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Business & Marketing

Clay

Clay brings company search, people discovery, enrichment, and AI email drafting into one outreach workflow, but it still needs validation steps and has notable Chrome/API limits.

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Company + people searchContact enrichmentAI email draftingLinkedIn/API limitations

Strong end-to-end workflow, but not frictionless

Clay performed well on the core outreach workflow in this test: it found companies, surfaced employees, enriched contact records, and generated personalized email drafts inside one workspace. The tradeoff is that users still need to validate important details. Employee coverage was useful but not fully complete, enriched contact records can include confidence caveats, the Chrome extension did not work on LinkedIn profiles, and Clay does not expose a traditional API for direct prospecting outside enterprise-limited lookups.

Download video: Demo video
Hands-on walkthrough recorded during the Clay outreach workflow test.

In-Depth Review

Our detailed analysis of Clay — features, performance, and real-world testing.

AV
Ajay V
AI Demos Team
Verified Review

Feature-by-Feature Breakdown

Company lookup from a known company input
Clay successfully returned a matching company record and exposed enough company context to continue the workflow.
▾
Test Summary
Feature tested: Company lookup from a known company input
Result: Passed — Clay successfully returned a matching company record and exposed enough company context to continue the workflow.

Feature tested: Company lookup from a known company input

Result: Passed

Verdict: Clay successfully returned a matching company record and exposed enough company context to continue the workflow.

Expected behavior: Tested Clay's Find Companies workflow with a known company/domain-style search to see whether the platform could identify the target company and provide usable company-level context for prospecting.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): Clay's Find Companies workflow narrowed the search to a single matching company record. The result screen exposed a full filter sidebar plus company-level detai — clay-clay-find-companies-one-result.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): Clay's Find Companies workflow narrowed the search to a single matching company record. The result screen exposed a full filter sidebar plus company-level detai — clay-clay-find-companies-one-result.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Clay handled the initial company-identification step well, but company discovery by itself is only the start of the outreach workflow.

Tested Clay's Find Companies workflow with a known company/domain-style search to see whether the platform could identify the target company and provide usable company-level context for prospecting.

INPUT
Domain-based company search for a known target company.
↓
image
Output artifact for "Company lookup from a known company input" test: Clay's Find Companies workflow narrowed the search to a single matching company record. The result screen exposed a full filter sidebar plus company-level detai, clay-clay-find-companies-one-result.png

Clay's Find Companies workflow narrowed the search to a single matching company record. The result screen exposed a full filter sidebar plus company-level details in the preview table, which was enough for initial company research, although deeper prospect work still required moving into later workflow steps.

Bottom Line
Clay handled the initial company-identification step well, but company discovery by itself is only the start of the outreach workflow.
Employee discovery with prospect filters
Clay surfaced employee records with good prospecting context, but contact data was not shown directly in search results.
▾
Test Summary
Feature tested: Employee discovery with prospect filters
Result: Passed — Clay surfaced employee records with good prospecting context, but contact data was not shown directly in search results.

Feature tested: Employee discovery with prospect filters

Result: Passed

Verdict: Clay surfaced employee records with good prospecting context, but contact data was not shown directly in search results.

Expected behavior: Tested whether Clay could return employees associated with the target company and provide enough fields to narrow down relevant prospects.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): Clay's Find People workflow returned a single matching person record and showed company, job title, location, and LinkedIn URL in the results table. The screen — clay-clay-find-people-one-result.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): Clay's Find People workflow returned a single matching person record and showed company, job title, location, and LinkedIn URL in the results table. The screen — clay-clay-find-people-one-result.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Clay is strong at finding people and giving enough context to judge relevance, but email discovery happens later in the workflow.

Tested whether Clay could return employees associated with the target company and provide enough fields to narrow down relevant prospects.

