FastSwaps

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Co-Founder

Fastlaunchr

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The Data Swiss

We build robust data & ML platforms in the cloud so you can have any kind of insight within days, not months.

Founder & Principal Data Solutions Architect

Telia

Working on releasing ML platform functionalities and at the same time provide support and create documentation so application teams can leverage platform for doing their data analysis processes. Some of the projects:

- Extending, supporting and developing new functionalities of our ML platforms. Working with CFn, Service Catalog, SageMaker mostly, among many other services.
- Architect and define usage of Sagemaker Feature Store for tenants.
- Work related to operations in ML environments.

Stack: 

- Github actions as CI/CD + OIDC.
- HashiCorp Vault for credential management.
- Python and Bash.
- AWS.
- Airflow. For orchestrating and scheduling any workflow at scale.
- CloudFormation (CFn).

MLOps Engineer

Working on releasing Data Foundation and ML platform functionalities and at the same time provide support and create documentation so application teams can leverage platform for doing their data analysis processes. Some of the projects:

- Enabling serverless ingestion from on-prem to different business layers (s3 and redshift) using SDLF from AWS.
- Developing and implementing an abstract and centralized solution to define datasets that are ingested/residing within our cloud platform and handle permissions through Lake Formation.
- Implementing solutions according to the General Data Protection Regulation (GDPR): RTE tasks, purposed-based access, etc.
- Leading the organization adoption of Hashicorp Vault for our GitHub repositories to get AWS secrets and GitHub tokens. As part of this, we are decommissioning our old solution and I developed a framework in Python that acts as a IaC for Hashicorp vault components (it creates engines, roles, tokens, policies, etc).

Stack: 

- Github actions as CI/CD.
- HashiCorp Vault for credential management.
- Python.
- AWS. Wide variety of services: S3, Lake Formation, Glue, Lambda, Step Functions, SQS, SNS, KMS, Athena, Cloudtrail, IAM, SSM, Redshift, EventBridge, DynamoDB, etc. Worth mentioning we are using for one of our projects, Serverless Datalake Framework (SDLF) for ingesting data from on-premises up to the business layers.
- Airflow. For orchestrating and scheduling any workflow at scale.
- Jenkins. For deploying to our on-premise ecosystem.
- Terraform. For keeping all our IaC and managing infrastructure state and changelog.
- CloudFormation (CFn).

Senior Data Platform Engineer

Factmata

Acquired by PR giant Cision.

1) In charge of running infrastructure cost optimizations. Reduced over 70% in 5 months.
2)  Guide the team by setting technical directions, architectural governance, design patterns, and practices.
3) Architect and build highly scalable solutions for diversified and complex data flows.
4) Active role in interviewing candidates for technical positions (Back-end/Front-end and Data Engineer).
5) Create 'shardflow ecosystem'. This solution allows our RDS databases (or shardflows) to scale out/in and up/down and provides an API for creating, migrating, resetting and deleting shardflows.
6) Work with Kubeflow in k8s for running our ML pipelines and generate clusters and insights for our SaaS platform.
7) Implement a serveless ELT from different data providers (twitter, reddit API, etc) to ingest these data into our different data lake layers using Lambdas, Event Bridge, S3, Glue and Athena.

CTO & Data Solutions Architect

Bluetab Solutions

Projects I have developed [D] and/or contributed [C] within /bluetab:

1) [D] Migration to AWS of an algorithm developed by UC3M that finds association between power plants and meter boxes within the Spanish territory. Big Data project using several Amazon services such as EMR, Batch, Lambda, SQS, Cloud Formation, Athena, etc. I am also working with deployment tools such as Jenkins and Terraform as IaC.

2) [C] Automation and enhancement of a current solution that ingests data from databases in premises to our DW on AWS Cloud. Files are ingested in S3 and then, ETLs orchestrated by Airflow are triggered. In this solution we are using a wide variety of services, for example, EMR for applying transformations and/or consolidate data, DMS for retrieving the data from the on-premises databases, DynamoDB for storing metadata about the states of the different processes, etc.

3) [D] Bluemetric is a highly flexible solution that recurrently extracts relevant metrics from different services of AWS by using both CloudWatch and CloudTrail. Such metrics are materialized wherever specified (RDS, S3) for further analysis, for example with PowerBI, QuickSight.

