🦾Eric Nowoslawski

I am a direct response outbound lead generation expert. And by that jumble of words, I mean that I leverage cold email to build predictable revenue pipelines for my clients. 

I leverage over 20 databases to merge Firmographic, Technographic, and Persona data to build highly targeted playbooks with my clients. 

I believe we need to arm sales teams with the knowledge of their total Target Addressable Market and teach them how to score that Market for which companies have the most pain and need their solution. 

The best way to do this is to look at public indicators such as open jobs data, employee growth/attrition, social media growth, web traffic, new product launches, employee sentiment, individual employee tech investment, company tech stacks, etc.

With this data, not only are you only messaging with people that you know have a problem that you can solve, you also are armed  with powerful messaging that cuts through the noise. 

I teach sales teams to embrace technology and multiply what is possible per employee.

Founder

2 years of effort went into the Prospeo.io Company/Contact API and this is why it was WELL worth the wait.

2 years ago Jon asked me, "If there were a database that could do anything you want, what would you wish it would do?"

I had a laundry list of ideas from

- Data accuracy and transperancy
- Organization tools for the team
- API capabilities

And then, I didn't hear from them for a little while. We kept using their email finding tool in all our Clay tables as a happy customer of course but I thought they were just focusing on that.

Then Hugo reaches out to me and says, "Hey, we finished the database the way you wanted. Want to have a look?"

I sign in to Prospeo V2 and my mouth is on the floor of everything they built. Good things definitely take time and they crushed it on this one.

I go over my favorite parts in the video down below but here are my favorite features you should check out if you don't watch the video and go straight to the site.

- Data quantity - I did comparisons of searches against Sales Navigator and was extemely happy with the counts that come back for the same filters on people and company searches in sales nav vs. prospeo

- Data quality - Every profile I checked worked at the company Prospeo says that they work at. I tried to be sneaky and check a friend that I know changed jobs a week ago and they already had that in their database.

- Email quality - All emails are SMTP verified with bounceban on top of that.
The API is so great. I just spent the christmas break making a company database we are going to scrap and just use their's.

- When you enrich a person, you get the company data with the API response so you don't have to worry about double spending on companies!

- The platform is so fast to make searches in.

- Your team can visually make searches and then click a button to get all of the JSON of that search to quickly get that search with their API.

I think they are just starting to launch it and I would highly recommend checking it out to help with your company and contact finding workflows. 

We are starting to switch a lot of TAM links internally over to them so we can stop downloading and uploading so many CSVs!

Not paid to post this or anything. 

Just found it last week and thought they did a really great job and other people would like to see it!

Check it out here! 

https://lnkd.in/dJw9eAEy

Founder Growth Engine X | Clay Enterprise Partner

Tomorrow, join me and many other outbound minds to discuss the state of outbound in 2026. (of course focused on cold email because I'm not qualified to talk about anything else). 

We’ll cover the dreaded Gmail AI filter tweet that hasn’t even been rolled out yet that everyone is asking about. 

Deliverability trends we are seeing from sending 7M emails per month on behalf of our customers

The way data is getting more accurate and cheaper and combining this with AI can cheaply laser target your ICP. 

I’ll leave the link to register in the comments. Hope to see you there! 

Vaibhav Namburi
Harris Kenny
Nick Abraham
Wesley Hoang 🛠️♠️
Alex Fine🌲
Christian Oland
Matthew Lucero
Enzo Carasso 🧲

I built a way to monitor your customer's LinkedIn content with a RapidAPI endpoint and Cursor so you can reach out to your ICP and name drop a customer they have heard of. Here's the step by step on how to do it.

I think one of the best ways that we can reach out to an ICP is if they already know of one of our successful case studies. 

You could ask your customer for a referral, but that puts work on them. 

You can monitor who they already know and reach out as if it's a regular message, and the prospect will actually have heard of the social proof you're mentioning. 

To set this up, all I did was give a voice note while walking my dog and Cursor took care of the rest. 

I hope you can even copy and paste this post and give some more context and set it up yourself. 

Step 1

Get the Rapid API endpoint. If you comment "monitor", I'll send it to you. Sorry to play this game but LinkedIn hates external links. 

Step 2

Use the MCP in RapidAPI and connect that to Cursor

Step 3

Explain what you want it to do. 

"Hey Cursor, I want to use this endpoint to build a micro app that I can enter a comma separated list of linkedin companies. I want you to 

1) find all of the posts of the linkedin companies
2) Find all of the people that work at the company and find their posts
3) Pull all of the people that have liked and commented on the posts and filter for XYZ titles and company size. 