INPUT
People search for a known employee associated with the target company, filtered down to one result.
↓
image
Output artifact for "Employee discovery with prospect filters" test: Clay's Find People workflow returned a single matching person record and showed company, job title, location, and LinkedIn URL in the results table. The screen, clay-clay-find-people-one-result.png

Clay's Find People workflow returned a single matching person record and showed company, job title, location, and LinkedIn URL in the results table. The screen also exposed filters for job title, experience, location, professional bio, network reach, languages, and education, which makes narrowing a prospect list straightforward. Contact information was not shown directly in this search view and required follow-up enrichment.

Bottom Line
Clay is strong at finding people and giving enough context to judge relevance, but email discovery happens later in the workflow.
Contact enrichment with validation signals
Clay enriched records with contact and mail-system metadata, but the surfaced email confidence still needed review.
▾
Test Summary
Feature tested: Contact enrichment with validation signals
Result: Passed — Clay enriched records with contact and mail-system metadata, but the surfaced email confidence still needed review.

Feature tested: Contact enrichment with validation signals

Result: Passed

Verdict: Clay enriched records with contact and mail-system metadata, but the surfaced email confidence still needed review.

Expected behavior: Opened an enriched employee record to inspect what Clay adds after people discovery, including email data and validation metadata.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): For the selected Shreyas Dhaware record, Clay surfaced an email value plus validation fields such as status, credits consumed, MX record, MX provider, security- — clay-clay-table-cell-details-shreyas-dhaware.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): For the selected Shreyas Dhaware record, Clay surfaced an email value plus validation fields such as status, credits consumed, MX record, MX provider, security- — clay-clay-table-cell-details-shreyas-dhaware.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Clay gives more than just an email string—it gives supporting validation data—but users still need to judge whether a record is trustworthy enough to use.

Opened an enriched employee record to inspect what Clay adds after people discovery, including email data and validation metadata.

INPUT
Selected employee record: Shreyas Dhaware in a Clay workspace table.
↓
image
Output artifact for "Contact enrichment with validation signals" test: For the selected Shreyas Dhaware record, Clay surfaced an email value plus validation fields such as status, credits consumed, MX record, MX provider, security-, clay-clay-table-cell-details-shreyas-dhaware.png

For the selected Shreyas Dhaware record, Clay surfaced an email value plus validation fields such as status, credits consumed, MX record, MX provider, security-gateway flags, and expanded MX records. The same record also showed the message 'Email was not confidently found and verified,' which is a useful reminder that the platform provides supporting signals rather than a guarantee of contact accuracy.

Bottom Line
Clay gives more than just an email string—it gives supporting validation data—but users still need to judge whether a record is trustworthy enough to use.
Employee coverage for a target company
Clay returned a broad employee list across roles, but completeness still benefited from outside validation.
▾
Test Summary
Feature tested: Employee coverage for a target company
Result: Passed — Clay returned a broad employee list across roles, but completeness still benefited from outside validation.

Feature tested: Employee coverage for a target company

Result: Passed

Verdict: Clay returned a broad employee list across roles, but completeness still benefited from outside validation.

Expected behavior: Checked Clay's coverage for FutureSmart AI to see how many employees it surfaced and whether the list looked current enough for prospecting.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): Clay returned a sizable list of FutureSmart AI employees spanning roles such as Founder & CEO, AI research intern, generative AI engineer, product manager, mach — clay-clay-find-people-futuresmart-ai-results.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): Clay returned a sizable list of FutureSmart AI employees spanning roles such as Founder & CEO, AI research intern, generative AI engineer, product manager, mach — clay-clay-find-people-futuresmart-ai-results.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Clay provides a useful workforce view for prospecting, but it should not be treated as a guaranteed complete employee roster when completeness matters.

Checked Clay's coverage for FutureSmart AI to see how many employees it surfaced and whether the list looked current enough for prospecting.

INPUT
People search scoped to the company FutureSmart AI.
↓
image
Output artifact for "Employee coverage for a target company" test: Clay returned a sizable list of FutureSmart AI employees spanning roles such as Founder & CEO, AI research intern, generative AI engineer, product manager, mach, clay-clay-find-people-futuresmart-ai-results.png

Clay returned a sizable list of FutureSmart AI employees spanning roles such as Founder & CEO, AI research intern, generative AI engineer, product manager, machine learning engineer, AI content creator, partnership development, HR operations, frontend development, and data engineering. The researcher noted that many of the surfaced individuals aligned with current company information, but external validation still showed some gaps in coverage.

