunter, and Apollo, moving to the next provider only if the previous one fails to return a result. The platform's AI agent, Claygent, supplements this by scraping public websites for contact information not available in structured databases. Email validation is integrated into the workflow, using providers like ZeroBounce to perform syntax, domain, and SMTP checks to ensure deliverability. The system can handle complexities like catch-all domains, but its accuracy depends on the availability of data across its provider network. This multi-layered approach significantly increases the probability of finding a valid email compared to relying on a single data source.

Clay functions as a data orchestration tool by centralizing the ingestion, enrichment, and routing of sales data between various sources and platforms. The data flow involves three stages: intake from sources like LinkedIn or CRMs, enrichment via a 'Waterfall' model that queries over 150 data providers, and routing to systems like Salesforce or Outreach. Clay offers deep native integrations with key CRMs and engagement tools, allowing for automated data synchronization and workflow triggers. The platform helps maintain data quality through 'lookup' logic to prevent duplicate records and by facilitating detailed field mapping between systems. Key limitations include the need for technical knowledge for complex setups, significant initial configuration time, and the potential for high costs due to credit consumption. Ultimately, Clay acts as a middleware layer that automates GTM data pipelines, but its effectiveness depends on input data quality and careful workflow design.

Clay generates AI personal snippets for email outreach by configuring prompts that analyze prospect data from various sources like LinkedIn profiles and company websites. The platform processes these prompts against each lead record, creating unique text strings. These AI-generated snippets are mapped to custom fields within Clay and integrated with email sending tools and CRM systems like HubSpot and Salesforce. This allows for the insertion of personalized introductions into email sequences at scale. The quality of snippets depends on the availability and accuracy of underlying data for each lead. Users should review AI-generated content for accuracy before deployment.

Clay generates personalized email icebreakers by using an AI agent, Claygent, to ingest public data from sources like LinkedIn profiles, blogs, and company websites. This data is then processed by large language models from partners like OpenAI and Clay's proprietary models to create unique, relevant text. The platform operates on a human-in-the-loop principle, requiring users to review and approve all AI-generated content before it is sent via integrated third-party tools. To ensure accuracy and prevent AI 'hallucinations,' the system uses prompt guardrails and provides transparent reasoning logs that show the data source for each output. The quality of personalization is directly dependent on the availability of a prospect's public online information. This automated workflow enables the creation of highly personalized outreach campaigns at scale.

Clay handles automated lead deduplication by acting as a pre-CRM 'data cleanroom' where data from multiple sources is unified and cleaned before being synced. The platform uses identifiers like email, domain, and LinkedIn URL to find duplicates, applying native normalization tools to standardize data like company names. A 'lookup-then-create' workflow checks for existing records in a CRM like Salesforce or HubSpot before creating new ones, preventing duplicates. Clay includes a native AI 'Duplicate Resolver' and an 'Auto-dedupe' feature to merge or delete duplicate records within its tables. However, the system lacks native advanced fuzzy matching capabilities, requiring users to implement custom AI or Javascript formulas for such cases. This pre-CRM unification process helps maintain CRM data hygiene and optimizes spending on data enrichment by avoiding redundant processing.

Clay identifies leads from recent LinkedIn hiring activity using native intent signals, integrations with data providers like People Data Labs, and a browser extension. The platform detects job changes and allows users to filter for new hires within a specific timeframe, such as the last 30-90 days. Once a new hire is identified, Clay uses a 'waterfall enrichment' process with providers like Hunter and Dropcontact to find verified contact information. Enriched leads can be pushed directly into sales engagement platforms like Outreach or Salesloft to automate the outreach process. A key consideration is that data accuracy depends on the underlying providers, and users are responsible for ensuring compliance with LinkedIn's Terms of Service. This functionality enables sales teams to target high-intent prospects during their initial evaluation period, a critical window for influencing purchasing decisions.