4) [D] Bluehaul is an event-driven architecture almost-serverless solution that makes files that are ingested within the cloud (namely, S3) available for analysis and computation. It is mainly focused on the movement and optimal structuration for efficient retrieval and manipulation. Services used: S3, CloudWatch, for triggering events and monitoring, Lambda, DynamoDB, SNS, ECR and AWS Batch.

5) [D] Architecting and developing a scalable, resilient and distributed platform in AWS to programmatically schedule and monitor workflows (using Apache Airflow) for ETL processes within Datalakes across multiple projects.

6) [C] Providing support on an ETL project in Redshift orchestrated by Apache Airflow.

AWS Data Engineer

Big Data dept at University Institute of Intelligent Systems and Numeric Applications in Engineering

Collaborating on OSMOSE, a European H2020 project. 

1) Developed Pyself framework. A novel simulation lab factory for Siemens PSS/E. It was used for analysing and optimising the impact of a new device within the electrical system in the Canary Islands.
2) Submitted a paper titled "OPTIMISING POWER SYSTEMS BY AUTOMATING LARGE SETS OF SIMULATIONS"  to The European Multidisciplinary Society for Modelling and Simulation Technology (EUROSIS) as a result of Pyself.

Research support technician

Self-Employed

Teacher of Programming

Self employed

Utterly plunged into the wondrous world of teaching Mathematics and Physics to 12th-grade students (Bachillerato and Uni)

Teacher of Mathematics and Physics

EIFER - European Institute for Energy Research

1) Research and develop a web-based application (SAVVY) for visualizing different kind of graph-based datasets within the company.
2) Extensive research on the suitability of using SPARQL and Graph Databases such as Neo4j for the aforementioned objective.

Technical Support Engineer (Intern)

Data and ML Engineering | Founder @TheDataSwiss

Hace 1 año que salí de prisión.

Una prisión en la que podía salir a la calle.

En la que tenía los sábados, domingos y generalmente todas las tardes para mí.

En las que dormía en mi casa.

En las que tenía 23 días por año para irme a Indonesia si quisiera.

Pero de resto, jodido. Tenía si o sí que estar en prisión.

Por suerte, me escapé.

Y ahora vivo en otra prisión construida por mi en la que no salgo casi nunca, jajajajjaa.

Mierda.

He ido a peor. ¿te has dao cuenta tu también no?

Tomás Denis Reyes Sánchez

Ayuda a hombres a tener relaciones con mujeres sin depender de Tinder.



Llegan los primeros resultados de los alumnos dentro de la formación Apollo.

En este caso Guille hace 2 meses no tenía newsletter.

0 suscriptores.

No tenía landing.

No tenía página de ventas.

Y ahora hace 2 semanas empezó con Meta Ads.

Desde entonces crece gratis. 

Nada mal para empezar jajaja.



Todo esto lo enseño dentro de Apollo.

Una formación de 3 meses con acompañamiento en la que acabas con newsletter que te trae clientes y negocio.

El 9 de enero de 2026 empezaremos la III edición.

En caso de interés, suscríbete a mi newsletter.

Enhorabuena Guille! 👏

Negocios que ganan dinero enviando correos

graphical user interface, text, application, email

Scale revenue with AI & tech

I decided to skip the traditional corporate path & start building in the AI x Sales space at 27. 
Here's the journey so far:

Age 18-21: Business school in Brussels, realized I needed practical experience
Age 22: Coding bootcamp + lucky Bitcoin exit gave me 50K to fund a world trip
Age 23: 12-country world tour learning new skills every month in a new country.
Age 24-26: Growth hacker roles at German startups, learned systems but felt unfulfilled 
Age 27: Co-founded ColdIQ with Mich
Age 28: Hit our first major milestones learning everything the hard way 
Age 29: Scaled to $6M ARR and became Clay's #1 global partner. Running a business that does $477K/month with 30+ team members across 4 continents.

It's been wild. Today through ColdIQ, we help B2B companies build AI-powered sales systems that work. 

We've run outbound for 300+ organizations and generated millions in pipeline.

The best part? We built this entirely remote with people I genuinely enjoy working with. 

Turns out you can have rapid growth AND life quality - you just need the right systems.

If you want to build your own AI sales agency, I teach everything in our ColdIQ Agency Accelerator, the exact playbook we used to hit $6M ARR in 2 years. 