Bottom Line
Clay provides a useful workforce view for prospecting, but it should not be treated as a guaranteed complete employee roster when completeness matters.
Natural-language prospect search to structured filters
Clay's AI assistant reduced setup time by translating prompts into filters, but the generated results still needed review and refinement.
▾
Test Summary
Feature tested: Natural-language prospect search to structured filters
Result: Passed — Clay's AI assistant reduced setup time by translating prompts into filters, but the generated results still needed review and refinement.

Feature tested: Natural-language prospect search to structured filters

Result: Passed

Verdict: Clay's AI assistant reduced setup time by translating prompts into filters, but the generated results still needed review and refinement.

Expected behavior: Tested whether Clay's AI assistant could convert natural-language prospecting requests into actionable company-search workflows for outreach.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): Clay generated a structured company search from the natural-language request, adding filters such as software development, private held companies, small company — clay-clay-find-companies-ai-startups-customer-support.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): Clay generated a structured company search from the natural-language request, adding filters such as software development, private held companies, small company — clay-clay-find-companies-ai-startups-customer-support.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): For an AI video-editing request, Clay again created a structured search with AI/video-related keywords and prepared a preview workflow. The setup showed that th — clay-clay-find-companies-ai-video-editing-tools.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): For an AI video-editing request, Clay again created a structured search with AI/video-related keywords and prepared a preview workflow. The setup showed that th — clay-clay-find-companies-ai-video-editing-tools.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: The assistant is useful for turning a prompt into a draft search, but it does not remove the need to audit the resulting filters and company list.

Tested whether Clay's AI assistant could convert natural-language prospecting requests into actionable company-search workflows for outreach.

INPUT
Find 10 AI startups in customer support automation and identify the most relevant contacts for partnership outreach.
↓
image
Output artifact for "Natural-language prospect search to structured filters" test: Clay generated a structured company search from the natural-language request, adding filters such as software development, private held companies, small company, clay-clay-find-companies-ai-startups-customer-support.png

Clay generated a structured company search from the natural-language request, adding filters such as software development, private held companies, small company-size and funding constraints, and keywords including AI, customer support, support automation, chatbot, and conversational AI. The preview still surfaced large names like IBM, Amazon, Accenture, Meta, Oracle, Infosys, and OpenAI despite the '10 AI startups' framing, which shows that the assistant can broaden beyond the original intent if the generated filters are not carefully checked.

INPUT
Find 10 startups focused on AI video editing tools.
↓
image
Output artifact for "Natural-language prospect search to structured filters" test: For an AI video-editing request, Clay again created a structured search with AI/video-related keywords and prepared a preview workflow. The setup showed that th, clay-clay-find-companies-ai-video-editing-tools.png

For an AI video-editing request, Clay again created a structured search with AI/video-related keywords and prepared a preview workflow. The setup showed that the assistant can translate vague market requests into editable filters quickly, but the user still needs to inspect whether those filters actually match the target segment.

Bottom Line
The assistant is useful for turning a prompt into a draft search, but it does not remove the need to audit the resulting filters and company list.
LinkedIn-side prospecting via Chrome extension
This workflow failed in testing because the Clay extension was not available on LinkedIn profile pages.
▾
Test Summary
Feature tested: LinkedIn-side prospecting via Chrome extension
Result: Passed — This workflow failed in testing because the Clay extension was not available on LinkedIn profile pages.

Feature tested: LinkedIn-side prospecting via Chrome extension

Result: Passed

Verdict: This workflow failed in testing because the Clay extension was not available on LinkedIn profile pages.