Step 4

Add a little bit more to the prompt above about how you exactly want this to work.

Step 5

Enter Plan mode in Cursor. Drop your prompts and watch the plan

Step 6 

Edit/Approve the plan. I was lucky that Gemini Flash got this done in one shot, so I'm hoping I can pass this on and help others use it too. 

Again, comment "monitor" and I'll send it to you but if you don't want to wait for me, searching rapidapi(.)com(user)apibuilderz should help those that want to search on their own!

This workflow I made with Cursor and hosted on Railway saves me 6 hours a week on email deliverability tasks and our customers get much more consistent results from it. 

Over the last 6 months, we really systematized our email deliverability infrastructure. 

No more guessing and then ordering domains that we thought felt right. I built an SOP for everyone that I can give away at the end of the post. 

We would miss customers. Over order for some, under order for others. It was crazy. 

So we got extremely scientific about it. 

We keep all domains marked as status, Active, Insurance, or Cancel in Supabase. 

We always want to keep AT LEAST 50% of our Active daily sending volume in Insurance so that we can always rotate our domains. 

Every Friday, I would use a python script that would get the reply rates of all the domains in our campaigns so I could make decisions of what should be cancelled and what should be rotated from Insurance to Active. 

Once I had the numbers, spreadsheet warfare would begin. 

Making sure that every customer had enough and we didn't cancel too many domains. Numbers wouldn't look right so I'd check them manually. It was a mess.

Now, every Friday, I run this little Preview Global Rebatch and it send me a slack message with every customer, their daily send goal, how many domains are active, how many domains do we suggest to cancel, how many domains I need to buy more of, etc. 

And I hit accept and not only does it change all the statuses in our Supabase, it also pulls all of our customer's campaigns and swaps out the old domains for new domains. 

Easily my favorite workflow for deliverability. 

If you want our email deliverability SOP that goes over how we do this and how we work with Hypertide.io and Zapmail.ai to get it done, comment "primary" and I'll comment it back to you!

Here's the prompt to get you started building your own google maps scraper with Claude code or Cursor. 

One of my favorite things about LLMs and coding is that my team can build things that impact our business and then everyone can use. Bharatt Arorah built this google maps scraper for us and it's been so easy to use. 

Then when someone else on the team wants to add to it, it'd be really easy for them. 

This scraper is built on a RapidAPI endpoint that gives us 3,000,000 requests per month for $100 a month. 

Here's the prompt I'd give for Cursor or Claude Code to figure this out for you. 

First connect to the Rapid API MCP and then run this because it'll ask you to restart. (I don't use the MCP to actually run the code. I just use it to connect to the documentation to help set everything up). 

If you need the list of zip codes don't ask me or your mother for it. Ask Chat GPT literally asking "Do you know of any GitHub repositories that have all of the zip codes in the United States, or any other public ways that you can download that information easily?" and it'll come up, I promise. 

Long Text not optimized for a linkedin post AT ALL. 

Hey cursor, can you explore this Google Maps endpoint and build a plan for me that would be able to create an input where I can say a Google category, and then you'd be able to scrape it?

Here is a file of all the zip codes around the United States so that you have something to parse by. Search every one of these zip codes with my keyword to then find the companies that are in those areas. Build something into it that includes logic that, if a company is in a different business category that is excluded from my results, I want to be able to filter for the category. Let me know if we could exclude the cities and also run multiple category searches in one run. Set up the database schema to pull the zip codes from SupaBase. Literally go one by one doing a search across each of the zip codes. I also might wanna specify that I want to include certain cities, exclude certain cities, or include and exclude states in the future as well. Let's also add the filters for that to be possible as well. 

Hope that helps and provides a good lead gen project for people trying to get into coding with Cursor or Claude code.

Send those companies over to a Clay webhook to have a contact finding waterfall and you're all set!

I have to admit, six months ago I didn't get the point of MCPs.         
                                         
This post is for people who were like me. Also for my employees who are reluctant to start using these tools.                     
                                         
What is an MCP (Model Context Protocol)?                    
                                         
Think of it as a gateway for an AI agent to understand a tool.         
                                         
Instead of asking an agent to read API documentation (it will miss things),  
you connect it to an MCP. The agent figures out:                
 - What endpoints exist                             
 - What payloads to send                            
 - How to actually accomplish your task                     
                                         
Then you ask it to write the TypeScript or Python to make those API calls   
directly.                                   
                                         
Way more efficient. Way fewer tokens.                     
                                         
 My biggest tip:                                
                                         
 We're not actually doing a lot of work with MCPs as in moving data or doing work with MCP.             
                                         
We use them so agents understand the API documentation better.         
                                         
The MCP helps the agent learn the tool. Then we have Claude write actual code that calls the APIs.                                
                                         
That's the workflow:   
                           
 1. Connect MCP                                 
 2. Agent learns the tool                            
 3. Agent writes the automation code                      
 4. You run the code directly                          
                                        
 If you've been confused about MCPs, hopefully this helps.

