Clay vs Enginy (2026)

Clay buys data from more than 200 providers in one place, syncs CRMs, pushes ad audiences to LinkedIn, Meta and Google, sends through its own sequencer or connected email tools, and offers HTTP API integrations and webhooks for anything unlisted, so agents take authenticated action in outside systems.

Sourceclay.comread 2026-10-01

The integration catalog has its own page, with a logo wall showing twelve more connectors beyond those named. Two way CRM sync writes verified contact and company data into HubSpot, Salesforce, Pipedrive and Zoho and keeps them updated in real time. Outreach sends from connected mailboxes and social accounts, and reply alerts post into a Slack channel. More than thirty enrichment providers feed the data layer.

Sourceenginy.ai integrations page and CRM sync product pageread 2026-09-01

The core product is a table based workflow builder with enrichment columns, AI columns, conditional logic, routing, and trigger driven automations, positioned as the orchestration layer for GTM.

Sourceclay.comread 2026-10-01

The sequence builder composes multi step campaigns from named action nodes (send connection request, send message, like last social post, visit profile and send email). Wait steps of a set duration and condition nodes sit between them, and one condition node tests whether a contact has accepted an invitation and routes the sequence accordingly.

Around the builder, the platform orchestrates import, waterfall enrichment, list cleaning against ICP rules, outreach, reply handling and booking. The developer documentation has a step by step reference for every Advanced Workflow import, enrichment and action step.

Sourceenginy.ai multichannel sequences and docs.enginy.ai Advanced Workflow referenceread 2026-09-01

Trigger driven workflows fire on signals like headcount change, technographics, hiring, and web intent, plus webhooks and scheduled refreshes, and push to email tools, ad audiences, and CRMs.

Sourceclay.comread 2026-10-01

Outreach starts on signals, without a person asking. Enginy detects hiring changes, job transitions, tech stack updates and funding activity and starts outreach on them, and replies arriving in the smart inbox are categorized by intent and drafted for automatically.

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Grounding is assembled for each request. The customer controls which table columns are included as context on each AI action, a My Business Context setting carries company positioning and brand voice into message drafting, Claygents research prospects on instruction, and some models accept document upload.

Data is sent to a model only when the customer explicitly runs an action. There is no maintained retrieval structure over the customer's own corpus, with no ingestion of a document set, no index the agent queries at run time and no retrieval interface. The natural language company search runs against Clay's own database of fifty million companies.

Sourceuniversity.clay.com/docs/ai-in-clayread 2026-09-01

Agents work from data about each prospect and from standing configuration, not from a body of the customer's own documents. Waterfall enrichment draws verified contact and company data from more than thirty providers, and AI deep research browses the web for profile and company insight on each record. List building uses real market signals together with the customer's company context. An AI Playbook holds standing instructions the agents work from, which the customer writes. Enginy names no document or file ingestion, no knowledge base and no retrieval over the customer's own material.

Sourceenginy.ai AI powered research and find pagesread 2026-09-01

State persists across runs in the customer's tables. Claygent research results are stored back into the customer's table under their control, tables are continuously refreshed as accounts are monitored for headcount, technographic, hiring and intent changes, and the customer can delete individual records, tables or an entire workspace at any time, with workspace deletion removing all data after thirty days. Nothing describes the agent accumulating learning across runs or reading back a memory distinct from the table it writes to, and Sculptor's chat context is explicitly transient.

Sourceuniversity.clay.com/docs/ai-in-clayread 2026-09-01

State persists across a long running motion. Contacts, companies and lists are standing objects in the workspace, with enrichment written back. The smart inbox keeps full conversation threads across email and social, carrying intent tags and counts forward. An AI Playbook holds standing configuration the agents work from, and CRM sync keeps the durable record updated. Enginy does not say what an agent retains between campaigns or for how long, and names no store an agent writes to and reads back apart from the shared contact records.

Sourceenginy.ai smart inbox and CRM sync product pagesread 2026-09-01

Control is exercised before an action runs. AI features are opt-in through the customer's own actions, data leaves a table only when the customer explicitly runs the action, the customer chooses which columns an action may see, and admins control who in the organization may use AI features, with role-based permissions, function-level permissions on the MCP surface and per-rep credit budgets. Opt-out and do-not-call handling covers outbound compliance. Nothing lets a customer mark particular actions as requiring sign-off, name who signs off or define what happens while approval is pending.

Sourceuniversity.clay.com/docs/ai-in-clay and clay.com/pricingread 2026-09-01

In the smart inbox, every conversation sits in one queue with a separate AI Drafts count. AI prepares a reply, and a rep can generate, edit and send it. Intent tags show who to answer first, and Slack alerts fire when a lead replies with interest. The agent can also keep conversations going autonomously and book meetings. Enginy names no way for a customer to mark actions as needing sign off, name who signs off or set what happens while approval is pending.

Sourceenginy.ai smart inbox product pageread 2026-09-01

Role based permissions control who can access data, and the Enterprise plan adds sign in with SSO and role based access control. Clay states an active SOC 2 Type II certification and GDPR compliance. Both SSO and RBAC are limited to the Enterprise plan.

Sourceuniversity.clay.com/docs/ai-in-clayread 2026-10-01

Independent audits back the company's ISO 27001 certification, and it complies with EU data protection rules. It has a dedicated security policy page, a data processing agreement, a data opt out page and a privacy policy. Enginy names no access model for who inside a customer's organization can do what, and no SSO, SAML or audit surface for customers.

