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 🧠🛠️

I built Claude Skills that create a full GTM team, generate campaign ideas, and write cold email sequences for you.

All inside Clay. Without switching tools.

Instead of sharing "AI prompts."
I’ll share the skill files that actually work inside Clay in our $7M Agency.

Generic prompts give you generic outputs.
These are different.

Each skill file is built for a specific Clay workflow:

→ Company Research Agent
→ Personalization Writer (that doesn't sound like AI)
→ ICP Scorer
→ LinkedIn Profile Analyzer
→ Data Cleaner & Normalizer
→ Objection Handler
→ Email Sequence Writer
→ Competitor Analyzer
→ Job Posting Analyzer
→ Technographic Qualifier
→ News & Signal Synthesizer
→ Account Brief Generator

Here's how it works:

Drop it into Clay → Map your columns → Run.

Built from hundreds of Clay tables that got us to $7M ARR.

And today I'm giving the full pack away for free.

Want them?
Comment "SKILLS" and I'll DM it over.

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

Most people think that impressions = revenue.

Here is what to do instead: 

Most people see content funnels and think they just need to follow the formula.

40% awareness, 30% consideration, 30% conversion.

Looks great in a strategy doc. 
But here's what actually happens:

You're so busy optimizing percentages that you forget to make content people actually want to read.

I learned this the expensive way at $2M ARR.

Posts hitting 200k impressions with zero pipeline.

Because the content was checking boxes instead of starting conversations in my client's mind.

There’s one shift that took us from $2M to $7M ARR in under 2 years:

We stopped asking "what funnel stage is this?" and started asking "would I actually stop scrolling for this?"

Here's the structure that worked, but not how you think:

→ Top of Funnel (40% of your content)

Yes, get attention. Get people to know you exist.

But if your viral content isn't tied to your expertise, you're just collecting the wrong audience.

Generic thought leadership that 500 other people posted this week it’s not awareness. It’s noise.

→ Middle of Funnel (30% of your content)

Show your actual work. 

Walk people through your process, your thinking, the results you've driven.

This isn't about proving you're smart. It's about showing them you get it.

Most "consideration" content fails because it reads like a sales pitch disguised as education.

→ Bottom of Funnel (30% of your content)

Be direct about who you help and how you help them.

Most people skip this entirely because they think it's too "salesy."

That's exactly why their content never converts.

But here's the thing:

The content that drove the most revenue for us didn't fit neatly into these buckets.

Some posts drove awareness and conversion at the same time.

Others built trust by being brutally honest about what didn't work.

The posts that made people say "these guys actually get it" did more for our pipeline than any perfectly orchestrated funnel.

Stop optimizing the structure and start making each post worth reading.

What's your technique to transform impressions into revenue?

My team didn't want me to give this away for free.

We built the biggest Skills database for GTM.

And I'm sharing it. You just copy + paste into Claude.

Outbound

• email-infra – checks deliverability basics so you don't nuke domains
• list-building – builds ICP lists + exclusions + prioritization
• copywriting – writes sequences that don't sound like templates
• clay-operations – enrichment + waterfall logic + clean field mapping

RevOps

• inbound-orchestration – capture → qualify → route → follow-up flows
• lead-routing – ownership rules, SLAs, edge cases, round robin
• lifecycle-model – MQL/SQL/SAO definitions + required fields
• pipeline-reporting – weekly exec view: created, influenced, velocity

ABM

• account-selection – fit + signals → tiering
• persona-mapping – buying roles, pains, triggers, proof points
• linkedin-ads – structure, testing plan, creative direction
• ads-outbound-signaling – sync ads → outbound

And those are just a few.

We're constantly updating the database with new Skills + improved versions.

How to use them:

→ Copy/paste the Skill into Claude
→ Tell it what you need
→ It reads the Skill, then helps you execute

No complex setup.
No API keys.
No code.

Just copy, paste, ask.

Comment "SKILLS" and I'll send you free access.

I'm doing something stupid.

Sharing my entire AI GTM resource library.
In a Notion file. For free.

What's Inside:

→ Best AI Sales Tools, B2B Data Providers & Tech Stack for 2026 
→ 37 Best Performing Sales Triggers (the ones that actually book meetings)
→ Cold Email Infrastructure Setup Guide (deliverability frameworks we run for clients)
→ Campaign Strategy Templates (HOT Copy Framework, lead magnets, trigger-based outreach)
→ Clay templates, GPT recommendations, and content ideation systems

It's a Notion doc. With sections. And a lot of tactical information.
But it's what we actually use at ColdIQ to run GTM for 80+ clients.