A couple of customers have asked me what I think about this announcement from Google and here's my humble prediction. 

I'm not going to be naive and say that this won't have any affect on cold email. But also, even the video doesn't explain very well what this will do. 

I think that there is a best case scenario and a worst case scenario. 

Best case - this is an overall summarization of the email meaning that the preview text we optimize our first lines for today won't be as important as optimizing for how the summary will come across. 

A recipient can see the offer (maybe even stated cleaner than what an average copywriter can do) and choose to open or disregard. 

Worst case - it becomes a new spam filter and labels all external emails you haven't received before as spam with no preview. 

I truly think the actual outcome will be somewhere in the middle and lead magnets will become more and more important so you aren't just pitching calls and getting hung up in the spam filters. 

Truly, no one knows but it's not in my inbox yet but excited to see it when it is so we can start playing with it and see how it affects the channel.

Founder | Revenue Engine Builder | Predictable Rev Growth & Ops for B2B Tech

⏩ Your B2B tech company's growth has stalled. Your GTM strategy is an expensive, unpredictable struggle holding you back. This is a common story, but it doesn't have to be yours...👇

The Problems I Solve:
❌ High CAC from inefficient sales processes.
❌ Inconsistent pipeline causing stress in board meetings.
❌ GTM efforts that aren't a scalable, long-term asset.

How I Partner with Founders & C-Suite at $10M+ Tech Companies:
My team engineers and installs a "Hands-Off Predictable Revenue Engine" as a permanent asset for your company.

✅ We transform your GTM from a cost center into a valuable company asset.
✅ We implement a full-funnel system, from AI-powered outreach to coaching your sales team to close the high-quality leads we generate.
✅ We provide radical transparency with a real-time dashboard tracking metrics that matter: pipeline, meetings, and ROI.

You want to build a category-defining company with a GTM system as precise as your product, not gamble on another failed SDR hire or generic agency. To win, you must own your growth infrastructure. This requires:

♟️ A GTM Asset Blueprint: A strategic plan to own your pipeline.
🤖 Intelligent Automation: Using AI & Clay to create an unfair advantage.
🧬 A Revenue-Focused Partnership: An expert team to build the engine and help you run it for maximum ROI.

Here’s how we've done it for others.

"Their scientific, data-driven approach gave us control over our metrics and freed our sales team... to focus on building real connections."
- Leonardo Petrini, Head of Marketing @ LimoLane

"Matteo and his team automated our process, saving 15-20 min per lead... With reply rates of 3-10%... they brought us more revenue immediately."
- Alex Shinkarovsky, Founder @ RRA

✅ See more results at https://kinetyca.com/case-studies

✅ You deserve the confidence of a predictable revenue machine.

This is for you if you're a Founder, CEO, or Head of Sales at a $10M+ Tech Co who:

❌ Is tired of presenting unpredictable forecasts to your board.
❌ Worries competitors are scaling faster & more efficiently.
❌ Feels stuck managing processes instead of driving strategy.

And if you want to:

✅ Build a valuable, permanent asset on your balance sheet.
✅ Have a clear, predictable path to your next revenue milestone.
✅ Partner with an expert to engineer your growth.

Let's talk.

Send me a DM with "ASSET" and we can discuss your GTM blueprint.

Contact me:
Message me here on LinkedIn
Or book an appointment above

Matteo Fois

🎉We’re finally launching Kinetyca! 🚀
 
This is a long-awaited merger between TAM Acceleration and Growth Momentum – and honestly, it feels great to finally share it.
 
I met David through an agency mastermind in the Dominican Republic.
And then again at #Sculpt in San Francisco.
 
What started as casual conversations... slowly turned into something else.
 
👉 We kept thinking out loud: "how can we serve our clients better?"
 
The more we talked, the more obvious it became that our teams, our thinking, and our ambitions were naturally pulling in the same direction.
 