From Clay workflows to hiring A-players to scaling without burning out.

If you're already doing $1M+ ARR and want help scaling your existing business, let's talk about our done-for-you services.

https://coldiq.com/

If you're not there yet, I share everything for free on LinkedIn and in our 7-day email course (in the featured section).

The AI x Sales opportunity is the biggest wealth creation event of our generation. The market is so massive that there's room for everyone to win.

Ready to start your agency? Join 130+ founders in our accelerator.

https://coldiq.com/accelerator-program

Alex Vacca 🧠🛠️

We analyzed 100K+ LinkedIn DMs to figure out what works.

Here's what we found:

1️⃣ Personalization beats volume every time

Campaigns with copy tailored to the ICP got up to 54.7% more replies.

We stopped blasting generic messages and started personalizing based on specific pain points.

We're using Clay for data enrichment and personalization, LinkedIn Sales Navigator for precise targeting.

Next month target: Scale personalization without losing quality. Why? Take some time to personalize your messages; it'll go a long way.

2️⃣ Short messages crush long ones

Messages under 150 characters had 22% more replies on average. We track which message length converts from each persona.

We aim to get our point across in as few words as possible - nobody's reading novels.

Next month plan: Test extra short hooks under 100 characters.

3️⃣ Contextual outreach destroys generic

Messages referencing recent activity (LinkedIn posts, role changes) saw +18% replies. We find relevant reasons to reach out every time.

Tool stack breakdown:

Clay for finding recent activity, role changes, company updates

LinkedIn Sales Navigator for activity monitoring

Expandi for sequencing

Zapier for workflow automation

Next month: Keep refining context triggers - when something works, you optimize it.

4️⃣ Warm approach sequences that build relationships

Connection requests sent after profile visits, post likes, or follows saw +30.2% acceptance rates. We make ourselves seen before reaching out.

Sequences with 3+ steps performed 42% better than single-message flows.

Different relationship temperatures get different approaches.

5️⃣ Multi-channel orchestration across every platform

Adding email follow-up after no LinkedIn reply lifted reply rates by 13.8%. We don't rely on LinkedIn only.

This is where most agencies lose pipeline - we track everyone who doesn't respond and re-engage via other channels.

6️⃣ Conversational copy beats direct pitches

The messages that actually work:

"Noticed you're hiring [role] - what's working for you?" (+18.2% replies)

"Saw your post on [topic], curious what are you testing right now?" (+19.3% replies)

"Saw you joined [company] - how's the first month going so far?" (+21.5% replies)

"We analyzed [findings] - worth sharing what others in your space are doing?" (+27.1% replies)

The results:

54.7% higher reply rates with personalized outreach

40%+ acceptance rates consistently

18%+ reply rates across campaigns

8%+ positive reply rates

The system works because it's not just LinkedIn messaging - it's relationship building at scale with contextual touchpoints where your prospects actually engage.

Want the full outbound playbook that's generated 54.7% higher reply rates? 👉 Comment "OUTBOUND" for our 7-day email series on the exact sequences we use.

Co-Founder @ ColdIQ ($6M ARR) | Helped 300+ companies scale revenue with AI & Tech | #1 AI Sales Agency

lemlist hit $37M ARR.

Here’s the outbound engine behind it (and why it works so well)↓

Most people think lemlist grew by sending better cold emails.

They didn’t.

They built a system that turns content → intent → enriched data → personalised outreach at scale.

Here’s the exact workflow:

1️⃣ Content that attracts their ICP (not vanity content)

An influencer or internal creator publishes on LinkedIn - but not just any content.

It’s written to attract one specific persona with a problem lemlist solves.

Every comment, like, and share = a signal.
A signal that someone cares about the topic.

They use:

- Jungler to scrape all post interactions
- LinkedIn for content publishing

Because people are already familiar with their brand, reply rates are way higher than pure cold outreach.

2️⃣ Data routing & enrichment

Not every engager is worth contacting.
So they have a system to score, clean, and enrich.

They push all extracted contacts through:

- n8n for data routing
- Clay as the data hub (enrichment, scoring, workflow automation - all in one place)

Scoring includes:

→ Job title relevance
→ Engagement type
→ ICP match
→ Buying power indicators
→ Existing customer check
→ Tech stack + initiatives + recent activity

Here’s the thing nobody talks about:

If the enrichment is bad, the personalization fails.
If the personalization fails, the whole system collapses.