When I need to:

→ Build a campaign from scratch for a new client
→ Fix someone's cold email deliverability in under 24 hours
→ Remember which sales trigger converts best for SaaS vs agencies
→ Compare data providers without wasting 3 hours on research

I open this library. That's it.

So if you want it, comment "𝐋𝐈𝐁𝐑𝐀𝐑𝐘" below.
I'll DM you the link.

Save this post if you think you'll need it later. Share it with someone scaling GTM.

We built an ABM system that generated $2M in pipeline in 90 days.

Here's the 4-step playbook behind it.

Step 1: Build a high-potential account list

We looked at our closed/won deals and reverse-engineered the ICP from there.

Then we found lookalike companies, qualified them, and enriched the contacts.

1,250 accounts made the cut and were added directly to our LinkedIn Ads campaign manager.

Step 2: Create thought leadership content worth promoting

We went through our last 100 qualified sales calls.

Pulled out the exact pain points, objections, and language prospects were using.

Then studied what was already performing well in the niche.

That became the content engine, published across every active leadership profile in the company. 

Step 3: Turn organic wins into paid distribution

Every post that performed well organically became an ad.

Promoted directly in front of those 1,250 target accounts.

We tracked the pipeline through UTMs and monitored the full journey from impression to engagement to pipeline.

After about a month of warming up the audience, we layered in mid-funnel and bottom-funnel retargeting.

Step 4: Warm outbound on engaged accounts

We monitored every like, comment, and website visit from target accounts.

De-anonymized traffic. Identified qualified leads.

Then routed them into automated outbound sequences.

The result:

- $2M in pipeline.
- Under 90 days.
- Each channel feeds the others.

Ads warm up outbound. Content fuels ads. Outbound re-engages the hand-raisers.

It's not about picking one channel.

It's about making them work together.

What's your biggest challenge with ABM right now?

If I wanted to turn my entire company into LinkedIn marketers tomorrow, here's the exact playbook I'd follow.

Not hire a ghostwriter. Not ask the marketing team to "post more."

You have to implement a real system.

Step 1: Get every customer-facing team member posting.

Not just the founder. SDRs, AEs, CSMs. Anyone who talks to prospects or customers has stories worth sharing.

The goal isn't polished thought leadership. It's genuine insights from people doing the work every day.

Step 2: Centralize the scheduling.

You can't manage 5-10 people posting across LinkedIn natively.

I'd move everyone into Taplio so there's one place to schedule, review drafts, and track what's performing.

The analytics layer matters here. You need to see what's working across the whole team, not just individual accounts.

Step 3: Build shared engagement lists.

This is where most companies stop and it's where the real leverage starts.

I'd create lists of target accounts, partners, and industry voices inside Taplio.

Then every team member spends 15 minutes a day engaging with posts from those lists. Meaningful comments. Real takes. Not "love this."

When a prospect sees 3 different people from your company showing up in their feed with valuable insights, something shifts. You're not a cold outreach anymore. You're a brand they already recognize.

Step 4: Run a weekly content review.

Every Friday, pull analytics for the team. Which hooks worked. Which formats drove comments. Which posts generated inbound DMs. Double down on what's working. Kill what isn't.

At ColdIQ, our team publishes 200+ content pieces a month. That contributed directly to scaling to $7M ARR.

But it didn't start at 200. It started with a system.

The volume came after.

How many people on your team are posting on LinkedIn?

We've built GTM systems for 267+ B2B companies. Most were running 20+ tools. They didn't need half of them.

What they needed was the right tools in the right layers, connected to each other.

Here's the 12-tool, 5-layer architecture we now build every system on:

1. Signal layer

This is where buying intent gets detected.

PredictLeads - tracks hiring surges, tech adoption, funding rounds, and news events across 100M+ companies

Common Room - merges community signals, product usage, G2 intent, and job changes into one unified view

Attention - lets you query your entire sales call history with natural language prompts

Without this layer, you're guessing who to reach out to.

2. Data layer

This is where raw signals become actionable contacts.