One project after another, working together kept making things better:
✅ Clients got more support & results
✅ Projects moved faster
✅ Decisions got sharper
 
So from our 2 agencies TAM Acceleration and Growth Momentum, we brought it together to make a Super Agency 🔥
 
Kinetyca is the result 🤝
 
We’re incredibly excited about what this unlocks – for our clients, our team, and everything that’s coming 🚀✨
 
Can’t wait to share more very soon 🎉

Founder | 👉 Building Allbound Revenue Engines | 📈 Predictable Rev Growth & Ops for B2B Companies | 🤝 Official Partners: Clay, Hubspot, Heyreach, Smartlead, La Growth Machine

Our LinkedIn stack that hit a 50% acceptance rate
 
Most LinkedIn outreach fails for one simple reason:
👉 people try to earn relevance after sending the message.
 
We flipped the order.
 
Instead of starting with cold outreach, we start with intentional visibility and only then move into conversations.
 
Here’s the exact system 👇
 
Step 1: Start with an ABM list in Clay
👉 Everything begins with a deliberate list of companies and roles we actually want to talk to.
👉 No inbound randomness. No “whoever liked a post.”
 
Step 2: Warm up targets with Extrovert
👉 Before any message is sent, we engage our ABM targets at scale.
👉 Context-aware comments in the right conversations, so our name is already familiar.
 
Step 3: Content reinforces recognition
👉 As targets see our comments, they also start seeing our content in-feed.
👉 Some engage. Some don’t. Both still increase familiarity.
 
Step 4: Enrich and qualify in Clay
👉 The ABM list is enriched and scored based on ICP fit, seniority, and relevance.
👉 If someone doesn’t qualify, they don’t get messaged.
 
Step 5: Generate the message inside Clay
👉 For qualified, warmed profiles, Clay generates a LinkedIn opener that references prior exposure or shared context.
👉 No templates. No generic openers.
 
Step 6: Send via HeyReach.io
👉 Messages are delivered natively on LinkedIn with human pacing.
 
And here’s what this gets you:
✅ ~50%+ acceptance rate
✅ Higher-quality replies
✅ Conversations that don’t start defensive
 
The flow is simple:
Relevance isn’t created in the first message.
 
It’s created before the message is ever sent.
 
That’s the difference between cold outreach and engineered conversations.

Yesterday we announced the merger. Now we’re HIRING 2 key GTM & 2 key Sales roles! 🚀
 
These roles sit at the core of what we do.
✅ Designing GTM strategy.
✅ Building the systems behind it.
✅ Owning client outcomes end to end.
 
We’re hiring:
1. GTM Engineer
2. GTM Strategist
3. SDR DM Setter
4. SDR Cold Caller
 
Baseline requirement: 2/3+ years of experience (agency / growth / lead gen preferred)
 
Skills we’re looking for:
 
GTM Engineer
- Clay power user (required)
- n8n / Make automations
- Smartlead/ HeyReach.io / Instantly.ai workflows
- Strong CRM + RevOps understanding
 
GTM Strategist
- ICP + segmentation
- Messaging + offer angles
- Outbound + ABM planning
- Comfortable with CRM, Sales Nav, Sheets
 
SDR DM Setter
- LinkedIn prospecting + Sales Nav
- Strong DM openers + qualification
- Consistent daily output + clean CRM hygiene
 
SDR Cold Caller
- Confident cold calling + objection handling
- Qualification + meeting setting
- High activity + disciplined follow-up
 
DM me if you fit (or tag someone who does).

The Shift from Outbound to GTM Systems

A few years ago, outbound meant -> cold email & cold calls.

Write good copy, get replies, call them, book meetings...

But that playbook doesn’t really work anymore. Most teams have overused the same data, the same sequences, and the same tools. 

The real advantage now comes from how your system connects everything together.

You’ve probably heard about this with terms like ‘GTM Systems’, ‘GTM Engines’ or ‘RevOps systems’

Here’s what a connected GTM system actually looks like in 2025:
→ Data: Apollo, Sales Navigator, Ocean.io, Clearbit
→ Engagement: Masterinbox.com, Extrovert
→ Signals: RB2B, Common Room
→ Orchestration: Clay
→ Outreach: Smartlead, HeyReach.io
→ Automation: Make, n8n
→ CRM: HubSpot, Attio

If your GTM still lives in isolated tools, you’ll keep seeing isolated results.

Systems are the new advantage.

20 Claygent Navigator Use Cases YOU MUST TRY in 2026
 
Clay released Navigator last year, and it quietly changed the game.
 
We've been experiment with what’s actually possible using this new agentic workflow engine inside Claygent over the last few months.
 
