LastingSales

Helping LastingSales with outbound systems and GTM strategies

Sales Advisor

Genflows Agency

GTM Ops

Frostmailer

It was actually an agency at that time worked with 6 clients.

-Delivered amazing results for 5 of them.
-UI/UX agency was the space where I failed
-Got 45 meetings for 1 client in 23 days with $720,000 in pipeline.

GTM Engineer

QuikSuite

B2b sales and marketing

Leadsify

Cold Email specialist

I am a B2B lead generation addict and GTM strategist , I specialise in identifying and engaging high-quality prospects to drive business growth. My approach combines strategic research, compelling communication, and persistent follow-up, allowing me to build meaningful connections that convert.

Key Highlights:
-Proven Success:Increased meeting bookings by 15% through optimized outreach processes and improved engagement rates in email marketing.
-Leadership Experience:Managed multi-channel B2B client acquisition strategies, achieving a 25% uplift in positive response rates and enhancing team performance by 20%.
-Data-Driven Strategies:Designed customized messaging for LinkedIn outreach, resulting in a 30% increase in lead responses and a 20% growth in conversions.

I am passionate about leveraging Sales technology and insights to unlock the full potential of businesses. Let’s connect and explore how we can collaborate to achieve your sales goals!



Founders keep calling AI a “tool.”

That’s why their results look like experiments instead of systems.
The founders winning with AI don’t treat workflows as automation.
They treat them as product.

Here’s the difference.

1. Tools add features

Product creates outcomes

When founders think in terms of tooling, they ask:
 → How do we automate this?
When founders think like product builders, they ask:
 → What outcome should this workflow create every single time?
That shift upgrades the entire GTM engine.

2. Tools automate tasks

Product engineers behavior

Automation handles:
 • enrichment
 • research
 • drafting
 • routing
 • logging

But workflow-as-product handles:
 • buyer psychology
 • timing
 • message sequencing
 • narrative framing
 • friction removal

Tools create speed.
Product creates revenue.

3. Tools operate in isolation

Product operates as a system
Losing teams add AI → a process breaks → they blame AI.

Winning teams design:
 • inputs
 • logic
 • triggers
 • feedback loops
 • observability
 • governance
 • iteration cycles
AI becomes the execution layer, not the strategy.

4. Tools reduce work

Product removes failure points
AI that saves time is helpful.
AI that removes inconsistency is transformative.

This is the difference between:
 → faster SDRs
 → predictable pipeline

Product replaces heroics with architecture.

5. Tools need users

Product creates value without being asked

The best AI systems behave like:
 • a routing engine
 • a prioritization engine
 • a scoring engine
 • a messaging engine
 • a qualification engine

Not because someone pressed a button.
Because the system runs automatically.
That’s product thinking.


If founders treat AI as a shiny feature, they get shiny results.
If they treat AI as product, they build a compounding revenue machine.

AI is not the strategy.
The workflow is.

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AI-driven GTM for B2B SaaS | GTM Engineer | 30–90 day pipeline sprints

Everyone talks about $1M in outbound pipeline.
Almost nobody can engineer it.

Here’s the truth:

A $1M outbound engine is not built with more reps.
It’s built with a 5-layer GTM system that removes human drag from every step.

I’ve built this stack.
I’ve run this engine.

And it works across SaaS, agencies, and service companies.
Here’s the architecture.

1. ICP Modeling (The Part 90% Skip)

Most teams “target broadly” because they don’t understand pattern math.
The $1M engine starts with surgical ICP clarity:
• Value bands
 • Trigger-based segmentation
 • Buyer motion patterns
 • Expected ACV ranges
 • Signal likelihood scoring
 • Message–moment mapping

If your ICP is soft, your outbound engine guarantees failure at scale.

2. Data Operations (Your Revenue Oxygen)

You can’t automate what you can’t trust.
The winning stack runs on:
 • Multi-source scraping
 • Real-time enrichment
 • Buying-committee mapping
 • Funding, hiring, intent, and tech signals
 • Data freshness pipelines
 • Automated dedupe + conflict resolution
 • 360° account graphs

Bad data kills outbound faster than bad messaging.
Your data layer is not a tool it’s infrastructure.

3. Lead Data Ops (Where the Money Actually Lives)

This is the hidden engine that most teams never build.

You need systems that:
 • Score leads based on real-time motion
 • Route accounts to channels automatically
 • Update fields dynamically as signals change
 • Trigger sequences based on timing, not guesswork
 • Auto-generate persona-specific insights

The $1M engine does not ask reps to “check Salesforce.”
It routes and activates every lead without a human touch.

4. Adaptive Messaging System (Not Templates, Logic)

High-performing outbound isn’t creative writing.
It’s context synthesis.

Your messaging layer should:
 • Pull in 1st + 3rd party signals
 • Rewrite messaging based on buyer behavior
 • Adjust tone based on persona
 • Swap CTAs based on probability
 • Compress value props into 7–12 second reads
 • Cross-check with ICP pain-modeling

The message must evolve every time the buyer moves.
Static sequences = static pipeline.

5. Omnichannel Orchestration (Email + LinkedIn + Phone + AI Agents)

Outbound at scale requires pressure from multiple touchpoints:
• Warm-up messaging on LinkedIn
 • Precision email sequences
 • Phone follow-ups triggered by intent
 • AI agents engaging instantly on inbound signals
 • Intelligent fallback logic (no dead ends)
 • Time-of-day optimization
 • Human-layer interruption when needed

This is not “multichannel.”
This is sequenced orchestration.

