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GTM Engineer · Outbound Systems

I build GTM systems that turn cold lists into qualified pipeline.

You're leaving revenue on the table because you don't have the data or the system to reach the right people at scale. I build that with AI.

See how I workGet in touch →

This is for you if your pipeline depends on referrals or one sales person hustle, if you've tried SDRs or agencies and nothing stuck, or if you want to own the system rather than rent someone else's access to it.

Joao, the independent GTM engineer behind AIento

2,000+ partners recruited at AnyDesk

€2.7M partner revenue tracked at Kubermatic

~20 VP & C-suite meetings a year at LiveEO

$380K ARR identified via PQL detection (Neo Bank)

The system

How I actually run this

Five steps. The same five every engagement. The tools don't change. The ICP, signals, and message do.

01

Map the ICP and signals

We figure out who to target and what buying or intent signals actually matter. Industry, role, tech stack, account relationships, hiring patterns.

Strategy
02

Build the enrichment layer in Clay

Pull and score leads, then attach the data points that make outreach relevant to the account, not just the inbox.

Clay
03

Sequence outreach in Lemlist, sourced via Apollo

Campaigns anchored to the ICP's real pain, with A/B variants on subject lines and openers.

LemlistApollo
04

Automate the machine in n8n

Enrichment, sequencing, and the CRM all wired together so leads move through the system without manual handoffs.

n8nHubSpot
05

Run and operate it through Claude Cowork

The actual operating layer. Each client gets a workspace with their ICP, history, and task log, so the system always picks up where it left off.

Claude Cowork

Inside the operating layer

This is the actual system I run client work through.

cowork · fluxpark workspace

Workspace

📁 ICP & Signals
📁 Campaigns
📁 Operator Mapping
📁 Pilot Notes

Tasks

• Refresh parking operator list
• QA Lemlist variant B
• Push warm leads to HubSpot

Campaign #08 · Houston parking operators

running

Cowork · summary

187 Houston-area operators sequenced this cycle. Subject variant B is +29% on opens. 3 pilot calls booked, 2 still waiting on a reply from SP+ and Premier Parking.

Joao · next

Re-enrich the no-reply cohort in Clay, then route Houston operators with EV charging projects into a dedicated pilot track.

ClayLemlistApolloHubSpotn8n

Selected work

Systems I've built and shipped.

Four engagements, four very different problems, same operator behind each one.

AnyDesk

Case study

Global channel programme, 80 to 2,000+ partners

Problem. Strong self-serve growth, but no indirect motion. Just 80 ad-hoc reseller agreements with no real framework behind them.

Built. Built the full programme from scratch: Ideal Partner Profile, a three-tier framework, sales playbook and battlecards, ZINFI portal, onboarding flow, and MDF structure. Then rolled it out across 80 countries.

HubSpotNetsuiteZINFILooker

Result. 2,000+ partners across 80 countries, €2.7M in partner revenue.

Neo Bank (mock dataset)

Case study

Product-usage churn alert and PQL detection

Problem. 92.6% of users sat on the free tier. There was no way to tell who was close to upgrading, who was quietly churning, or who was sharing accounts.

Built. A full PQL engine: an ETL pipeline feeding an ML classifier that scores upgrade probability per user, two BI dashboards, and reverse ETL pushing high-scoring users into HubSpot for targeted upgrade nudges.

Python MLETLLookerReverse ETLHubSpot

Result. 74% model accuracy, $380K in ARR identified, $50.7K in PQL pipeline surfaced in the UK segment.

Kubermatic

Case study

Partner GTM data pipeline

Problem. As the partner network scaled, activity, deal data, and enablement tracking lived in disconnected spreadsheets. QBRs took hours of manual pulling.

Built. An automated pipeline (HubSpot to Fivetran to BigQuery to Looker) with Census reverse ETL pushing enriched segments back into HubSpot.

HubSpotBigQueryFivetranCensusLookerZapier

Result. Dashboards for partner revenue, live partner pipeline visibility, zero manual QBR pulls, fully automated partner processes.

LiveEO

Case study

Account-Based Partner Marketing engine

Problem. LiveEO sells AI satellite monitoring to tier-1 utilities and rail operators. Their partners (AWS, SAP, Esri, Accenture) already had relationships with their dream accounts, but no process to activate them.

Built. A bi-weekly campaign engine combining account mapping against AWS ACE, partner-seller activation, and direct dream-account outreach.

LemlistHubSpotAWS ACESales Navigator

Result. Around 20 VP and C-suite meetings booked per year, running for over 12 months.

Each system was built and handed off for the specific business it serves. Nothing here is templated.

Joao

About

I'm Joao.

I've spent years building partner programmes, GTM data pipelines, and outbound systems for companies like AnyDesk, Kubermatic, and LiveEO. I now run AIento as an independent practice, building and operating these systems end to end through Claude Cowork.

I work best with B2B companies, roughly 10 to 200 people, that want a real outbound or partner system built and handed off.

You get the system, the playbook, and the operating layer. Then it keeps running.

Joao presenting a PQL data model and correlation heatmap to a live audience
Presenting the PQL detection model (correlation heatmap of user features vs. upgrade probability) to a live audience.

Contact

Let's talk about your system.

Tell me a bit about what you're trying to build. I read every message and reply personally.