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

🎉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 🎉

Matteo Fois

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

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.

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.

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.

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.

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.

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.

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!

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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