And what we found blew us away:
 
→ Your competitor hides their pricing behind a calculator?
Claygent clicks through it and returns the full tier list.
 
→ Your customer reference is buried in a case study library?
Claygent scrapes every name and headline outcome into your table.
 
→ Your target company posts 40 new job listings in their ATS?
Claygent goes through all of them and brings back titles, departments, and locations.
 
So we took the 20 most useful Navigator workflows that were basically impossible before and turned them into a playbook with:
 
✅ What the use case is
✅ Why it matters for GTM
✅ Tested prompts and output fields
✅ Step-by-step instructions to build it in Clay
 
If you want the full list, comment “Claygent” and make sure we’re connected.
 
I’ll send it over.

$1,000/mo GTM stack, explained as a system
 
This is not a list of tools.
It’s a step-by-step workflow where each step sets up the next.
 
1. Define target accounts
We start by clearly defining who we want to reach and why.
👉 Industry, company size, geography, and buying roles are locked first.
 
2. Source the initial contact universe
Apollo.io is used to pull verified B2B contacts that match the target profile.
👉 This gives us a broad but relevant starting list.
👉 The goal here is coverage, not perfection.
 
3. Centralize everything in one system
All accounts and contacts are pushed into Clay.
👉 Clay becomes the single workspace for enrichment, segmentation, and routing.
👉 No enrichment or outreach happens outside this system.
 
4. Enrich and clean the data
Inside Clay, records are standardized and missing fields are filled.
👉 Titles, company data, LinkedIn URLs, and firmographics are cleaned.
👉 This ensures every record is usable before outreach starts.
 
5. Segment by context, not just filters
The list is split into smaller segments based on relevance and timing.
👉 Different segments have different reasons to engage.
👉 This prevents one-message-fits-all outreach.
 
6. Decide the right channel for each segment
Each segment is assigned an outbound path.
👉 Some go to email first.
👉 Some go to LinkedIn.
👉 Some run on both in parallel.
 
7. Prepare personalized message inputs
Clay is used to generate structured context for messaging.
👉 Personalization is based on company situation and role relevance.
👉 Not surface-level or gimmicky tokens.
 
8. Launch cold email outreach
Smartlead handles email sending at scale.
👉 Multi-domain setup protects deliverability.
👉 Volume is controlled to keep the system stable long-term.
 
9. Launch LinkedIn outreach in parallel
HeyReach.io runs LinkedIn connection and follow-up flows.
👉 Multiple operators can run sequences without overlap.
👉 Messaging stays consistent across the team.
 
10. Keep the system in sync
Clay connects the stack end to end.
👉 Enrichment updates, replies, and status changes flow automatically.
👉 No manual exporting or updating.
 
11. Track pipeline and outcomes
HubSpot is the system of record.
👉 All replies, meetings, and deals are logged here.
👉 This is where performance and revenue are measured.
 
This is how a ~$1k/month stack works in practice:

clear targeting → clean data → segmented outreach → multi-channel execution → pipeline visibility.

The AI outbound engine that’s helped generate $14M+ in pipeline across our clients
 
It's built around one idea:
Outbound works best when every step is intentional and connected.
 
Here’s the system we run end to end:

 1️⃣ Define target accounts
We start by clearly identifying who we want to reach using Gemini for ICP clarity and market research.

2️⃣ Define ICP databases
The right data sources are selected to match each ICP. Databases like Apollo, ZoomInfo, Ocean.io and others. Not every source, just the right ones.

3️⃣ Push everything into one system
All leads and accounts are pushed into Clay, which acts as the central system of record.

4️⃣ Layer specialized ABM signals
Intent, hiring, funding, and buying signals are layered in using specialized ABM data sources before any outreach decisions are made.

5️⃣ Clean and enrich data
Missing fields are filled, inconsistencies are fixed, and records are standardized inside Clay.

6️⃣ Choose channels by segment
Each audience is matched to the channels that make the most sense for them. Email, LinkedIn, or both.

7️⃣ Create personalized messaging
Messages are generated using Clay workflows and AI agents, with full account and contact context.

8️⃣ Launch outreach
Campaigns are launched across channels using Smartlead, HeyReach.io, and Instantly.ai, with consistency and control.

9️⃣ Sync replies and notify sales
Responses flow back into CRM, and sales teams are notified in real time via Slack.
 
Just a deliberate outbound system where every decision is made with context, and every step feeds the next.
 
When targeting is clear, data is clean, signals arrive on time, and messaging is created with real understanding, outbound stops feeling reactive.
 
