Clay

Automation with Clay turns the enrichment and outreach-prep work your team does by hand into scheduled or triggered table runs. Rather than a general workflow engine, Clay’s automation is built around one core loop: a row enters a table, moves through enrichment and formula columns, and exits through a webhook, HTTP API call, or native integration action.

We start by mapping your current manual sequence—pull a list, look up contacts, check fit, push to your sequencer—into Clay columns: a waterfall enrichment stack that tries multiple data providers in order and only pays for the first hit, formula columns that combine or clean fields, and an action column that fires once the row is ready.

How Clay’s automation actually runs is table-native: a run can be triggered manually, on a schedule, or when new rows land, and each row runs independently through the same column pipeline—there’s no separate flowchart-style orchestration layer to maintain.

Who this is for—RevOps and growth teams who need lead lists enriched and routed on a recurring basis without manual exports and re-imports between tools. It’s not built for general back-office process automation outside GTM data—for that, you’d pair Clay with a dedicated automation platform.

Delivery approach starts with one high-volume workflow—new inbound leads, a target account list, a re-enrichment pass on stale CRM data—built end-to-end in a single table, tested on a sample, then scheduled or connected to a live trigger.

Deliverables include the built table with its enrichment waterfall, formulas, and exit actions, a credit-consumption estimate per run, and a short runbook covering what to check when a provider in the waterfall starts failing or a webhook destination changes.

As table volume grows, we split single tables into linked workbooks—companies, contacts, engagement—so each automation stays fast and its credit spend stays traceable, rather than one table trying to do everything.

Practical automations teams run in Clay:

• New inbound form fills trigger a waterfall enrichment run and route qualified leads straight to a rep’s CRM queue.
• A scheduled table run re-checks target accounts weekly for hiring signals or leadership changes worth a fresh outreach touch.
• Stale CRM contacts get pulled into Clay on a schedule, re-enriched, and pushed back through the native CRM integration.
• A webhook fires the moment a row’s enrichment completes, dropping the contact into an outbound sequencing tool.
• Formula columns normalize company size and industry fields pulled from different providers into one consistent value.
• A Slack alert fires when an enrichment run flags a high-fit account so a rep can act the same day.
• List-building runs pull companies matching firmographic filters, waterfall-enrich contacts at each one, and export the finished list.
• HTTP API actions post enriched records directly into a custom internal tool instead of a supported native integration.
• A recurring run re-verifies email addresses before a campaign send to protect sender reputation.
• Conditional logic inside a table routes enterprise-fit accounts to one pipeline and SMB-fit accounts to another.

Clay’s automation strength is enrichment-to-action for GTM data specifically—it’s the layer between “we have a list” and “the right message reached the right person,” not a replacement for a general-purpose workflow tool.

Intro Clay-driven automation accelerates execution and improves reliability across mission-critical processes. Below are representative use cases designed for immediate impact.

• Automated lead routing and routing-lag minimization using Clay’s rules engine.
• Invoice reconciliation automation that matches line items with vendor feeds through Clay.
• Payment status notifications triggered by events captured in Clay.
• Data normalization pipelines feeding dashboards powered by Clay governance.
• Alert triage automation that categorizes incidents and assigns owners via Clay.
• Customer onboarding provisioning with role assignment through Clay workflows.
• Document processing automation that extracts, validates, and stores data using Clay.
• Inventory replenishment triggers coordinated across ERP and supply chain systems with Clay.
• Quality checks and issue escalation embedded in release pipelines via Clay.
• Audit-ready runbooks automatically generated by Clay for compliance reviews.

These examples show how Clay enables scalable, disciplined automation that adapts to your evolving environment.

AI with Clay centers on Claygent, Clay’s built-in AI research agent, and on AI enrichment columns that call OpenAI and Anthropic models directly inside your lead and account tables. We help you design and roll out these columns so research and personalization that used to take an SDR hours happens automatically, row by row.

We start by mapping the manual research your team already does—checking a company’s tech stack, finding a decision-maker’s title, confirming hiring activity—and turning each check into a Claygent prompt or an AI column that reads context from other cells and writes back a structured answer. Because every run consumes credits, we tune prompts and add guardrail columns so spend tracks accuracy rather than guesswork.

What Clay’s AI actually does is narrower than a general AI platform: it browses the open web and your enrichment data to answer a specific question per row, or generates short, prompt-driven text such as an icebreaker line or a lead-scoring rationale. It does not train custom models, monitor for model drift, or manage a model registry—accuracy depends entirely on the quality of your prompt and the data already in the table.

Who this is for—growth, RevOps, and outbound sales teams building or enriching prospect lists who want AI-written personalization and AI-assisted research without hiring a data science team. It is a poor fit for teams looking for enterprise AI governance tooling or model lifecycle management.

Delivery approach pairs a working session on your ideal customer profile with hands-on column design: we build the first Claygent prompts and AI columns against a sample list, review outputs for accuracy, and only then scale the run across your full table.

Deliverables typically include a documented set of AI column prompts, a credit-usage estimate so you can budget runs, and a review checklist your team uses to spot-check AI output before it reaches outbound sequences.

As you scale, AI columns get chained—Claygent research feeds a scoring column, which feeds a personalization column, which feeds an export to your sequencer—all inside the same table, with each step’s credit cost visible before you run it.

Practical ways teams use Clay’s AI capabilities: the list below reflects real workflows, not hypotheticals.

• Claygent visits a company’s website and careers page to confirm whether they’re hiring for a specific role.
• AI columns draft personalized cold-email opening lines using a prospect’s recent public activity as context.
• Claygent checks a company’s public tech-stack signals to flag ICP fit before a rep spends time on it.
• AI columns classify inbound leads by industry or use case based on their website copy.
• Claygent looks up a company’s most recent funding round or leadership change from public sources.
• AI columns generate a one-line “why now” rationale for each account before it’s routed to a rep.
• Claygent verifies whether a scraped job title still matches the person’s current role.
• AI columns summarize a prospect’s recent press mentions into a two-sentence briefing for reps.
• Claygent flags companies that mention a competitor by name in public job listings or blog posts.
• AI columns score leads on a simple fit scale that other columns and exports key off of.

Clay’s AI layer is intentionally lightweight: fast to wire up, priced per row, and best treated as a research and drafting assistant rather than an autonomous decision system.

Intro Clay-powered AI engagements emphasize augmentation and decision support with clear governance.

• Churn risk scoring with human-in-the-loop validation through Clay.
• Dynamic pricing recommendations surfaced to operators via Clay dashboards.
• Fraud signal routing with explainability baked in by Clay.
• Maintenance prediction alerts integrated into operations using Clay.
• Content personalization scoring reviewed by editors under Clay governance.
• Energy optimization guidance constrained by policy enforced by Clay.
• Inventory demand signals tested in sandbox environments with Clay.
• Model drift monitoring and automated remediation plans managed by Clay.
• Experimentation harnesses where Clay controls feature flags and metrics.
• Multi-model ensemble insights delivered with provenance from Clay.

These scenarios demonstrate how Clay delivers practical AI outcomes with discipline.