Openmart- 200M+ local business records with verified owner contacts

Wiza - converts LinkedIn profiles into emails, phones, and firmographics (up to 2,500 contacts per batch)

Prospeo - 98%+ verified email accuracy across a 200M+ contact database

FullEnrich - waterfalls through 20+ providers until it finds verified data (80%+ find rates)

Apify - custom data extractor for anything the other tools don't cover

Without this layer, your signals have no one to send to.

3. Action layer

This is where outreach actually happens.

Instantly.ai - high-volume cold email with full API control over campaigns, warmup, and deliverability

lemlist - multichannel sequences combining email, LinkedIn, and calls

The enrichment data decides the channel.

LinkedIn URL found? Route to Lemlist.
Email only? Route to Instantly.

Without this layer, your enriched data just sits in a spreadsheet.

4. System of record

This is where every touchpoint gets tracked.

Attio - full CRM with API access to deals, contacts, companies, custom objects, and pipelines

Every signal, enrichment result, and outreach event writes back here automatically.

Without this layer, your team has no shared source of truth.

5. Revenue layer

This is where the loop closes.

Hyperline - handles billing, subscriptions, usage metering, and invoicing with webhooks for every payment event

Without this layer, you're automating everything except getting paid.

That's the full architecture:
Signal → Data → Action → CRM → Revenue.

Claude Code connects every layer by reading the API docs, writing integration scripts, handling errors, and retrying until the pipeline works end-to-end.

No copy-pasting between dashboards.
No manual handoffs between stages.
No engineering team required.

Which of these 5 layers is the biggest gap in your current setup?

I just crossed 60,000 followers on LinkedIn with this exact tech stack

1. Taplio (Post ideation)

This is where I discover high-performing posts in my niche to draw inspiration from.

I break down what made them successful and adapt those insights to my own content.

2. Scripe (Team analytics)

I analyze viral content from top creators in my space.

It also lets us monitor our entire team's posting performance so we can replicate what's actually working.

3. Grammarly (Writing assistant)

Fixes my typos and grammar errors while drafting posts.

4. Canva / Figma / Adobe (Visual design)

Every post I publish includes a visual element.

We've developed a distinct visual style that audiences now recognize and connect with my brand.

5. Claude (Content brainstorming)

Helps me generate fresh hooks, rework successful content, and discover different angles for posts.

It's also great for understanding why certain posts went viral on LinkedIn.

6. Screen Studio (Screen recordings)

Creates professional-looking screen captures.

It automatically focuses on whatever part of the screen you're working with.

7. Ezgif (GIF creation)

Converts my screen recordings into GIFs.

I typically accelerate videos to stay under 10 seconds. This makes people replay the GIF multiple times to catch all the details.

8. Notion (Content planning)

My hub for organizing my content calendar, storing ideas, and writing drafts.

It's where all my content strategy lives.

What tool am I missing?

tools to 10x on LinkedIn 

We've built GTM systems for 267+ B2B companies. Most were running 20+ tools. They didn't need half of them.

The teams that close the most deals don't have the biggest stack. They have the leanest one.

Here's what actually works in 2026 across every layer of the revenue stack:

1) CRM 

We started on HubSpot. Data lived in 3 places. Nothing talked to each other. Now we run everything on Attio. If your team is under 100 people, you don't need a $50K/year CRM. You need one that plugs into your stack without a 6-month implementation.

2) Data Enrichment 

In 2021, you'd pay ZoomInfo $30K/year and still manually verify half the data. We replaced all of that with Clay. One workflow chains 50+ data sources. FullEnrich for phone numbers. What used to take a team of VAs a full week now runs in minutes.

We literally used to hire virtual assistants to write the first line of every cold email by hand. Clay killed that workflow overnight.

3) Outreach

This is where most teams still burn money. In 2021, sending 500+ emails a day was standard. 

The numbers tell the story: the bottom 10% of outbound teams send the most volume for the fewest replies. The top 10% send micro-campaigns and achieve the highest positive reply rates. Same tools. Opposite strategy. Completely different results.

We use Instantly.ai for cold email with warmup and inbox rotation built in. 50 targeted emails beat 500 spray-and-pray sends every time.

4) Intent Signals 

This category barely existed 3 years ago. Now it's the backbone of what we do at ColdIQ

We don't prospect cold anymore. We stack signals: did the company hire recently? Are they hiring a GTM engineer? Did headcount grow 20% in 6 months? Did they raise a round?