This is where lemlist invested the most time - and where most teams cut corners.

3️⃣ AI personalization & multi-channel outreach

Personalization isn’t “{first_name}, loved your post!”

They use:

lemlist’s AI variables → genuine first-lines for every prospect
Multi-channel sequences → email + LinkedIn for maximum reach

A typical opener:

“Hey {{first_name}}, saw you engaged with our post on {topic}. Looks like you’re focusing on {initiative} — happy to share what’s worked for us.”

Warm context + real personalization + multi-channel = consistent replies.

Teams spend hours “perfecting” cold emails.
lemlist spends hours perfecting context - and lets AI scale the rest.

4️⃣ Meeting booked

Warm, relevant outreach performs.

This is one of the most efficient ways to:

→ Warm up leads
→ Segment the highest quality ones
→ Gently ping them with context at exactly the right time

This is one of lemlist's highest performing outbound plays. It's also a play we love to run at ColdIQ.

They've spent months testing and optimizing this workflow to run on autopilot.

The compound effect takes time, but when it kicks in, it's massive.

If you want to implement something like this - we'll literally run this entire workflow for you, along with other high-converting GTM plays.

Drop me a DM👇

We just crossed $6,000,000 ARR.

Here's the tech stack that made it happen ↓

1️⃣ Content & Inbound

We generate over 100 warm leads per month via LinkedIn.

These come from my account (45,600+ followers) and the rest of the ColdIQ team (230,000+ followers).

I use:

- Scripe for post ideas + analytics
- Notion as my content calendar to keep me organised

We generated ~50M impressions on X last month, which is honestly wild. And for this we use:

- Typefully to visualize & draft threads: makes the whole process way smoother

I am getting some momentum behind my YouTube, so at the moment I use:

- Two Sony FX30 camera for filming
- Tella for screen & audio recording (super clean output)
- vidIQ to gain inspiration from successful videos, basically reverse engineering what works

We drove 102,000 visitors to our website in the last 90 days. 35,000+ come from SEO alone. We use:

- Ahrefs for keyword research (can't live without it)
- Webflow's CMS to host/publish our articles
- Surfer to optimize our copywriting for search engines

We have 7,000+ subscribers to our newsletter, and growing. We use:

- beehiiv to send our welcome sequence & weekly emails

2️⃣ Outbound Engine

- We identify website visitors with Instantly.ai & Vector 👻
- Based on their firmographics + page visit, we:

→ Email them via Instantly.ai

→ or connect + send voice notes via lemlist

- more personal touch

- For data: Prospeo.io, LeadMagic, Clay, FullEnrich
- For AI research & personalization: ChatGPT, Claude
- For deliverability: a mix of Gmail + Outlook inboxes

3️⃣ Ops & Project Management

- Everything runs in Airtable
- We automate with n8n, Make or Relevance AI, depending on what we're trying to do
- Big-picture process flows? We map those in @Puzzle
- Internal comms → Slack

4️⃣ Sales & Closing

- Calls get booked through Default
- We use Attention to record & summarize meetings
- Proposals: Qwilr (clean, professional, gets signed)
- Banking: Mercury
- Contracts: PandaDoc
- Payments: Stripe

We've spent months testing, tweaking, and optimizing this entire stack to work together seamlessly.

Tech moves fast. Really fast.

New tools drop every week, and what worked 6 months ago might not be the best option today.

I break down the complete stack + how we actually use these tools on my YouTube.

Subscribe here → https://lnkd.in/dKsZpY2D

What tools are you running? Drop them below 👇

Behind Lemlist’s $30M ARR growth engine.

... and 20,000 paying users.

Michel Lieben 🧠 sat down with Charles Tenot, CEO of Lemlist, to go behind the scenes of their growth strategy.

Let me build this out properly for you. Here's the full breakdown:

1️⃣ TRAFFIC GENERATION

lemlist gets over 200,000 website visitors every month. But they don't rely solely on one channel.