Expected behavior: Tested whether the Chrome extension could reduce manual effort by surfacing Clay data while browsing a prospect's LinkedIn profile.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): On the LinkedIn profile for Tytus Gołas, Clay's sidebar explicitly stated that 'The Clay Chrome extension is not available on LinkedIn.' That means the extensio — clay-linkedin-profile-clay-extension-warning.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): On the LinkedIn profile for Tytus Gołas, Clay's sidebar explicitly stated that 'The Clay Chrome extension is not available on LinkedIn.' That means the extensio — clay-linkedin-profile-clay-extension-warning.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: If your workflow depends on enriching people directly from LinkedIn, Clay's extension was a clear limitation in this evaluation.

Tested whether the Chrome extension could reduce manual effort by surfacing Clay data while browsing a prospect's LinkedIn profile.

INPUT
LinkedIn profile page for Tytus Gołas.
↓
image
Output artifact for "LinkedIn-side prospecting via Chrome extension" test: On the LinkedIn profile for Tytus Gołas, Clay's sidebar explicitly stated that 'The Clay Chrome extension is not available on LinkedIn.' That means the extensio, clay-linkedin-profile-clay-extension-warning.png

On the LinkedIn profile for Tytus Gołas, Clay's sidebar explicitly stated that 'The Clay Chrome extension is not available on LinkedIn.' That means the extension could not be used for direct prospect research or enrichment inside LinkedIn during this test.

Bottom Line
If your workflow depends on enriching people directly from LinkedIn, Clay's extension was a clear limitation in this evaluation.
Website-side extraction via Chrome extension
The extension could detect page data, but it did not return the company and contact insights needed for outreach research.
▾
Test Summary
Feature tested: Website-side extraction via Chrome extension
Result: Passed — The extension could detect page data, but it did not return the company and contact insights needed for outreach research.

Feature tested: Website-side extraction via Chrome extension

Result: Passed

Verdict: The extension could detect page data, but it did not return the company and contact insights needed for outreach research.

Expected behavior: Tested the Chrome extension on a company website to see whether it could extract useful prospecting data while browsing.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): On the Marblism homepage, Clay's extension identified an autodetected list of six items and offered to export or add the data to a workspace. In this test, it d — clay-marblism-homepage-clay-autodetected-list.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): On the Marblism homepage, Clay's extension identified an autodetected list of six items and offered to export or add the data to a workspace. In this test, it d — clay-marblism-homepage-clay-autodetected-list.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Clay's extension can recognize webpage structure, but that did not translate into meaningful prospect or contact discovery in this test.

Tested the Chrome extension on a company website to see whether it could extract useful prospecting data while browsing.

INPUT
Company website test on marblism.com.
↓
image
Output artifact for "Website-side extraction via Chrome extension" test: On the Marblism homepage, Clay's extension identified an autodetected list of six items and offered to export or add the data to a workspace. In this test, it d, clay-marblism-homepage-clay-autodetected-list.png

On the Marblism homepage, Clay's extension identified an autodetected list of six items and offered to export or add the data to a workspace. In this test, it did not surface company details, employee records, or contact information, so the extension added only limited research value for outreach preparation.

Bottom Line
Clay's extension can recognize webpage structure, but that did not translate into meaningful prospect or contact discovery in this test.
API and automation readiness
Clay supports automation, but not through a traditional public prospecting API.
▾
Test Summary
Feature tested: API and automation readiness
Result: Passed — Clay supports automation, but not through a traditional public prospecting API.

Feature tested: API and automation readiness

Result: Passed

Verdict: Clay supports automation, but not through a traditional public prospecting API.

Expected behavior: Investigated how well Clay fits internal automation use cases that need programmatic access for prospect discovery, enrichment, and data handoff.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): Clay Support stated that Clay does not have a traditional API users can query directly. Instead, it supports sending data into tables through webhooks, pushing — clay-clay-support-api-integration-chat.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): Clay Support stated that Clay does not have a traditional API users can query directly. Instead, it supports sending data into tables through webhooks, pushing — clay-clay-support-api-integration-chat.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Clay is better suited to orchestrated workflows and table-based automation than to direct API-first prospect search and enrichment.