Sourceenginy.ai security certifications section and security policy pageread 2026-09-01

Execution is visible at the row level. Each enrichment column shows its own result per record, Claygents process row by row with research results written back into the table, failed actions surface for troubleshooting, and a Debug with AI feature analyzes error messages and logs against the documentation to suggest fixes. Credit and usage consumption is tracked in detail. There is no run-history or audit surface that reconstructs a single agent run end to end, showing the steps taken and the tools called in sequence, as against per-column outputs and error logs.

Sourceuniversity.clay.com/docs/ai-in-clay and clay.com/pricingread 2026-09-01

Conversations are tagged by intent with a stated reason, and the platform provides conversation and insight analytics. These cover conversations and campaigns, and Enginy names no tracing of agent execution.

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Customer data is primarily processed in the United States on AWS US-East, the product is cloud only, and no region selection, self hosting, on premises or VPC option is offered. Warehouse sync moves data but does not relocate the service.

Sourceuniversity.clay.com/docs/ai-in-clayread 2026-10-01

Delivery is cloud SaaS only. The platform complies with EU data protection rules but offers no self hosted, on premises or in VPC deployment option.

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Use case templates on Clay University form a browsable library in their own navigation section, alongside courses and docs, which a customer can browse and run. Around it sit named packaged components for named jobs, namely Claygent as a repeatable research agent with its own builder and playground, Sculptor for table building and debugging, the Sequencer for campaigns, Audiences, and waterfall enrichment.

Sourceuniversity.clay.com use-case-templates and docs indexread 2026-09-01

Each customer configures its own messaging and enrichment flows, such as ICP rules and sequences. Enginy names no library of prebuilt agents, templates or packs.

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Clay publishes a complete table of available models by provider, including its own Helium, Neon, Argon and Clay Navigator, OpenAI's GPT 5.5 and 5.4 families through o3 and o4 Mini, Anthropic's Claude 4.8, 4.7 and 4.6 Opus, Sonnet 5, 4.6 Sonnet and 4.5 Haiku, Gemini 3.1 Pro and the Flash line, and image models from BlackForestLabs, Playground, Segmind and Stability. On top of the provider list the customer can bring their own API keys for supported providers, which unlocks MCP server support inside the Use AI action.

Sourceuniversity.clay.com/docs/ai-in-clayread 2026-09-01

The message composition surface shows the model in use, OpenAI GPT 5.2, alongside the personalization fields. Enginy names no customer choice of model, no alternative models, no bring your own key option and no routing policy.

Sourceenginy.ai multichannel sequences composition panelread 2026-09-01

MCP is both served and consumed, alongside a public API.

Clay exposes an MCP server at api.clay.com/v3/mcp that lets an external assistant, IDE, agent host or partner product run Clay's find-and-enrich tools and custom functions on behalf of a signed-in user, with OAuth discovery through standard protected-resource and authorization-server metadata and Dynamic Client Registration as a self-service path, plus authorize, token, device-authorization, registration and revocation endpoints.

Admins govern that surface with function-level permissions and per-rep credit budgets. Clay also consumes MCP servers inside the Use AI action when the customer brings their own keys. A public API, HTTP API integrations and webhooks are available, and Clay ships as a connector in ChatGPT, Claude and Codex.

Sourceuniversity.clay.com connect-to-clay-mcp and ai-in-clay docsread 2026-09-01

Developer documentation at docs.enginy.ai covers four programmatic surfaces. A REST API for custom integrations has endpoints, authentication and guides for contacts, campaigns and webhooks. A hosted MCP server connects Claude or another AI assistant to an Enginy workspace. A complete reference covers Advanced Workflow import, enrichment and action steps. A specification lets a customer sync an in house CRM by implementing a small set of endpoints, with a reference implementation in Enginy's GitHub organization.

Sourcedocs.enginy.ai introduction and github.com/Genesy-AIread 2026-09-01

A change can be evaluated before it reaches customers. The sequence builder lets the customer write, personalize and pressure-test the emails a campaign will send before a single one goes out, and A/B testing runs two versions of campaign copy against each other and then sends the remaining leads to whichever performs better, a scored comparison with a decision rule. Around that sit failed-action troubleshooting, a Debug with AI feature that analyzes error logs against the documentation to suggest fixes, and Sculptor for identifying issues in a table.

Sourceuniversity.clay.com build-your-sequence and ab-test-sequence-copy docsread 2026-09-01

Messaging is tuned with the support team's help, not through an evaluation framework, and Enginy names no agent testing, debugging or evaluation product for users.

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Clay offers web scraping, and Claygents are AI agents that navigate websites, extract information and decide which sources to consult, running row by row with results stored back into the customer's table. The Use AI action separately performs web research that searches and synthesizes information from the web. That is scraping and headless retrieval wired into the platform, and there is no control of a real interface, whether a hosted or local browser, a desktop session or remote computer control the agent drives.

Sourceuniversity.clay.com web scraping docs and ai-in-clay Claygents sectionread 2026-09-01

The agent acts inside LinkedIn through named sequence steps that send a connection request, send a message, like a contact's last social post and visit a profile, with a condition node that tests whether an invitation was accepted. A Deep Research step lets the AI agent browse the internet to find information. Enginy does not say how either the social actions or the browsing are carried out.

Sourceenginy.ai multichannel sequences and AI research surfacesread 2026-09-01