Here are the channels they use to drive that traffic:

a. SEO: 15–20% of signups come from search
→ They dominate keywords like "cold email templates" and "email outreach tools"
→ 300+ blog posts targeting bottom-funnel search intent

b. YouTube: 14.2K subscribers, and 393 videos
→ Educational content on cold email, outbound sales
→ Tutorial videos showing actual lemlist workflows

c. LinkedIn: Combined reach of 250K+ followers
→ Team members consistently post about outbound strategies
→ Share customer wins and case studies weekly

d. Paid Ads: €20K/month to Meta, Google, and LinkedIn
→ Google: Target high-intent keywords like "mailchimp alternatives"
→ Meta: Retargeting website visitors with social proof content
→ LinkedIn: ABM campaigns targeting sales leaders at 50-500 employee
companies

e. Webinars: Expert-led sessions on outbound and sales
→ Weekly live sessions with 200-500 attendees
→ Guest speakers from successful B2B companies
→ Replay content distributed across all channels

f. Affiliates: Partners and customers earn a % for referrals
→ Agency partner program with 30% recurring commissions
→ Strategic partnerships with complementary SaaS tools
→ Customer referral program with account credits

2️⃣ LEAD CAPTURE

lemlist turns traffic into signups, fast.

They convert 7–8% of 200K+ monthly visitors (that's 14,000-16,000 signups monthly).

Here's what increases those conversion rates:

Free trial strategy:

→ No credit card required
→ Full access to the platform
→ 14-day trial with onboarding sequence
→ In-app tutorials and success metrics tracking

3️⃣ ACTIVATION & ONBOARDING

This is where most SaaS companies lose people, but lemlist nailed it:

→ Day 1: Welcome email + calendar booking for optional onboarding call
→ Day 3: "Send your first campaign" tutorial with templates
→ Day 7: Case study of similar company + advanced features demo
→ Day 10: Personal video from Guillaume + upgrade incentive
→ Day 14: Trial ending soon + success metrics from their usage

4️⃣ CONVERSION TO PAID

Here's where the "reverse trial" magic happens:

→ Soft paywall: Free users hit email limits naturally through usage
→ Value demonstration: Show ROI metrics before asking for payment
→ Multiple upgrade paths: Self-serve pricing page OR book sales call
→ Usage-based triggers: "You've sent 95/100 free emails this month"

The numbers:

→ 200K monthly visitors
→ 95%+ monthly retention rate
→ €150 average monthly deal size
→ 7-8% conversion to trial (14K-16K trials/month)
→ 15-20% trial-to-paid conversion (2.1K-3.2K new customers/month)

What do you think lemlist should add to grow even faster?

Content helped ColdIQ hit $6M ARR.

And this workflow turns every single post into pipeline.

We built a system that converts LinkedIn engagement into qualified meetings.

Here's the breakdown we use at ColdIQ:

STEP 1: CAPTURE ENGAGEMENT IN REAL-TIME

Every like, comment, and profile view gets tracked automatically.

When someone likes OR comments on your post they're basically telling you they actually care about what you're talking about.

We use Trigify.io and Teamfluence™ to monitor all LinkedIn engagement signals.

Most people get that engagement and then just move on to the next post - we actually do something with it.R

STEP 2: INSTANT ENRICHMENT

The moment someone engages, Clay pulls full contact and company data.

Job title, company size, tech stack, funding rounds, hiring signals - all of it happens in seconds.

STEP 3: AI QUALIFICATION

Not everyone who likes your post is actually a good fit prospect.

We use ChatGPT and Claude to score each contact against our ICP criteria.

- Low fit? Filtered out immediately
- High fit? They move to the next step

The AI looks at things like:

- B2B vs B2C
- Geographic fit
- Company size match
- Estimated customer lifetime value

STEP 4: CRM DEDUPLICATION

Before we do anything, we check: is this person already in our system?

Already a customer? Already in active conversations? Skip them completely.

New contact? Push to sequence.

The fastest way to look like an idiot is cold emailing your existing customers.

STEP 5: MULTI-SOURCE EMAIL VERIFICATION

We waterfall through Prospeo.io, FullEnrich, and LeadMagic until we get a verified email.

No single provider has 100% coverage.

But stack three of them together? You're getting close to 95% hit rate.

STEP 6: PERSONALIZED OUTREACH ON AUTOPILOT

Each prospect receives a tailored message via Instantly.ai that references the exact post they engaged with.

Not generic "Hey {firstName}" nonsense.

It's contextual: "Saw you engaged with my post about Clay workflows - we just built something similar for a client in your space that books them 2-3 meetings per week."

