Dawid Stasiak

Acclaim

CEO & Founder

Swyply

Ever felt like the digital realm is a maze? That's where my teams and I step in. At Acclaim, we whip up websites that don’t just look good but work like a charm. Think of us as the wizards behind the curtain, turning your "umm, maybe?" into "WOW" with WordPress & Webflow.

Oh, and if you’ve ever tried finding that tech genius to boost your squad (and ended up pulling your hair out), breathe easy. Over at Swyply, we play matchmaker, ensuring you swipe right to your ideal IT soulmate, minus the awkward first dates.

In essence? We craft. We code. We connect. And throw in a few laughs along the way. Why? Because who said business can't be fun?

Ready to amp up your digital game and team power? Let’s chat. No ties. No jargon. Just digital magic and some good ol' human connection.

Driving Growth: Websites, Software Testing & Sales

I won the first-ever Clay GTM World Cup, a global go-to-market competition, and was featured in Times Square, New York recently.

Now, I’m building DFY Automation, where we help B2B companies design complete All-Bound GTM systems that bring together inbound, outbound, and CRM enrichment. Our goal is to create fully connected growth systems that generate, qualify, and convert leads while giving teams time to focus on strategy and relationships.

We combine GTM strategy, intelligent automation, and AI agents to transform how revenue teams operate. From campaign execution to RevOps, our systems handle the heavy lifting so you can scale faster with precision and control.

We also partner with businesses that want to build these capabilities in-house. Through our consultation programs, we work alongside your team to design the right architecture, train your operators, and scale sustainably.

If you’re exploring automation, AI, or GTM transformation, I’d love to connect.

📩 Service Form: https://tally.so/r/nplXGV

🤝 Consultation Form: https://tally.so/r/nPaxPQ

🤝 B2B Partnerships & Collaborations: DM

Javeria Shah

Modern GTM isn't what it used to be and I love this.

The lines between sales, marketing, and technical execution are blurred. You can't treat them like separate functions anymore.

Look, knowing how to use Cursor or write some code is good. But that's just the start. To actually scale, you need to understand system architecture, hosting, token costs. The technical choices you make today become your growth ceiling tomorrow.

The skill convergence is real. You can build a lean team where one person owns sales and strategy, another lives in data and AI engineering. Or you can have people who bridge both worlds, because learning has never been faster or more accessible.

Modern GTM isn't about briefing an agency and letting them handle it. Whether you're working with external teams or building in-house, they need complete context. Your data, insights, business model, everything. Surface-level information gets surface-level results.

And it's definitely not just outbound anymore. The intelligence you gather for prospecting powers your inbound too. Target account lists can fuel content, social, partnerships, events. That's real account-based marketing, not another cold email spray.

The real advantage is Pattern recognition. You're collecting data daily on prospects, customers, campaigns, product usage. Most companies sit on it. Modern GTM means constantly evaluating that data and acting on patterns. With today's tools, this is easier than ever.

If you're a business, find partners who understand both strategy and technical implementation. Who can build systems that compound.

If you're an agency, build GTM infrastructure/systems. 

The businesses winning right now aren't the ones with the biggest budgets. 

They're the ones where GTM, product, and data work together. Where everyone speaks enough of each other's language to move fast.

That's modern GTM in my opinion.

Clay GTM World Cup Champion 2025 🏆 | Co-Founder & CTO @ DFY Automation | All-Bound GTM & Business Process Automation

15,000 of you are here now, thanks for being a part of my journey 🙏 

When I started on LinkedIn 3 years ago, I had no clue what I was doing.

No content strategy, no playbooks, I had no idea about how to grow a social media platform.

I just shared what I was learning and experimenting with in real-time.

Somewhere along the way, LinkedIn became my best inbound lead generator. 

Clients started reaching out. Revenue started flowing. All from this platform alone.

Even after launching my business, LinkedIn continued to deliver. Every client, every deal was inbound through content.

But as we scaled, I knew we couldn't rely on inbound alone. So we built our own outbound motion. And since outbound is literally what we do for our clients as part of our GTM services, we want to become our own best case study.