This engine is built to stay stable as volume increases, teams grow, and markets shift.

Is Clay running your GTM Engine?
 
We’ve started running most of our GTM systems through Clay.
 
It’s not a replacement for CRM or sequencers, but it’s become the brain that connects everything in between.
 
Here’s how it fits across our stack:
 
1. Market Intelligence
👉 We build our ICP and TAM maps inside Clay.
👉 Every account gets enriched with firmographics, intent, and tech data from multiple sources.
👉 We also score them based on relevance and buying signals before syncing them into the CRM.
 
2. Data Ops (CRM Hygiene)
👉 Our CRM data flows back into Clay for enrichment.
👉 We clean duplicates, match owners, and refresh missing fields automatically.
👉 Scoring and segmentation happen here before the data goes back to HubSpot or Attio
 
3. Inbound Automation
👉 All inbound leads hit Clay first.
👉 They get enriched, scored, and routed instantly to the right rep through Slack or CRM
👉 High-intent leads trigger personalized outreach sequences without delay.
Clay handles the enrichment, scoring, and decision logic.

A small orchestration layer (n8n) connects the timing between them.
 
👉 Curious to hear how others are wiring Clay into their stack. Comment your biggest Clay growth hack below!

Most GTM teams aren’t short on signals.
 
They’re drowning in them.
 
We kept seeing the same issue across teams:
- Accounts were showing intent in different places.
- Someone would visit the site.
- A company would engage with a newsletter.
- Another team would start researching competitors.
 
Individually, none of these meant much.
Together, they usually give us a lot of data about our ICP.
 
So instead of treating these as isolated activities, we built an ABM system that treats them as account-level signals.
 
Here is the step by step flow.
 
📌 Step 1: Detect account-level signals
 
We start by identifying companies that are already showing signs of interest.
 
👉 Buying intent
👉 Website visits
👉 Competitor, Ad & Newsletter engagement
  
📌 Step 2: Pull all signals into one system
 
Every detected account is pushed into Clay as the central workspace.
 
👉 One place for all signals
👉 One timeline per account
👉 No fragmented context across tools
 
Decisions only work when everything is visible in one place.
 
📌 Step 3: Qualify accounts against ICP
 
Not every active account is a good fit.
 
We qualify every account inside Clay using clear rules and blocklists.
 
👉 ICP match
👉 Company size and geography
👉 Industry relevance
👉 Existing customers or active opportunities
 
If an account does not fit, it stops here.
That is intentional.
 
📌 Step 4: Enrich qualified accounts
 
For accounts that pass qualification, we add the context needed to act.
 
👉 Company and firmographic data
👉 Relevant contacts
👉 Missing fields required for outreach / routing
 
Enrichment is purposeful, not bulk.
We only add what the next step requires.
 
📌 Step 5: Generate signal-based messaging
 
Messages are generated based on the exact signal that triggered the account.
 
👉 Different messaging for intent vs website visits
👉 Different framing for competitor research vs newsletter engagement
 
Every message is tied to something to the action.
 
📌 Step 6: Activate outreach across the right channels
 
Outreach is activated based on account value and signal strength.
 
👉 Email
👉 LinkedIn
👉 Cold calls
👉 Personalized videos
👉 Voice notes
 
This is selective and deliberate.
 
📌 Step 7: Personalised landing pages (optional)
 
For high value accounts, we sometimes add domain-level personalised landing pages.
 
👉 Used only when deal size justifies it
👉 Keeps post-click experience consistent
👉 Supports deeper ABM plays

📌 Step 8: Custom ABM plays (optional) 

When needed, we design custom plays aligned to GTM strategy.

👉 Bespoke signal combinations
👉 Creative outreach logic
👉 Tight alignment with sales motion 
 
📌 Step 9: Book meetings
 
When an account shows interest, meetings are routed automatically.
 
👉 Booking flows
👉 Sales calendars
👉 Clean handoff with context
 
📌 Step 10: Update CRM and attribution
 
Finally, everything syncs back to the CRM. 
 
Sales can see when and why a meeting took place.

All in 1 cohesive ABM signal loop!

We’re hiring a GTM Strategist 🚀
 
This is not a traditional account management role. You’ll own client growth end to end, from GTM strategy to hands on execution, building AI driven outbound and ABM systems with tools like Clay, Smartlead, and n8n.
 
We’re looking for someone who operates at the intersection of client success, GTM engineering, and revenue ops.
 