We use RB2B to identify anonymous website visitors at the person level, then push that into Clay and trigger outreach automatically. Cold outbound is dead. Warm outbound is all we run.

5) CPQ

Salesforce CPQ entered end-of-sale in March 2025. Most of our clients were quoting with spreadsheets and PDFs anyway. Hyperline now connects CRM to billing natively.

6) Billing 

67% of SaaS companies now use usage-based pricing. Legacy billing tools weren't built for hybrid pricing, subscriptions plus usage in one system. Hyperline handles this natively. 

7) AI & Automation 

This layer changed everything for us. In 2021, "AI in sales" meant lead scoring models nobody trusted.

Now AI runs the infrastructure. At ColdIQ, one GTM engineer can manage 10 clients. Three years ago that number was 3. We use Claude Code to build internal tools, research accounts, and write personalized copy at scale. Clay orchestrates the data layer. n8n connects every workflow in between.

—

The 2021 approach: bolt together 20 tools and hope the data syncs.
The 2026 approach: build a connected revenue system where every layer feeds the next.

What's the most outdated tool still in your stack?

This AI onboarding system has saved us $29,013 → off a single deployment.

I wanted to simplify the onboarding of new clients without relying on 10 different tools or thousands of manual tasks.

When ColdIQ passed 30 active clients, onboarding started breaking.

4 hours per client. Contracts going out late. Payments chased manually. Folders set up wrong. Team finding out last minute.

I didn’t want the team to lose time on tasks that could be automated.

So I built an AI agent that runs the whole thing:

→ Contracts sent and signatures tracked automatically
→ Invoices are fired the second a deal hits "Closed Won"
→ Drive folders and CRM records created instantly
→ Slack pings sent to the right people
→ Kickoff call booked
→ Welcome sequence goes out to the client

It runs 24/7 without missing a step.

The result?

→ 12 minutes instead of 4 hours
→ 0 errors across 47 onboardings
→ Team doing actual work, not admin
→ Every new client gets a clean experience from day one

Another agency saw it in action and wanted the whole thing.

The agents. The workflows. The logic.

Why am I sharing this?

Because this system can transform how other companies manage their onboarding, and I also benefit from every implementation.

You win. I win.

I'll send you the full workflow:
→ Same tools
→ Build order
→ Logic

Want to duplicate it in your business?

You can get access to the whole agent here: https://lnkd.in/dKAyEgGM

We've built 3200+ outbound campaigns at ColdIQ.

Here's how we ideate every single one:

𝗦𝘁𝗲𝗽 𝟭: 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗜𝗖𝗣 𝗠𝗮𝘁𝗿𝗶𝘅

Every client gets one in before we touch anything.

We look at:

→ Industries where they already have traction
→ Company sizes they typically win
→ Titles that actually buy (not just influence)

If there are multiple segments, we pick the one with the most clarity first.

𝗦𝘁𝗲𝗽 𝟮: 𝗙𝗶𝗻𝗱 𝗹𝗼𝗼𝗸𝗮𝗹𝗶𝗸𝗲𝘀 𝗼𝗳 𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝗰𝗹𝗶𝗲𝗻𝘁𝘀

Simple question: "Who else looks like the people already paying you?"

We run Clay enrichments to find patterns.

Industry, headcount, region, tech stack.

Then we use Ocean.io to build a match list.

We're looking for repeatable wins. Not unicorn edge cases.

𝗦𝘁𝗲𝗽 𝟯: 𝗟𝗮𝘆𝗲𝗿 𝗶𝗻 𝘁𝗿𝗶𝗴𝗴𝗲𝗿𝘀

The best campaigns don't just target the right person.

They target the right person at the right moment.

Triggers we look for:

→ Fundraising rounds
→ Headcount growth
→ New tech adoption (Salesforce, HubSpot, Shopify)
→ Job openings in key roles
→ Leadership changes (new VP of Sales, new CRO)

Combine triggers with firmographics and the campaign gets tight.

𝗦𝘁𝗲𝗽 𝟰: 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗯𝗲𝗳𝗼𝗿𝗲 𝘆𝗼𝘂 𝗹𝗮𝘂𝗻𝗰𝗵

Before we build anything, we ask 3 things:

→ Is there enough volume? (at least 500–1,000 accounts)
→ Does it align with what the client actually wants to sell?
→ Have we confirmed the idea with the client?