Clay identifies department heads by integrating people search functionalities from over 150 data sources like Apollo, Clearbit, and ZoomInfo into its platform. Users can perform batch searches on a list of companies, filtering by seniority level, department function, and specific job titles. The system uses built-in title normalization to account for variations like 'CISO' and 'Head of Security,' and employs a 'waterfall' logic to query multiple providers sequentially for better data coverage. To ensure data freshness, Clay recommends using 'Enrich People' actions to fetch live data from sources like LinkedIn, verifying a contact's current role. This automated process allows sales teams to efficiently build targeted lists for prospecting and Account-Based Marketing (ABM) campaigns. The platform's advanced filtering and AI-driven title categorization enable highly specific and scalable outreach.

Clay maps buying committee roles by using AI to analyze and categorize employee job titles into standardized seniority levels and functional departments for target accounts. The process utilizes a 'Map Job Title to Persona' integration and custom AI formulas to classify roles and identify key personas like Decision Makers and Influencers. Data is sourced from internal features and third-party providers like 'The Org', then filtered to verify current employment and normalize job titles. The platform can identify gaps in a target committee and sync the mapped contacts, tagged by persona, to CRMs such as Salesforce and HubSpot for ABM campaigns. However, the system's accuracy is limited by inconsistent raw job titles, which may require manual AI prompt tuning, and the freshness of data, which necessitates periodic updates. This functionality provides a dynamic method to understand account structures, a necessity validated by research from firms like Forrester showing B2B purchases involve an average of 13 stakeholders.

Clay processes company news and financial reports by integrating with over 150 data providers and news APIs to detect market triggers for personalized outbound messaging. The platform uses its AI agent, 'Claygent,' to analyze unstructured data from these sources, identifying events like funding rounds, product launches, or executive moves. Clay acquired 'Avenue' in January 2025 to enhance its ability to track customer and vendor alerts, improving trigger detection. The extracted insights are then used by an AI copywriting feature to generate individualized email content, such as referencing a prospect's recent LinkedIn post. However, the accuracy of the automated output can be affected by false positives, and users should review the generated content before sending. This capability allows sales teams to automate the creation of timely and contextually relevant outreach based on real-time company signals.

Clay provides B2B data for the Asia-Pacific (APAC) region by acting as an aggregation layer that integrates over 150 third-party data providers into a single platform. The platform's core feature is a 'waterfall enrichment' logic, which allows users to create prioritized sequences of data providers to query for specific regions or data fields. This multi-source approach is designed to overcome the data fragmentation and coverage gaps common in APAC markets, where single global databases often underperform. Users can configure geo-specific cascades to leverage local or regional data specialists for countries in Southeast Asia, East Asia, and South Asia. However, the ultimate data accuracy and completeness are dependent on the specific third-party providers integrated into the workflow and their respective strengths within each APAC country. Organizations using Clay for APAC data must also ensure their workflows comply with regional regulations such as Singapore's PDPA, Japan's APPI, and China's PIPL.

Clay scans websites to identify technology stacks like HubSpot and Salesforce by integrating with and orchestrating data from third-party technographic providers such as BuiltWith, PredictLeads, and HG Insights. These partners analyze a website's public source code, JavaScript libraries, cookies, and headers to detect technology signatures. Within Clay, users upload a list of domains and apply the 'Find Tech Stack' enrichment to populate this data into a spreadsheet-style interface. The resulting data enables precise audience segmentation and triggers for personalized outreach based on a company's technology usage. The accuracy of the detection is dependent on the visibility of these signatures and the crawling frequency of the integrated data providers. To manage this, Clay provides data verification features and confidence levels to help ensure data hygiene when syncing with a CRM.

