The tools. How they connect.
The GTM engineering stack I use to build automated revenue systems. Organised by the three layers that make any system work: data foundation, intelligence, and activation.
Updated as the stack evolves. Last reviewed July 2026.
Waterfall enrichment across 100+ providers. The core of every data build, from CRM hygiene to buying committee mapping. If a record needs enriching, Clay runs first.
clay.comPrimary CRM for most builds. Sequences, lifecycle tracking, deal pipelines, and the destination for enriched records. Also the trigger source for most automation workflows.
hubspot.comEnterprise-grade CRM for larger orgs. Used for complex territory models, opportunity management, and as the system of record when HubSpot isn't in play.
salesforce.comUsed inside Clay and n8n for AI-powered account research, first-message drafting, lead scoring rationale, and extracting structured data from unstructured sources at scale.
anthropic.comContent consumption and intent signals across the B2B media network. Used to triangulate account buying stage and validate intent before activation.
madisonlogic.comServes ads to specific named individuals (not just accounts). Used in ABM plays to warm up buying committee members before outbound sequences hit them.
influ2.comThe connective tissue of the stack. Handles event triggers, conditional routing, multi-system writes, and scheduled jobs. n8n orchestrates; Clay enriches. Keeping these concerns separate is the key architectural decision.
n8n.ioBuying committee research, job change signals, and list building. Feeds into Clay for enrichment and n8n for trigger-based plays when contacts at target accounts change roles.
linkedin.com/salesWeekly query loops across category keywords, competitor comparisons, and ICP-framed prompts. Tracks which queries return brand citations, which return competitors, and how brand description accuracy changes over time.
perplexity.aiFAQ schema, HowTo, Product, and Organisation markup on high-intent pages. The gap between what a human reads and what an AI crawler retrieves is usually larger than teams expect. Structured data closes it.
schema.orgWorkflows that adapt a single high-authority asset for multiple discovery surfaces: web page, AI-readable summary, LinkedIn post, community answer. Keeps messaging consistent without manual republishing.
n8n.ioB2B buyers validate on social before converting. Tracking community discussions, ensuring accurate brand representation in threads, and publishing content that serves the specific intent of each platform's search behaviour.
linkedin.com