STEP 7: MEETING BOOKED

This system generated 144 meetings for us in a single month.

And it runs 24/7 without human intervention because:

- Every piece of content becomes a lead magnet
- Every engagement becomes a qualified opportunity
- The entire conversion happens automatically

If you're posting on LinkedIn without this system you're basically just creating content and hoping meetings happen (which they won't).

That's not how it works in 2025.

The companies winning are the ones that built actual systems to convert attention into pipeline.

Are you tracking your LinkedIn engagement right now?

(We can build this entire workflow for you, plus show you the other plays that got us to $6M ARR)

P.S: Tools don't book meetings. Systems do. And this is the system that's working right now.

4 GTM agents that save 2-5 hours per client every day.

They run:

Prospecting, content
AND market research on autopilot.

Most teams think AI is about writing faster. It's not.

It's about eliminating the 80% of GTM work that never should've been manual in the first place.

Here's the system:

AGENT 1: LEAD RESEARCH AUTOMATION

Manual prospecting is dead.

Every company profile gets captured, enriched, and scored automatically.

Pipeline:

- GhostGenius captures profiles
- We filter by credibility signals (200+ followers, real website, actual activity)
- GPT scores each profile against our ICP (in real time)
- LinkedIn IDs dedupe contacts so we never hit the same prospect twice

Clean, qualified prospects → straight into Google Sheets for outbound.

AGENT 2: LOCAL PROSPECTING ENGINE

Apify pulls local businesses from Google Maps.
Raw Google Maps data is a mess. Half the info is missing or outdated.

The fix:

- Google Search API fills missing metadata
- We scrape each website for keywords, offers, tech stack
- GPT classifies and enriches the business profile
- We extract missing emails, numbers, contact pages

Export to Airtable or Sheets → done

This turns low-quality data into sales-ready records.

AGENT 3: BRAND CONTENT GENERATOR

Drop a brand brief into a Google Form. Wake up to:

- LinkedIn posts
- Carousel copy
- Newsletters
- Hooks
- CTAs
- Variations
- TOV matched drafts

All written in your style, using your positioning.

Drafts go straight into Notion or Google Docs, ready for a 10 minute edit.

AGENT 4: REDDIT INTEL COLLECTOR

This one is my unfair advantage.

Most GTM teams ignore Reddit.
But it’s where your prospects say the things they’d never admit on a sales call.

Pipeline:

- Input a brand or website
- GPT identifies ICP signals
- Reddit API scrapes relevant threads
- GPT summarises pain points, the language prospects use, their frustrations, what they actually care about
- A daily insight report sent to your inbox

This is the closest thing to reading your market’s mind.

THE RESULT: AUTOPILOT PIPELINE

These 4 agents work together to create a system that runs 24/7:

1. High-fit leads → already in your CRM
2. Weekly content → drafted before you wake up
3. Market intelligence → delivered before your first coffee
4. Zero hours wasted on research, scraping, or repetitive admin

You’re not “doing more.”
You’re just letting the system do what humans shouldn’t.

And here’s the real shift:

> Companies scaling in 2025 aren’t hiring more SDRs.
> They’re eliminating the work SDRs used to do.

If you’re still manually researching prospects and writing content from scratch, you're working 10x harder than necessary.

We build these exact workflows in n8n - plus the other automation plays that save 15+ hours per week, per team member.

Which one are you building first?

4 GTM n8n Agents 

138 meetings from 144 LinkedIn posts (that's almost 1:1).

Here's our $6M ARR workflow:

Step 1: Content volume that converts

We published 144 LinkedIn posts last month. Not random content, everything's built around ICP pain points.

We're using Taplio and Scripe for inspiration, but we're analyzing what books meetings. We look at hooks, visuals, and formats from posts that converted.

Step 2: Paid amplification with actual ROI tracking

$5,100 in LinkedIn Ad spend generated 155K extra impressions at $33 CPM. But we ONLY boost organic posts that have already booked meetings.

We track which persona converts from each post type, then push those winning posts to that exact audience.

Step 3: Intent capture across every touchpoint

This is where most agencies lose pipeline. We're tracking everyone who shows intent but doesn't book.

Tool stack breakdown:

Website visitors: Instantly.ai, Midbound Vector 👻 and Common Room for anonymous visitor identification.