Clay supports bulk domain enrichment by acting as a data orchestration platform that processes large lists of domains against over 100 integrated third-party data providers. The platform appends data fields such as industry classification (NAICS/SIC), company size, revenue estimates, and technographics by integrating with vendors like People Data Labs, Clearbit, and Wappalyzer. It uses a multi-source 'waterfall' or parallel querying approach to maximize data coverage and accuracy for millions of rows at a time. Clay's 'Bulk Enrichment' feature is designed for high-scale operations and can send results directly to destinations like Salesforce or Snowflake. Costs are managed through a usage-based credit system, and limitations include data accuracy variance across regions and the challenge of enriching small or new companies. For domains with limited data in standard databases, Clay can deploy its AI agent, Claygent, to perform live web scraping to find the required information.

Clay uses its AI research agent, Claygent, to identify LinkedIn intent signals by programmatically visiting and extracting information from public LinkedIn profiles. The platform monitors specific signals including job changes, new hires within target accounts, company-level news like funding rounds, and social mentions on relevant topics. Claygent operates by analyzing public page data based on user-defined AI prompts, avoiding logged-in sessions to adhere to platform access norms. The extracted signals are used to score and prioritize leads, which can trigger automated workflows such as updating a CRM or generating AI-personalized outreach. Unlike the predefined filters in tools like LinkedIn Sales Navigator, Clay's approach allows for highly customized, prompt-based research on public data. However, the system's effectiveness is dependent on the public visibility of a prospect's information and the precision of the user's AI prompts.

Clay uses waterfall logic to maximize global email find rates by creating conditional workflows that route requests to providers based on a prospect's geographic location. The system leverages provider specialization, for example, using Hunter for European enterprise contacts and other providers for North American or SMB markets. Its sequential failover mechanism queries providers in a user-defined order, stopping at the first successful result to optimize cost and coverage. This approach can increase email enrichment rates from a typical 30-40% with a single source to over 70%. The platform also incorporates compliance features relevant to regional laws like GDPR, with integrated providers such as DropContact marketing themselves as 100% GDPR compliant. By combining geographic routing with a multi-provider sequence, users can effectively balance the tradeoff between data coverage and accuracy for international prospecting.

Clay's AI platform summarizes recent product launches for SDRs using an AI agent called Claygent, which performs live web scraping of public company websites. Users provide a company domain and a natural language prompt, and the agent navigates the site to find and synthesize relevant information. The agent is powered by large language models like OpenAI's GPT-4 and Anthropic's Claude to understand and summarize unstructured text. The output is a concise summary populated directly into the user's Clay table, which can be used for email personalization and call preparation. This process is limited to publicly available information but effectively handles variable website structures. The automation significantly reduces manual research time and helps increase the relevancy of sales outreach.

Clay's data credit waterfall system manages costs by allowing users to configure a sequential query of multiple data providers. The system operates on a 'stop-on-first-valid' principle, ceasing the enrichment process and consuming credits only from the first provider that returns a successful result. Users can prioritize providers based on cost, sequencing free or low-cost sources before premium, credit-based APIs. A key cost-saving feature allows users to integrate their own API keys from providers like Apollo, which results in zero Clay credit consumption for those lookups. While this system optimizes credit usage, the sequential nature may increase total enrichment time compared to querying a single premium source directly. This approach enables businesses to maximize data coverage, achieving rates of 80-95%, while controlling enrichment expenditures.

Clay aggregates sales intelligence data from over 150 different providers and databases into a single, unified platform. It operates as an aggregation layer using a 'Waterfall Enrichment' model, which sequentially queries a user-configured list of providers to maximize data coverage and fill rates. The platform offers a wide range of data categories, including contact, firmographic, technographic, and intent signal data from providers like Clearbit, Apollo.io, and People Data Labs. Users can also integrate their own subscriptions from other tools via a 'Bring-Your-Own-Key' (BYOK) model to further enhance their data sourcing. Key limitations include a steep learning curve, potentially unpredictable credit costs due to the waterfall system, and data accuracy that varies depending on the underlying provider. This aggregation model provides comprehensive data access without requiring separate subscriptions to each source but requires careful management of enrichment workflows to be effective.