Investigated how well Clay fits internal automation use cases that need programmatic access for prospect discovery, enrichment, and data handoff.

INPUT
Support query: does Clay support API integration?
↓
image
Output artifact for "API and automation readiness" test: Clay Support stated that Clay does not have a traditional API users can query directly. Instead, it supports sending data into tables through webhooks, pushing, clay-clay-support-api-integration-chat.png

Clay Support stated that Clay does not have a traditional API users can query directly. Instead, it supports sending data into tables through webhooks, pushing data out through HTTP API actions, and wrapping workflows with tools like Make or Zapier; the same reply also noted that enrichments can take a minute or more, and that only enterprise customers get a limited People and Company API for basic lookups.

Bottom Line
Clay is better suited to orchestrated workflows and table-based automation than to direct API-first prospect search and enrichment.
AI-generated outreach emails inside the workflow
Clay produced personalized outreach drafts with visible reasoning fields and could run them across a table of leads.
▾
Test Summary
Feature tested: AI-generated outreach emails inside the workflow
Result: Passed — Clay produced personalized outreach drafts with visible reasoning fields and could run them across a table of leads.

Feature tested: AI-generated outreach emails inside the workflow

Result: Passed

Verdict: Clay produced personalized outreach drafts with visible reasoning fields and could run them across a table of leads.

Expected behavior: Tested Clay's email-generation workflow using company and prospect context, then reviewed both the agent setup and generated outputs for personalization depth.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): Clay's Email Outreach Writer showed a full agent prompt with context, objective, and instructions, and the test pane generated an email draft for Daniel Kim. Th — clay-clay-email-outreach-writer-sculptor.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): Clay's Email Outreach Writer showed a full agent prompt with context, objective, and instructions, and the test pane generated an email draft for Daniel Kim. Th — clay-clay-email-outreach-writer-sculptor.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): In the Marketing Leads AI Voice table, Clay showed auto-run progress and generated output fields such as Email Body, Reasoning, Confidence, Steps Taken, and Sub — clay-clay-marketing-leads-ai-voice-table.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): In the Marketing Leads AI Voice table, Clay showed auto-run progress and generated output fields such as Email Body, Reasoning, Confidence, Steps Taken, and Sub — clay-clay-marketing-leads-ai-voice-table.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): For Shreyas Dhaware, Clay generated a draft that referenced FutureSmart AI's work around multi-agent architectures, LangGraph RAG agents, and AI knowledge graph — clay-clay-marketing-leads-ai-voice-cell-details.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): For Shreyas Dhaware, Clay generated a draft that referenced FutureSmart AI's work around multi-agent architectures, LangGraph RAG agents, and AI knowledge graph — clay-clay-marketing-leads-ai-voice-cell-details.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Clay can create materially personalized outreach drafts inside the same prospecting workflow, but the outputs still warrant human review before sending.

Tested Clay's email-generation workflow using company and prospect context, then reviewed both the agent setup and generated outputs for personalization depth.

INPUT
Prospect-email generation task using person and company context; visible test data included personName Daniel Kim and personTitle VP of Engineering with instructions to write a concise, value-first outbound email and explain the personalization choices.
↓
image
Output artifact for "AI-generated outreach emails inside the workflow" test: Clay's Email Outreach Writer showed a full agent prompt with context, objective, and instructions, and the test pane generated an email draft for Daniel Kim. Th, clay-clay-email-outreach-writer-sculptor.png

Clay's Email Outreach Writer showed a full agent prompt with context, objective, and instructions, and the test pane generated an email draft for Daniel Kim. This demonstrated that Clay can use structured prospect inputs to generate outreach copy directly inside a Claygent rather than forcing the user into a separate copy tool.

INPUT
A table-based outreach workflow named Marketing Leads AI Voice, run across multiple leads.
↓
image
Output artifact for "AI-generated outreach emails inside the workflow" test: In the Marketing Leads AI Voice table, Clay showed auto-run progress and generated output fields such as Email Body, Reasoning, Confidence, Steps Taken, and Sub, clay-clay-marketing-leads-ai-voice-table.png

In the Marketing Leads AI Voice table, Clay showed auto-run progress and generated output fields such as Email Body, Reasoning, Confidence, Steps Taken, and Subject Line for lead rows. The visible cell details showed that email generation can be operationalized at table level rather than only as a one-off prompt.

