Hiring lens · AI GTM · 10 yrs marketing, now shipping the systems

Nine AI GTM systems live, all built solo

CreativeOS, FinGuard, AdGuard, MarketingOps, Career Capybara, Hyper Creator, TikTok Miner, TechSpy and AdRadar. Every one has a real public URL, and every one runs LLM pipelines with a human in the loop.

9 systemslive, solo-built, public URLs
200+ workflowshandoff ~12h → 1–2 min
10 yearsof marketing before the code
Marketing half, engineering half

When the stack can't do it, I open an editor

Most GTM teams hit a wall in the stack and file a ticket. I stopped filing tickets. After ten years running marketing for B2B fintech and SaaS, I started shipping the software myself.

At PingPong I owned marketing for the cross-border payments line end to end, the line that went from $2M to $76M in year one and past $250M+ by year two. Sales closed the deals and finance ran the payment rails; every campaign, channel and lifecycle touch that fed their pipeline was mine. I personally cut LinkedIn CPL from $300+ to ~$160 and Google registration cost from $90+ to ~$35, and I governed the 8-tool GTM stack that carried it all: Salesforce, HubSpot, Webflow, 6sense, Zapier, ZoomInfo, Salesloft, GA4 and Google Tag Manager. Before that, as VP of Marketing at ToLocal, I ran a $10M annual media budget at 80%+ ROI leading a team of 7.

I built the 200+ workflow layer that collapsed lead handoff from ~12 hours to 1–2 minutes. That was my build, wired into a stack I governed, feeding a sales team that worked the leads. I wrote the LLM pipeline that turned a messy monthly report from one to two hours of manual work into an under-five-minute run producing eight different reports from a single cleaned dataset. My LinkedIn title line reads “GTM Systems Builder” because that is the job I actually do.

The full record

Three kinds of evidence, and what each one settles

They answer different doubts, so they are grouped by kind rather than by date. The systems are the artifact, each on a URL you can open right now. The case studies show the same method applied inside a company, with other people in the loop. The writing shows the reasoning before the result was known.

How a build happens

From a marketing pain
to a public URL

01 · Start from a pain I've paid for

Every system began as a GTM problem I lived with as a marketer: ad copy dying in policy review, leads going cold over a ~12-hour handoff, a monthly report eating an afternoon. I write the problem down with a number attached before I write any code.

No number on the pain, no build

02 · Ship the smallest system that closes the gap

CreativeOS is the pattern. I built it to write platform-ready ad copy for Google, Meta, LinkedIn and Bing with formats and character limits enforced, checking every line against platform ad policy and custom compliance rules as it writes. Next.js, an LLM API, Vercel. No committee, no roadmap review.

Ship the narrow thing, then stop adding to it

03 · Wire it into the stack

A tool that lives outside the stack is a demo. At PingPong I wired the winners into 200+ workflows across an 8-tool stack I governed myself, which is how I took lead handoff from ~12 hours to 1–2 minutes. Software that touches revenue has to sit inside the plumbing, so I build the plumbing too.

A tool outside the stack is a demo

04 · Keep a human in the loop

All nine of my systems ship with human-in-loop review, because GTM software touches money and brand. The model drafts, extracts or flags; a person approves. I measured what happens without that gate as a marketer, so I refuse to ship without it as an engineer.

The model drafts; a person stays accountable
Proof

Does “marketer who codes” mean scripts?

TikTok Miner · Full ETL + RAG

One TikTok URL in, a queryable intelligence system out

No. TikTok Miner is a full ETL and RAG system: paste a TikTok URL and it transcribes the video, extracts frame-level metadata, embeds everything into a Pinecone vector store, and serves RAG chat plus engagement analytics on top. React 19 and TypeScript on the front, multiple AI models behind it, built solo.

The messy monthly report I automated went from one to two hours of hand-assembly to under five minutes, with eight different reports generated from a single cleaned dataset. Different size of problem, same posture: I measured the pain, built the pipeline, shipped it to a URL anyone can visit.

9 systems live · full ETL + RAG · report time 1–2h → under 5 min
TikTok Miner · one URL in
Transcribeaudio → text
Extractframe-level metadata
EmbedPinecone vector store
ServeRAG chat · analytics
React 19 + TypeScriptmulti-model AI
Daniel is a fantastic member of the team with his ability to blend very deep technical knowledge with great interpersonal skills. He is able to quickly understand business requirements and then present a solution in a motivational way in terms appropriate for any level of the audience.
Emma Yu Emma YuSenior Product Manager, TikTok
TikTok

Hiring for AI GTM?

Hand me the GTM problem. I will ship the system that fixes it and give you the URL.