Every touchpoint increases the next touchpoint’s probability.

6. Conversion Layer (The Booking Engine Most Teams Never Build)

Once messaging works, the final step is where the revenue compounds:
• Instant booking pages
 • Calendar routing
 • Slack approval workflows
 • Pre-frame emails
 • SDR/AE assignment logic
 • Auto-generated research briefs
 


Build the system and the outcomes follow.

Last quarter, I ran an experiment that every outbound “expert” told me would tank our deliverability.

They were all wrong.

Not only did we increase meetings booked, we did it while sending 42% fewer 

emails.

Here’s the story:

I wanted to see what happens when AI doesn’t write the emails…
 …but manages the entire deliverability workflow like an ops engineer.

What happened shocked me.

First discovery:

Most teams get blocked not because of messaging…
 …but because their outreach looks like a bot even when written by a human.

So we flipped the model.
AI didn’t generate copy.
AI generated a risk score for every single send.
It predicted:
• Which domain was safe
 • Which alias had heat
 • Which words trigger filters
 • What time the inbox was coldest
 • Which prospect profile required a “soft open” via LinkedIn first

Outbound became risk-adjusted, not volume-adjusted.

Second discovery:

The moment we let AI orchestrate channel order —> not copy order —> replies jumped.

Email wasn’t step 1 anymore.
Sometimes it was step 4.
AI decided the sequence based on:
• Prospect’s digital exhaust
 • Domain temperature
 • Past rep-level deliverability
 • Buyer motion across our site
Humans don’t time outreach well.
AI does.

Third discovery:

When AI managed the infrastructure, not the SDR…
 our spam rate dropped to near zero.
• Domain rotation
 • Auto-throttling
 • Signature diversification
 • Real-time DNS health checks
 • Pattern smoothing across reps

No one talks about this because everyone wants “magic copy.”
Copy doesn’t save you if your system is screaming “this is automation.”


Outbound isn’t dying.
Bad infrastructure is.

AI won’t fix your messaging until it fixes your deliverability workflow first.

I used to think lead scoring was a math problem.

Turns out it’s a behavior problem…

 …and most SaaS teams are measuring the wrong behaviors entirely.

Here’s what happened:

I reviewed hundreds of “high intent” leads across three companies.
Almost none of them became pipeline.

But the leads everyone ignored?
The ones who looked cold?

They were hiding the real signal.

The breakthrough:

Prospects tell the truth in their micro-behaviors, not their form fills.

AI surfaced patterns no human SDR would ever notice:
• Scroll depth spikes on pricing
 • Re-reading the same comparison page
 • Viewing your founder’s LinkedIn before your homepage
 • Opening an email silently at 2am
 • Jumping from your site → G2 → back to your site in under 10 minutes
These weren’t “random actions.”
They were purchase anxiety loops the real gold.

Then we layered identity on top:

AI matched behavior → persona → company context.

A CFO lingering on ROI pages?
Very different from a PM scrolling feature docs.
AI understood why a user behaved a certain way…
 …not just what they clicked.
That’s when scoring accuracy exploded.

The wild part:

Once AI updated scores in real time, everything changed.
A prospect goes from “lukewarm” to “hot” in seconds:
• New visit
• New social action
• New competitive search
• New pricing click

Pipeline creation suddenly felt… predictable.

Not because reps got better.
Because signal got clearer.

Last quarter, I watched a founder lose $268,000 in pipeline because their reps trusted AI outputs more than data.

Not because AI was “bad.”
Because the inputs were chaos.

Here’s the part nobody wants to admit:

AI hallucinations aren’t an AI problem.
They’re a data governance problem.
The model is only hallucinating because your GTM engine is.

When I audited the team, here’s what I found:

3 competing ICP definitions
4 versions of the same contact record
6 enrichment tools firing simultaneously
Reps pasting prompts into ChatGPT with zero guardrails
No verification layer before email copy went live

That’s not hallucination.
That’s structural failure.

So we rebuilt everything around a simple principle:

“AI shouldn’t create truth, it should reference it.”

Here’s the system that prevented another revenue leak:

1. A Single Source of Truth (SSOT)

No exceptions.
CRM became the canonical layer.
Every enrichment, every update, every workflow wrote back to one place.
Because if AI pulls from 7 realities, it will invent an eighth.

2. A Verification Layer Before Output

We inserted a “sanity-check workflow”:
AI → Draft
Human → 10-second validation
AI → Adjust with corrections
CRM → Record changes

AI doubled rep speed.
Humans eliminated hallucination.
Perfect pairing.

3. Guardrail Prompts Tied to Data, Not Creativity
The prompt changed from:

“Write an email to this prospect.”

to:

“Using ONLY the fields in this CRM record + this verified account context, write an outbound message. If data is missing, clearly state ‘Insufficient data.’ Do NOT invent details.”

Hallucinations vanished instantly.

4. Weekly Model Drift Reviews Every Friday we checked:

Which fields were misinterpreted
Which assumptions AI repeatedly got wrong
Which reps introduced “prompt creep”
Which accounts were enriched incorrectly upstream

Fix the inputs → stabilize outputs.
Stabilize outputs → protect pipeline.


AI isn’t dangerous.
Sloppy GTM systems are.

Guard your data, and AI becomes a force multiplie, not a liability.

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