Aarone Den Patayan
Automations that hold up when it counts — because I break them first.
Marketing & AI Specialist
I design and build marketing automation systems — lead routing, billing and dunning, multi-step nurture sequences — for founders and marketing teams who need something that works the first time a real customer hits it wrong. Every build gets the same treatment: deliberately broken with malformed data, timeouts, and edge cases before it ever reaches your business, so the failure modes get found on my time, not yours.
About
How I Work
I'm Aarone Den Patayan, a marketing & AI specialist. I build multi-prompt AI pipelines that turn a business's marketing strategy into automation-ready specs — then build the automation itself, on whichever platform fits the job: n8n, Make, Zapier, or GoHighLevel, wired into the CRMs, databases, and messaging tools a team already runs on, like HubSpot, Postgres, Airtable, and Slack. From there I operationalize those specs into playbooks, copy, and dashboards a real team can execute against. Most of the value gets lost at the handoff between strategy and build — so I treat documentation and testing as part of the deliverable, not an afterthought.
Patch-based iterative delivery
Builds ship in small, reviewable changes instead of one big handoff. Each patch is tested and confirmed working before the next one starts, so problems surface early and stay easy to trace.
Zero-knowledge-loss handoffs
Every session ends with a written handoff — decisions made, what's still open, what to check next — so context never has to be re-explained or rediscovered between sessions, platforms, or people.
Execution-ready outputs
Specs and playbooks are written for the person who has to actually build them: exact steps, named inputs, no guesswork. If a number isn't measured yet, it's marked unmeasured — never estimated.
Featured Case Study
Laneframe: Four Platforms, One Specification
[Summary placeholder — one lead-intake automation specification, built independently on four platforms for a composite client, instrumented identically and measured honestly.]
[Status placeholder — actively building: n8n and Make live, Zapier and GoHighLevel in progress]
Active buildMethodology note: [Methodology note placeholder — every number on this page is either measured during a real build or a formula with its inputs named; unmeasured items are marked, never estimated]
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Want a build stress-tested like this before it reaches your customers?
Let's talkMore Work
Additional Case Studies
A closer look at two recent builds, with more on the way.
AI Customer-Service Agent, With a Human in the Loop
A knowledge-grounded AI agent that answers customer questions accurately — and knows when to say 'I don't know' and hand off to a person. Every AI-drafted reply passes human review before it reaches a real customer, tested against a 12-question boundary set that includes near-misses designed to tempt a confident wrong answer.
- AI Agents
- Human-in-the-Loop
- Write-up coming
Multi-Prompt Strategy Document Pipeline
A five-prompt generation pipeline that assembles a full GoHighLevel strategy document — tagging conventions applied systematically rather than re-generated per client, output styled in each client's own brand colors, not mine.
- GoHighLevel
- AI Content Pipeline
- Write-up coming
More Case Studies Coming Soon
This spot's reserved for the next build. Check back soon, or get in touch if you'd like to be the one who fills it.
View case study →Proof
Measured, Not Just Claimed
No client testimonials yet — here's what's actually been verified instead.
12/12
Adversarial probes passed
The most recent AI agent build — a knowledge-grounded customer-service agent — tested against answerable, undocumented, and near-miss questions designed to tempt a confident wrong answer.
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Platforms benchmarked independently
The flagship automation build is being built separately on n8n, Make, Zapier, and GoHighLevel — same specification, measured honestly, nothing cherry-picked.
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Invented numbers
Every stat on this site is either measured during a real build or a named formula. Unmeasured is marked unmeasured — never estimated.
Stack
Skills & Tools
The platforms, integrations, and testing practices behind every build below. Stack keeps moving — just like the pipelines it builds.
Automation Platforms
AI & Agents
Integration & Data
Not sure which platform fits your process? Let's figure it out together.
Let's talkProcess
How I Test Before I Trust
The delivery process, from scoping to launch — built around finding what breaks before a customer does.
- 1
Map the failure surface
Before writing a single node, I list everything that could break it: malformed data, unsupported requests, edge cases a happy-path demo would never hit.
- 2
Build against probes, not demos
Every build gets a written set of adversarial test questions — things it should answer, things it shouldn't, and near-misses designed to tempt a confident wrong answer — before it's called done.
- 3
Route AI judgment through a human gate
Anything an AI system drafts for a real customer passes through approval before it goes out. Automation should remove the busywork, not the judgment call.
- 4
Test for consistency, not just correctness
AI tools don't behave identically run to run. I re-run the same prompt and check where the answer drifts, not just whether it passed once.
- 5
Document the limits, not just the wins
Every build ships with an honest account of what it can and can't do yet — no polished demo standing in for a guarantee.
Contact
Let's Talk
Have a build that needs stress-testing, or a marketing system that's outgrown manual work? Tell me what you're dealing with — I'll get back to you within a couple of business days.