We’re looking for a builder and operator who enjoys shipping systems that actually drive meetings, pipeline, and retention.
 
You’d be a great fit if you:
✅ Think in systems, not tasks
✅ Are hands on with Clay and modern outbound tools
✅ Enjoy owning accounts like a mini CEO, from strategy to results
✅ Can turn technical wins into renewals, expansions, and happy clients
✅ Are comfortable working directly with founders, sales leaders, and RevOps teams
 
You’ll build AI driven outbound and ABM workflows, ensure CRM integrity, prove ROI, and turn strong results into long term client relationships.
 
If you love GTM, automation, and client ownership and want to build things that actually work, DM me with your portfolio!

How we built 11+ automated ABM plays for a performance marketing agency
 
This started with a simple observation.
 
They were getting signals everywhere.
Website visits. Social engagement. Hiring activity. Old CRM relationships.
 
But none of those signals were connected.
And most of them expired before anyone could act on them.
 
So the goal wasn’t to create more campaigns.
It was to design a system that could notice intent, interpret it, and respond consistently.
 
Here’s how the system came together.
 
Step 1: Capturing real signals
 
Every meaningful interaction was treated as a potential signal.
Website behavior, LinkedIn engagement, review site activity, even reappearing past contacts.
 
Not all of these mean buying intent.
But together, they tell a story worth paying attention to.
 
Step 2: Enrichment before action
 
Once a signal appeared, the account was enriched automatically.
 
👉 company context
👉 role and seniority
👉 competitors
👉 market signals
 
This happened before any outreach decision was made.
 
Step 3: Qualification, not assumptions
 
Most signals do not deserve follow up.
 
So each account was evaluated against fit and intent criteria before moving forward.
 
👉 elevant industry
👉 company size match
👉 intent strength
👉 role relevance
 
If it didn’t meet the bar, the system stopped there.
 
Step 4: Centralizing everything
 
Before outreach, the system checked one thing first.
 
👉 is this already in the CRM
👉 is there an active conversation
👉 is this a past client or closed lost account
 
Nothing moved forward without context.
 
Step 5: The centerpiece offer
 
All qualified signals pointed to one destination: the Marketing Plan 2.0.
 
A prospect enters a domain.
The system analyzes competitors, positioning, and opportunities in real time.
Results are delivered instantly, and the CRM is updated in parallel.
 
This became the anchor for every ABM play.
 
Step 6: Contextual follow up
 
Outreach was triggered only when it made sense.
 
👉 message matched the signal
👉 channel matched the account
👉 timing matched intent
 
No generic sequences. No cold guessing.
 
Step 7: Continuous execution
 
Once live, the system ran like a machine.
 
✅ 11+ ABM plays operating in parallel.
✅ Signals captured and routed automatically.
✅ Sales stepping in only when the context was already clear.
 
What you’re seeing in the diagram is not a campaign
 
It’s a signal based GTM system designed to work without constant manual intervention.

How many Clay fields are you enriching that never affect a single decision?

Most GTM teams don’t have a data problem.

They have a discipline problem.

They enrich everything Clay can pull – funding, industry, intent, traffic, firmographics. 

Assuming more data = better outbound.

But when you look closely, only a few fields actually do anything:
- Change routing
- Influence scoring
- Improve copy

The rest just sit there. Expensive. Noisy. Unused.

That’s what this Clay Enrichment Field Checklist is built to fix.

It clearly shows:
👉 Must-have fields you should always enrich
👉 Conditional fields that only matter in specific motions
👉 Fields to skip because they don’t move routing, scoring, or messaging

The takeaway isn’t “enrich less.”

It’s enrich with intent.

If your Clay table looks impressive but your GTM motion still feels blunt, this is probably the gap.

How did a LinkedIn-only outbound system generate $2.6M in qualified pipeline in 6 months for an AI-powered talent development SaaS?
 
1. Start with a clear view of the buyer
👉 Before any outreach, we defined who the system was built for.
👉 Specific company profiles, senior roles, and buying situations tied to skills development and enterprise enablement.
👉 This ensured every message was written for a real buyer, not a generic audience.
 
2. Account for how buying decisions actually happen
👉 The person replying is not always the decision-maker.
👉 We assumed conversations would often start with adjacent roles and designed targeting and messaging so it could travel internally without losing context.
 
3. Build a strong data foundation
👉 AI agents were used to enrich and qualify accounts before they entered outreach.
👉 Segmentation was done at both company and role level, using hiring signals, firmographics, and technology usage to add context to every profile.
 