Alignment > Assumptions. 

𝗦𝘁𝗲𝗽 𝟱: 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝘁𝗵𝗲 𝗱𝗼𝘁𝘀 𝗶𝗻𝘁𝗼 𝗰𝗼𝗽𝘆

Now you have the persona, the trigger, and the data.

The last step is writing copy that speaks directly to their situation.

Reference the trigger
Address the pain it implies. 
Then offer something that solves that exact problem.

Generic copy talks to everyone.
Trigger-based copy talks to one person at the right time.

That's the difference between a 2% and a 10% reply rate.

Most teams skip ideation and jump straight to copy.
That's why most campaigns underperform.

The best outbound starts before the first email is written.

What else would you add?

Most AI explanations overcomplicate things. Here are the 7 layers of Agentic AI explained so simply that your entire team will get it:

Think of AI capabilities like building blocks.

Each layer adds capabilities that the one below it can't provide on its own.

Here's the stack:

Layer 1: Machine Learning (The Foundation)

This is where AI learns patterns from data.

→ Supervised learning (teaching with examples)
→ Unsupervised learning (finding hidden patterns)
→ Reinforcement learning (trial and error optimization)

Think: Apollo's lead scoring, Instantly.ai's send time optimization

Layer 2: Large Language Models (The Processor)

Now AI understands and generates human language.

→ Processing massive text datasets
→ Understanding context across conversations
→ Handling multiple input types (text, images, audio)

Think: ChatGPT, Claude, Gemini

Layer 3: Generative AI (The Creator)

AI creates new content, not just analyzes it.

→ Writing code and content
→ Generating images and videos
→ Converting speech to text and back

Think: Midjourney, ElevenLabs.

Layer 4: AI Agents

AI that can use tools and execute tasks.
→ Breaking down complex requests into steps
→ Calling APIs and using functions
→ Remembering context between actions

Think: Claude with computer use, GPT with function calling

Layer 5: Agentic Systems (The Team)

Multiple specialized AI agents working together.

→ Each agent has a specific role
→ They coordinate and share information
→ Parallel execution of different tasks

Think: Lindy, Relevance AI

Layer 6: Agentic AI (The Strategist)

Agents that run autonomously for extended periods.

→ Long-running operations without human input
→ Self-correction based on results
→ Adjusting approach when something fails

Think: AI SDRs running full outbound sequences Artisan, 11x

Layer 7: Governance & Safety (The Guardrails)

What keeps everything from going off the rails.

→ Human oversight at key checkpoints
→ Cost limits
→ Kill switches

Think: Enterprise AI deployment infrastructure

Most companies are stuck at Layer 2-3.

The ones moving fastest are building at Layer 5-6.

Where's your team right now?

—
Repost to share it with your network
Follow Alex Vacca for more insights on how to scale revenue with AI and tech

The agency model as you know it is about to become obsolete.

And Y Combinator just confirmed it.

In their Spring 2026 Request for Startups, they officially listed AI-native agencies as one of the biggest opportunities they want to fund.

Their words: "Agencies have always been crazy hard to scale. Low margins, slow manual work, and the only way to grow is to add more people. But AI changes this."

This isn't some random prediction. 

This is the fund behind Stripe, Airbnb, and OpenAI telling the world: the agency game is about to change forever.

Here's what we figured out early:

The agency world is splitting into two completely different businesses.

Camp A: Agencies that sell hours.
Camp B: Agencies that sell systems.

We chose Camp B.

We don't scale by hiring more SDRs, researchers, or copywriters. We scale by building AI workflows that do in minutes what used to take a team days.

Lead research. List building. Personalization at scale. Signal tracking. Follow-up sequences.

All running on AI-powered systems now.

And YC is saying exactly what we've been experiencing, these agencies will start looking more like software companies. With software margins.

The model works like this:

→ Services fund the development
→ Repetitive client work becomes automated IP
→ The IP becomes the real asset

Most agency owners will read this and go back to hiring their next account manager.

But a few will realize the window to make this shift is right now.

YC doesn't add something to their wishlist unless they see a massive market opportunity forming.

If you run an agency and you're still scaling with headcount instead of systems, this is your wake-up call.