LinkedIn engagement: Trigify.io and Teamfluence™ for social signal monitoring

Step 4: AI-powered lead scoring in Clay

Every lead gets routed to Clay for intelligent scoring. We use Claygent for lead research, then OpenAI to segment into 3 tiers based on fit and timing.

Different tiers get different outreach strategies. A Tier 1 lead gets way more personalization than a Tier 3.

Step 5: Data enrichment that works

Prospeo.io and LeadMagic for emails, FullEnrich for phone numbers - all happening inside Clay so we don't lose context.

Step 6: Multi-channel re-engagement based on score

This isn't random outreach. Every message is contextual, every touchpoint provides value.

The results that matter:

- Made us 557k in revenue for October
- Team grew to 30+ FTEs
- 750+ meetings booked in 2025 already
- One client: $272,600 closed at 7X ROAS in 90 days

The system works because it's not just outbound - it's orchestrated revenue generation across every touchpoint where your buyers show up.

We're adding this GTM workflow FREE for the next 3 ghostwriting clients (15 of 18 spots already taken). Comment 'CONTENTIQ' to grabs yours ☝️

How I'd build a repeatable outbound system if I had to start from scratch:

(There's a public database of your competitor's churn reasons... and most teams aren't using it)

1/ I'd start by scraping competitor reviews

You can use G2. Or Capterra. Or Trustpilot.

And it's full of pain points, written by people actively looking for a better option.

Focus on 2–3 core competitors with a decent number of reviews.

It's a goldmine. You'll find everything from UI complaints to missing integrations to slow support.

Tools you can use:
- n8n / Make (for scraping)
- Apify / PhantomBuster (pre-built scrapers)

2/ I'd extract pain themes using AI

We don't read these manually.

We use ChatGPT to group complaints into themes:
- Feature gaps
- Support response time
- UX or onboarding issues
- Security concerns
- Pricing

You now have a live database of what your prospect hates about their current tool.

Tools you can use:
- ChatGPT (for clustering complaints)
- Google Sheets (for organizing themes)

3/ I'd align those complaints with my actual positioning

Map those complaints to your actual value props.

Here are 3 examples:

a. If their reviews mention "hidden fees," and your pricing is transparent, lean into that.
b. If they complain about integrations you support, name them.
c. If support is a weakness and you have a live-chat SLA, say it.

Now you're not just pitching. You're presenting a clear solution to a known frustration.

Tools you can use:
- Loom (for quick demo videos)
- Your pricing page / integration docs (as proof points)

4/ I'd launch targeted outbound

Once we know who's unhappy and why, we push that data into:
- Clay (for enrichment + personalization)
- Instantly.ai (for sending)

The messaging angle is already validated, and the frustration warms the list.

You're simply showing up with a better option.

Tools you can use:
- Clay (enrichment + personalization)
- Instantly.ai / Smartlead (for sending)

5/ I'd automate it into a system that runs weekly

We've built versions of this using:
- n8n (for scraping)
- ChatGPT (for clustering complaints)
- Google Sheets (for inputs)
- Clay + Instantly.ai (for execution)

It runs weekly. We review angles, update copy, and refresh the list.

Once it's live, it becomes one of your highest-converting outbound motions.

Why? Because you're hitting real pain points with a proven better solution.

Tools you can use:
- n8n / Make (workflow automation)
- ChatGPT (analysis)
- Google Sheets (central hub)
- Clay + Instantly.ai (execution)

6/ I'd run this consistently

This works across industries, and especially well in crowded SaaS categories.

But it requires setup. And it requires letting the system run long enough to see results.

Give it a shot.

P.S: Questions? Drop them below or DM me. Happy to unpack any step. 👇

Clay is one of the most powerful tools in B2B right now.

But most people don't know where to start.

Most people open a Clay table, stare at it for 10 minutes, and close it because they don't know what to build.

We've built 500+ Clay tables for clients. Here are the 5 workflows you should start with:

Workflow 1: Waterfall Email Finder

The problem: If you use one email finder you are getting something like a 40% finding rate. That means 60% of your list is useless.

The fix: Stack multiple providers in sequence. Start with the first one (ie Prospeo.io). If the first provider doesn't find the email, try another one like LeadMagic. You can run more than 10 different providers to maximise your chance to find the right email. 

Result: 85-90% email findign rate vs 40% with a single provider.

