The Practice
Go-to-market, engineered.
GTM Engineering is the practice of building automated revenue systems with data, AI, and workflow automation. Not filing tickets. Not blasting volume. Building the machine that finds the right account, reads the right signal, and acts, before a human touches it.
Reply rate
signal-based outbound
The stance
A GTM engineer is a builder, not a relabeled ops analyst. Half commercial thinker, half systems builder. The output is a working revenue engine, not a report.
By the numbers
Tools run most of the engine
Email coverage from a waterfall
Spam rate you must stay under
The curriculum
What you'll learn
The enrichment waterfall
Chain data providers in sequence so when one misses, the next fills the gap. Maximize coverage, pay on verified hits, and keep the CRM trustworthy at scale.
Explore → 02Signal-based outbound
Detect real buying signals (funding, hiring, job changes, tech installs, usage spikes) and fire outreach only when intent appears, instead of static lists and volume.
Explore → 03AI & agentic workflows
Put LLMs to work on research, ICP scoring, reply classification, and personalization at scale, with human-in-the-loop checkpoints where judgment still matters.
Explore → 04APIs, webhooks & orchestration
Connect tools that do not natively talk. Build self-healing, API-driven workflows across CRM, warehouse, enrichment, and sending in Clay, n8n, and Make.
Explore → 05The data foundation
Dedupe, model, and route before you automate a single play. Lead-to-account matching, the data points that predict a purchase, and a warehouse that feeds the machine.
Explore → 06Deliverability & sending
Domain warmup, inbox rotation, and sending infrastructure so programmatic outbound actually lands instead of getting flagged.
Explore →Recent essays
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Building your first enrichment waterfall
When to chain Apollo, ZoomInfo, and Cognism so that a miss from one provider becomes a hit from the next, and you pay mostly for verified data.
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Signal-based outbound: which signals are worth automating
Funding, hiring, job changes, tech installs, usage spikes. A field guide to the buying signals worth wiring up and the ones that just burn credits.
The lexicon
- Enrichment waterfall
- Sequential fallback across data providers to maximize match coverage, paying mainly on verified hits.
- Signal-based selling
- Outreach triggered by real intent signals rather than static lists or raw volume.
- Programmatic outbound
- Outbound run as an automated system: data, triggers, AI personalization, and sending, not manual rep effort.
- Data orchestration
- Coordinating data flow across sources, enrichment, CRM, and activation tools as one pipeline.
- Human-in-the-loop
- Deliberately placing human judgment at the points automation should not own.
- Data activation
- Pushing modeled, enriched data back into the tools reps and campaigns actually run on.
The newsletter
One dispatch. Every week.
Field notes on GTM Engineering: the plays, the tools, the systems that work. Written for practitioners, no filler.
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Connect a provider in src/config.ts