Eric Fleshman / AI-native GTM engineer / NYC

I don't manage,
I ship.

I build closed-loop revenue systems with AI agents at the core, the way engineers ship software.

Speaking: Vibranium AI outreach panel · Aug 27 · NYC

01 The loop, run on your company

01Company nameone input
02Public researchPerplexity
03GTM hypothesismy rules, any model

It runs on the company, not you. No person lookup, enrichment, or visitor record.

replay
One real run from July 2026, replayed exactly as it happened. Yours runs live below.

Perplexity researches the public web. A model writes the systems hypothesis under my rules, and the provider is swappable by design. No cache, contact enrichment, visitor identification, outreach, or input storage by me. Both calls are key-locked and capped daily. If either fails, you get my email.

The $50M inbound engine Kizen · first GTM engineering hire

$50M+pipeline processed
600+changes shipped in 90 days
$10.9Mburied pipeline recovered
web + events + Apolloraw signal arrives
Claude agenta domain becomes an account
ZoomInfo enrichmentcontacts + firmographics attached
right rep, clean contactreps call instead of cleaning

underneath: a 4-object CRM data model and an attribution chain that propagates source truth

Read the full build

The problem. Inbound came from three places (web forms, event lists, Apollo activity) and reps spent their day cleaning contact data instead of calling people. No system turned a raw inbound signal into a rep-ready, enriched contact automatically.

What I built. Inbound automation that turns a raw signal into a rep-ready contact with no human touch:

  • A Claude agent resolves company identity from the email domain
  • ZoomInfo enrichment attaches contacts and firmographics automatically
  • Routing assigns the right rep by territory and quota fill
  • Web inbound, 400+ event leads (a $38M+ TAM), and Apollo activity all flow through the same pipe

Underneath it: a CRM data model I designed from scratch across 4 core objects, and 600+ documented changes shipped in 90 days with zero onboarding.

The failure that taught me the most. Before the attribution chain existed, $10.91M in marketing-sourced pipeline sat misattributed in the CRM, invisible to the team that earned it. The early system assumed source data would arrive clean. It never did. The fix was not cleaning records by hand. It was building inheritance: a Contact to Lead to Account to Opp attribution chain across 4 production automations with a standardized 8-value taxonomy, so the truth propagates instead of being re-entered.

Also in this system: a 4-automation bidirectional Apollo integration, 6 stage gates with a Slack bot that DMs reps their exact missing fields, and the attribution chain above.

The closed outbound loop AlphaForge · Clay's GTM engineering program

631contractors in the qualified TAM
24/25emails delivered
1real buyer reply, caught live
qualified TAM631 real buyers, dealers + media rejected
fit gate + scoreonly qualified accounts advance
n8n send engine25 approved sends
Gmail24 delivered
real buyera real reply comes back
reply watchercatches "RE:" the hard way
back into Clayloop closed

"the fullest loop I've seen" (program coach)

Read the full build

The problem. A raw construction TAM is not a market. You have to separate real equipment buyers from dealers, media, and design-only firms, find a real commercial moment, and then actually send something a real company's reputation can stand behind.

What I built. A gate-first outbound system where only real buyers make it through:

  • A qualified TAM of 631 US heavy-civil contractors; dealers, design-only firms, and media rejected up front
  • A Fit Score built on disqualification logic and public award evidence
  • A reusable ICP Fit Gate that takes a plain-English ICP as input
  • An n8n daily permit watcher with stable dedupe
  • The send path: Clay list to n8n send engine to Gmail, and the reply back through a watcher into Clay

The failure that taught me the most. The first real reply almost slipped through. My watcher filtered case-sensitively for "Re:" and Gmail delivered "RE:", so the loop dropped it. I made the filter case-insensitive, republished, and the next poll caught it automatically. A bug found in production, fixed live. That is the difference between a loop that could theoretically work and one that actually closed. The reply came from an equipment leader at a top ENR heavy-civil contractor, who walked through exactly how his team decides rent versus own.

The ethics moment. Every draft had a value line offering an "anonymized 600-contractor study" that did not exist. I cut it before sending and replaced it with something true. "Offer value" is not a line you add to an email; it has to be true before you hit send.

Live public-award signal feeding the intake ICP fit gate verdicts on incoming events

04 More builds

EatTailor idea to live app, in one weekend

An NYU class project taken from idea to a working nutrition app, then rebuilt production-grade by directing AI agents. Including the three failures review caught that the agents had reported as done.

Read the build

Agent Anatomy teardown of a production coding agent

A source-grounded teardown of a production terminal coding agent: the harness, the tools, the loop, and why the model is the smallest part of the system.

Read the teardown

05 How I build

Bike, not Ferrari

I get the motion working end to end first on the simplest stack that runs, then improve it every week. A shipped bike beats a beautiful build that never left the garage.

Close the loop

A system is not done when the boxes connect. It is done when the failure paths are tested and a real reply comes back. I only trust a loop I have watched close.

Guardrails are the build

Rate limits, blocklists, fail-loud errors, and a human approval gate are not bolted on. They are the difference between a demo and something you can point at a real customer.

Those are the habits. The arguments underneath them, on picking the constraint before the build, and a structural test for AI-native claims, are written out in full: read the thesis.

06 The path here

8+ years on B2B revenue teams, seed to Series D. I started as employee #6 at a security startup, spent years running ABM and demand gen, and became a founding GTM engineer. Marketing taught me what pipeline is worth. Engineering taught me to build the systems that create it.

2026
Kizen Founding GTM Engineerfirst GTM engineering hire; built the $50M inbound engine above
2024
Convene Regional Marketing Manager, NYCowned marketing's share of $100M+ annual pipeline targets
2021
Augury, Podium, VTS, ComplyAdvantage ABM + demand gen$15M ARR influenced at Augury; launched Podium's AI chatbot to $1.5M in pipeline
2017
strongDM Employee #6, founding marketing hirehelped scale $200K to $5M+ ARR; acquired for ~$275M

If you are standing up your first GTM engineering function, or making an existing revenue motion AI-native, I have done both. We should talk.

Education / Certification

BS Marketing, Fordham · NYU graduate coursework · Clay AlphaForge GTM Engineering Cohort 2