Repost with your network so they can take advantage
Follow Alex Vacca 🧠🛠️ for more about how to scale revenue with AI and Tech

Our business hit $7M ARR in 3 years.

No VC money. No outbound team when we started.

All from one thing: content.

One post changed everything.

23 inbound meetings. From a single LinkedIn post.

That was the moment I realized:

Content IS the pipeline.

ㅤ
But most people treat LinkedIn like a job board.

They post what they want to talk about.

Not what their buyer needs to hear.

So we built a system around it.

ㅤ
1/ We stopped posting about ourselves.

Started answering the exact questions our ICP was already asking in Slack channels, Reddit threads, and sales calls.

If they were struggling with list building, we broke down our entire process.

Step by step.

ㅤ
2/ We picked 4 content pillars and never deviated.

→ GTM systems and strategy
→ Tech stack breakdowns (tools we actually use)
→ Workflows and automations (screen recordings, walkthroughs)

Every post had to fit one of these four.

ㅤ
3/ We gave away everything.

Templates. Frameworks. Playbooks.

The stuff agencies charge $5K+ for.

People said we were insane. "You're giving away your IP."

But here's what happened:

→ 100+ inbound meetings every month from LinkedIn alone
→ 267+ B2B clients served without sending a single cold DM
→ Pipeline that compounds every month because old posts still drive traffic

When you teach someone something valuable, they don't want to figure out the rest alone.

They want YOU to do it for them.

ㅤ
4/ We turned content into a flywheel.

Content → Engagement → Warm signals → Outbound → Retargeting → Conversion.

All of that becomes signal.
Signal becomes outbound.
Outbound becomes pipeline.

That's how you build a $7M ARR business from content.

ㅤ
If you want to see how our full GTM Flywheel system works and how content fits into it.

Click here: https://lnkd.in/dy8dMZ8e

Lessons from scaling to 7M ARR

30M impressions. 100 inbound meetings/month. It wasn't enough. Here's what we were missing.

We just added $506K in contract value from SEO and AI search in 4 months.

Here's how it happened.

Buyer research has changed fast. More people are using ChatGPT, Perplexity, and Claude to solve problems, compare vendors, and build shortlists.

All before they ever talk to sales.

For us, that meant one thing: If someone is searching for the problems we solve or evaluating agencies like ours, we need to show up in those decision moments.

4 months ago we started working with Reach AI to turn SEO and AI search into a real growth channel. Not top-of-funnel traffic. High-intent searches where people are actively researching solutions and shortlisting partners.

Here's what changed:

→ Organic + LLM search combined generated $506K in contract value
→ LLM search traffic grew 7.6x in 3 months
→ Organic search traffic grew 7.3x in 4 months
→ Combined traffic share went from 16.5% to 33.3%, doubling in 3 months
→ SEO and AI search is now our #1 traffic source
→ Ranking keywords increased by +41,470 (+983%)

And this doesn't even fully capture the AI search impact on direct traffic.

Here's the truth about attribution: It's messy. Anyone claiming perfect tracking is either naive or trying to sell you something.

In practice, we track first click and last click in UserMaven, look at assisted conversions, and ask prospects on sales calls where they first heard about us. Then we log it in the CRM. That's enough to guide decisions without pretending the model is perfect.

But it still undercounts AI search. A prospect discovers ColdIQ through an AI answer, then later Googles "ColdIQ" or types coldiq.com directly. The original AI touchpoint disappears.

Shoutout to José Velez, Bernardo Ferreira, and the Reach team. 

They pull real questions and intent signals from where buyers actually learn and decide. Then they narrow it down to the keywords and prompts that really matter for pipeline.

From there, they monitor how different AI models respond, reverse-engineer what patterns win, and adapt as models evolve.

That's what makes SEO and AI search feel less like guesswork and more like a repeatable system.

If you're a B2B company wondering whether SEO and AI search optimization can actually generate pipeline: This is the most concrete proof point I've seen.

Let me know if you want an intro.

Sales thinks marketing sends garbage leads.

Marketing thinks sales can't close. And both are right. 

Most companies run two completely different TAM lists.

Marketing has theirs. Sales has theirs. And they quietly hate each other's leads.

Marketing is measured on MQL volume, so they optimize for quantity. 

They cast a wide net, run campaigns to anyone who vaguely fits the ICP, and celebrate when form fills go up.

Meanwhile, sales is on the phones trying to close these leads and getting nowhere. 