4. Add intent through ABM layers
👉 Clay powered the ABM logic behind the system.
👉 Website visitors, competitor audiences, lookalike companies, and hiring-based signals were combined to prioritize accounts showing early intent rather than relying on static lists.
 
5. Use LinkedIn as a controlled execution layer
👉 LinkedIn was the only outbound channel.
👉 Multiple senders, controlled volumes, and structured connection and follow-up sequences were used to maintain deliverability while keeping activity consistent.
 
6. Measure what matters
👉 All activity flowed into internal dashboards and CRM reporting.
👉 Acceptance rates, replies, and pipeline movement were tracked together, allowing the system to evolve based on what actually influenced revenue.
 
The results didn’t come from volume or tactics.
 
They came from a single, well-structured GTM system where data, targeting, outreach, and operations were designed to work together.
 
When that alignment exists, one channel is enough to build predictable pipeline.

After 12.6M AI data points, we ended up among the top Clay users 👀
 
- Top 0.05% for data points found
- Top 0.12% of Claygent users, by treating it as infrastructure, not a feature ⚡️
 
That didn’t come from shortcuts.
 
It came from showing up every day and doing the work most teams never see ⚙️
 
295 days inside the same system.
138 tables built to solve real GTM problems.
6.5M cells created, cleaned, and actually used.
4.1M contacts surfaced and pushed downstream 📊
 
There were late nights and rebuilds when things broke 🕰️
Moments where scale exposed weak logic.

And lessons you only learn by pushing real volume through real systems.

The biggest takeaway from 2025 wasn’t growth.

It was discipline.

This recap is just the surface.

The real win was confidence in what we’ve built 💪

2026, we compound with intention 🚀

Why do many AI agents still struggle with accuracy in real-world use?
 
In most cases, it’s not a reasoning issue.
 
It’s an access issue.
 
Teams building production agents often discover this once they move beyond demos.
 
The web is dynamic, protected, and inconsistent to access at scale. JavaScript-heavy pages, rate limits, and bot protection quickly break naive “browse the web” approaches.
 
This is where tools like ZenRows tend to be used, not as an add-on, but as part of the infrastructure layer.
 
The goal isn’t scraping for its own sake. It’s creating a reliable way for agents to retrieve up-to-date web information in a format they can consistently work with.
 
Over time, this leads teams toward a fairly standard workflow.
 
1️⃣ Fetch pages reliably
👉 Agents need dependable access to web pages, including those that rely on JavaScript or apply anti-bot measures. This usually means supporting different levels of fetching, from basic requests to rendered pages, with stronger proxy infrastructure only when required.
 
2️⃣ Normalize content for machine use
👉 Raw HTML is rarely useful for agents. Pages are typically converted into clean Markdown or structured JSON so the information can be reasoned over, extracted, and reused without unnecessary noise or token overhead.
 
3️⃣ Store and reuse context
👉 Once normalized, content is stored and indexed so agents don’t need to re-fetch the same information repeatedly. This improves both reliability and efficiency.
 
4️⃣ Refresh based on how often sources change
👉 Different pages change at different rates. Production systems account for this by refreshing content based on volatility rather than crawling everything indiscriminately.
 
This approach doesn’t make agents more intelligent.
 
It makes them more dependable, which is usually what matters in practice.

Once you cross a certain scale in outbound, a few things start to matter a lot more.
 
Not copy.
Not sequencing.
Not even volume.
 
What really matters is what happens to sender reputation as you grow.
 
At low volume, almost anything works.
 
Shared infrastructure feels fine. Generic warm-ups feel “good enough.”
 
But scale changes the game.
 
When reputation is shared or rushed, trust becomes unpredictable.
 
And inbox placement doesn’t fail loudly – it degrades quietly.
 
That’s why scale shouldn’t weaken trust.
 
It should strengthen it.
 
This is where private outbound infrastructure starts to matter.

Reputation needs to be isolated first, then warmed up correctly – before scale ever enters the picture.
 
That shift is also reflected in how Smartlead now prices outbound at scale.
 
Instead of pricing around send volume, the plans are structured around what actually carries risk at scale:
 
👉 Private infrastructure to protect sender reputation
👉 Ultra-premium warm-up to build trust safely from day one
👉 A setup designed for controlled ramp-up, not rushed scale
 
These aren’t “features.”
 
They’re foundational decisions.
 
At scale, outbound isn’t priced by volume anymore.
 
It’s priced by how much infrastructure and trust you’re asking the system to carry.
 
And once outbound is working, foundations matter more than optimizations 📈

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