Workflow 2: Signal-Based List Builder

The problem: You're blasting the same message to everyone. 

The fix: Pull companies from Ocean.io or Apollo. Then start to enrich to find relevant signals. 

For example: Use Claygent to scrape funding news. Track hiring spikes with LinkedIn enrichment. Check tech stack changes with BuiltWith. Score them: 3 signals = immediate outreach, 2 = nurture, 1 = long-term.

Workflow 3: Inbound-led Outbound 

The problem: People are visiting your website but you are not capturing the intent.

The fix: RB2B identifies website visitors. Push to Clay for enrichment. Find decision-makers at those companies companies. Then push the data to Instantly.ai for outreach.

Workflow 4: LinkedIn Engagement -> Outreach

The problem: People engage with your posts and you never follow up.

The fix: Trigify.io tracks who engages with your posts. Push people engaging with your content to Clay. Enrich to find more relevant data. Do some ICP scoring and reach out to leads fitting your ICP. 

Workflow 5: Champion Reactivation

The problem: Your past champions changed jobs and you're not tracking them.

The fix: Export past customers from your CRM. Use UserGems 💎 or Clay tracks job changes. Enrich to rind the right data then reach out offering to help again.

Stop staring at empty Clay tables.

Build these 5 workflows first and you'll already have a good outbound engine going.

Which one are you starting with?

We scaled to $6M ARR.

If I had to start again, this is what I’d build first.

Most people overcomplicate GTM.

They chase channels. Growth hacks. Headlines.

But if you strip everything back, early-stage growth comes down to a few simple systems done well.

Nothing fancy.
Nothing loud.
Just the foundations that actually move pipeline.

If I was starting from zero again, here’s exactly what I’d put in place.

1️⃣ Email infrastructure

I wouldn’t touch ads.
I wouldn’t build a giant content engine.
I’d start with email, because it scales the fastest when you're unknown (yet).

The setup is simple:

→ 20 secondary domains
→ 40 mailboxes
→ 20–25 emails per mailbox per day

Warm everything up for two weeks.
Authenticate properly (SPF, DKIM, DMARC).
Rotate domains every 9–12 months.

Do this right and 95%+ of your emails land where they're meant to: the primary inbox.

2️⃣ Lead signals

Most people email too early.

They reach out when they want the meeting, not when the buyer is actually looking.

So I’d track 10+ buying signals first - simple things that show intent:

- Downloads
- Hiring spikes
- Website visits
- Funding rounds
- Tech stack changes
- Competitor research
- LinkedIn profile views

The goal is timing.

You don't need more leads.
You need leads who are ready.

3️⃣ Sequence timing

For SMB:

5–8 touchpoints across 30 days.
Shorter gaps. Faster signals.
A simple mix of email + LinkedIn.

For enterprise:

10–18 touchpoints across 60 days.
More education.
More channels.
Less pressure.

4️⃣ GTM workflows

This is where most teams fall apart.

Deals ghost
Leads sit untouched
Handoffs get messy
Nobody knows who's owning what

I'd set up 6 workflows on day one:

→ Ghosting (auto-close inactive deals)
→ Discovery nurture
→ Proposal follow-up
→ Closed-won onboarding
→ Lead routing
→ AE handoff

It’s not exciting work.
But it removes chaos.
And chaos is what kills early GTM.

5️⃣ The metrics that matter

People still obsess over open rates.
They don’t matter.

Track reply rate.
Track positive replies.
Track meetings booked.
That’s it.

Industry reply rate is 1–5%.
Good is 10%+.
Great is 25%+.

PRR tells you everything you need to know:
How many people said yes.

Nothing else matters.

6️⃣ Reporting

If you want pipeline to compound, you can’t just “hope it works.”

I’d check the same dashboard every day:

Deliverability >95%
Reply rate 15%+
Positive replies 1–5%
Meetings booked
Show rate
SQL conversion
Close rate
Spam rate
Bounce rate
Domain reputation

If I had to start again, I wouldn't try to do all the things. I'd build these foundations, stay patient, and let the system do its job.

Everything else comes later.

What would you add to this list? 👇

P.S. Kenny Damian crushed it putting this GTM cheatsheet together - feel free to send a DM if you need help with this.

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Alex Vacca 🧠🛠️

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Alex Vacca 🧠🛠️

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