The leads don't have budget. They're not decision-makers. They downloaded an ebook six months ago and forgot your company existed.
 
Sales complains that the leads are garbage. 
Marketing complains that sales can't close. 
Leadership sits in pipeline meetings wondering why the funnel is leaking.

Everyone points fingers but nothing gets fixed. 

The root issue is deceptively simple: 
Marketing and Sales aren't working from the same scored account list.

Here's how to handle it differently.

↳ You build one master TAM.

Not a marketing list and a sales list.
One list.

You pull in every company that could theoretically buy from you, then you enrich the hell out of it.

Firmographics like headcount, revenue, industry, location (Clay, FullEnrich).

Technographics like what tools they're currently using (BuiltWith, TheirStack).

Funding data, growth signals, hiring patterns (Crunchbase, PredictLeads, LinkedIn Sales Nav).

All of it lives in Clay where you can manipulate and score it.

Then you create a scoring model based on your closed-won data.

Companies that look like your best customers get high scores.

Companies that look like your churned customers or closed-lost deals get lower scores.

Now every account has a number attached to it.

Marketing runs campaigns against the high-scoring accounts.

Sales prioritizes outreach to those same accounts.

When a lead comes in, the rep already knows the account score before they pick up the phone.

They know whether this is a Tier 1 account worth dropping everything for or a Tier 3 account that can wait.

The magic isn't in the scoring model itself. It's in the alignment.

Marketing stops chasing MQL volume because they're measured on pipeline from target accounts.

Sales stops complaining about lead quality because the leads actually match the profile they want to sell to.

One list. One scoring system. One source of truth.

Everything else flows from there.

What's your current system for aligning sales and marketing on target accounts?

How do you 10x your pipeline with outbound + ads running together?

We're showing you on Thursday.

At ColdIQ, we run outbound and paid media as one motion. Not two separate teams hoping for the best.

Same accounts. Same messaging. Same timing.

We've done this across 70+ B2B clients.

On March 12th, we're breaking down the exact playbook with AirOps. Live.

What we'll cover:

Account-Based Outbound
→ Building and tiering target company lists with Clay
→ Syncing outbound and LinkedIn Ads to hit the same accounts
→ Signal-triggered plays that actually convert

Thought Leader Ads
→ Why we put 80% of ad budget on founder content, not brand ads
→ Creating webinar-based outbound plays
→ Cross-channel feedback loops between outbound and paid

The Results
→ 10x ARR growth
→ $10M+ qualified pipeline
→ 15x pipeline ROI
→ 164 deals, 30 closed-won
→ 1,058 outbound replies

We won't make it high-level, we will show the entire playbook we run every day.

Hosted by Ivan Falco (Head of Growth at ColdIQ) and Eoin Clancy (VP of Growth at AirOps).

March 12th. 1:30 PM EST.

Link to register below 👇

https://lnkd.in/dVSm55ki

Experienced Industrial Engineer, Certified Personal Trainer, and B2B Email/Messaging Specialist, eager to help in process improvement and ideas to automate tedious tasks.

First off, I'm human!

Experienced Clay cook :D, Industrial Engineer, and B2B sales development professional with a proven record of driving growth and brand awareness through messaging campaigns. Eager to help in product success, account/project management, process improvement, automation, or analytical tasks, where I can leverage and further develop my technical, copywriting, analysis, problem-solving, and informed decision-making skills to contribute to growth.

I’m a passionate and continuous learner. My main go-to topics are philosophy (mostly about morality and epistemology), strength and conditioning, exercise and sports science, health, and human behavior, and some finance; I also enjoy taking courses that improve my skills or allow me to acquire newer ones, especially analytical, scientific understanding, and use of technology such as Google Suite or Microsoft Office, especially AI nowadays!

Additionally, as a resistance/strength training aficionado and Certified Personal Trainer, I enjoy learning and applying scientific principles in my programs that allow for the best results that fit my lifestyle and goals. This gives me the opportunity to use an analytical thought process and the occasional use of technology to measure and optimize progression. Also, looking forward to helping others in their journeys!

Star Gazer (and Star Wars fan). Though space travel is still a dream, one can always use some imagination to explore where humans can go.  Looking forward to what the JWST will help us discover, and how we can work alongside AI's capabilities to exponentiate our problem-solving systems worldwide.

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