INPUT
Selected lead review for Shreyas Dhaware at FutureSmart AI.
↓
image
Output artifact for "AI-generated outreach emails inside the workflow" test: For Shreyas Dhaware, Clay generated a draft that referenced FutureSmart AI's work around multi-agent architectures, LangGraph RAG agents, and AI knowledge graph, clay-clay-marketing-leads-ai-voice-cell-details.png

For Shreyas Dhaware, Clay generated a draft that referenced FutureSmart AI's work around multi-agent architectures, LangGraph RAG agents, and AI knowledge graphs. The same output included a high-confidence label and a multi-step reasoning trace, which indicates that Clay's personalization is based on research-like context rather than completely generic cold-email boilerplate.

Bottom Line
Clay can create materially personalized outreach drafts inside the same prospecting workflow, but the outputs still warrant human review before sending.

Pricing seen during testing

The plan selection screen showed three tiers with monthly pricing and credit/action limits.

Launch
$185/mo
15,000 actions/mo; 2,500 data credits/mo.
Growth
$495/mo
40,000 actions/mo; 6,000 data credits/mo. Marked as Recommended in the pricing screen.
Enterprise
Custom
Custom pricing. Visible extras included data warehouse sync, unlimited AI sync, unlimited rows with Audiences (Beta), SSO, RBAC, and a dedicated growth strategist.

Pricing was taken from the visible plan-selection screen only.

Is This Right For You?

A side-by-side guide based on our hands-on testing.

✓ Use This If
●You want company search, people search, enrichment, and AI email generation in one workspace.
●You are comfortable reviewing generated filters and validating contact quality before using records for outreach.
●You prefer workflow automation through tables, webhooks, and connectors rather than a direct search API.
✕ Skip This If
●You need a Chrome extension that works directly on LinkedIn profiles.
●You need a traditional public API for direct prospect search and enrichment.
●You need guaranteed complete employee coverage or fully confident email verification without extra validation.
Business & MarketingOtherotherMarketingFounders
Yes. In testing, Clay returned a matching company record from a known company search and surfaced employee records with fields such as name, title, location, and LinkedIn URL. It worked well as a starting point for prospect discovery.
No. The people search results showed prospect context like job title, location, and LinkedIn URL, but contact information required a later enrichment step. In the enrichment view, Clay added validation fields such as MX data and status indicators.
Useful, but not something to trust blindly. For one selected record, Clay showed an email value and detailed validation metadata, but also displayed a message saying the email was not confidently found and verified. That means the platform gives supporting signals, not certainty.
Yes. Clay's assistant converted prompts like finding AI startups in customer support automation or AI video editing into structured company-search filters. The time-saving benefit is real, but the results still needed review because one 'AI startups' prompt still previewed large companies like IBM, Amazon, and Accenture.
Not in this test. On a LinkedIn profile page, Clay explicitly showed that its Chrome extension is not available on LinkedIn, so it could not be used for direct profile-side prospect research there.
No. Clay Support stated that Clay does not offer a traditional API users can query directly. Programmatic use is mainly through webhooks, HTTP API actions, and tools like Make or Zapier, while enterprise customers get a limited People and Company API for basic lookups.
Yes. The tested Email Outreach Writer used company and prospect context to generate drafts, and table outputs showed fields like Email Body, Reasoning, Confidence, Steps Taken, and Subject Line. One reviewed email for Shreyas Dhaware referenced specific FutureSmart AI themes such as multi-agent architectures and LangGraph RAG agents, which indicates meaningful personalization.
The visible pricing screen showed Launch at $185/mo with 15,000 actions and 2,500 data credits per month, Growth at $495/mo with 40,000 actions and 6,000 data credits per month, and Enterprise as custom pricing.

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