But still this year is all about content-led growth.

The most important learning from last 3 years  is that showing up consistently and sharing authentically works.

Today marks a new chapter for DFY Automation - Ali Asghar, my life partner and the person who knows how I think better than anyone, is joining as Co-Founder & CEO.

What we do: 

We do two things exceptionally well.

→ All-bound GTM Engines that work
→ Business process automations across sales, support, and ops

How we do it: Lean team. Minimal tech stack. Maximum impact.

The momentum we've built so far has been incredible. But this is where it scales.

Two minds fully aligned on one vision.

Here's to building the best service agency in the space 🥂

Co-Founder & CEO @ DFY Automation | All-Bound GTM & Business Process Automation.

VP of Sales → GTM Engineer → Co-Founder & CEO. Here's the full story ↓


I've joined DFY Automation as Co-Founder & CEO.

From Vice President of Sales for almost 2 years in the US market...

To cold messaging my way into a remote GTM Engineer role at < oneaway >...

To now owning my own agency.

The journey has been anything but linear, and that's exactly what made it worth it.

Excited to scale the business with "The Clay Cup Champion 2025", my fellow Co-Founder & Wife Javeria Shah

🥂,
Ali Asghar

P.S. If you're looking to start an all-bound GTM motion or automate your business processes, let's talk.

Link to schedule a discovery call in the comments 👇

300M verified leads, refreshed weekly.

If you’ve ever had a campaign die from bounces… read this.

Prospeo.io V2 is here, and it’s impressive.

I spent the last month poking around the platform, and it’s not just “more data.”

It’s a fully searchable database + list-building workflow.

For a long time, I used Prospeo solely for enrichment.

Now you can build your entire prospect list inside it.

You’ve got the core database + workflow most teams use Apollo for,
plus 30+ filters, including technographics and intent signals.

But the bigger story is quality.

Their emails aren’t “verified once and forgotten.”
Every contact goes through triple-verification: SMTP checks, real send-outcome data from their network, and BounceBan filtering that strips out catch-alls and spam traps before they quietly burn your domain.

On the mobile side, they're doing something similar. 
Every number is triple-checked: carrier validation, person-level matching, and a call-tested database where failing numbers get removed and working numbers get reinforced.

And the data stays fresh.
The entire database refreshes on a 7-day cycle. Emails re-checked, mobiles re-scored, job changes updated, so you're not running campaigns on stale data from six months ago.

I actually tested their data in a recent campaign, and the results were great. 
I got a positive response before we hit 200 emails sent.

If you’re currently evaluating data providers, this one should be on your radar.

What do you trust most as a quality signal: bounce rate, pickup rate, or replies?

A few months ago, I got interested in context engineering after being influced by Jacob Dietle's content, and it changed how I thought about working and interacting with AI. 

Since then, I've been obsessively consuming content from the LangChain YouTube channel, absolute gems on this topic that have expanded my understanding in ways I couldn't any other way.

I've compiled all the best resources I've found into a carousel below. If you're interested in context engineering, prompt optimization, or getting better results from AI systems, these videos are worth your time (It's just part 1).

All the links are in the comments so you can easily access them.

What resources/use cases have you found most helpful for learning about context engineering? Drop them in the comments, I'm always looking for more!

Context Engineering Content Library Part 1

How are you documenting your GTM results?

I've been noticing businesses take very different approaches to tracking and documenting their go-to-market outcomes.

Some are building comprehensive dashboards that track everything from first touch to revenue. 

Others keep it simple with spreadsheets and monthly reviews.

What's particularly interesting: as more teams build with Claude Code, I'm seeing GTM documentation and collaboration increasingly happen in GitHub. Version-controlled strategies, documented experiments, collaborative iteration on what's working.

Curious what approaches you're seeing:

Where are you documenting GTM results?

What tools or workflows are working for your team?

How are you balancing simplicity with comprehensive tracking?

Engineering is in my blood. I grew up in Karachi, Pakistan 🇵🇰 watching my grandfather work at Pakistan Steel Mills, my father lead engineering departments at pharmaceutical companies, and my uncle head up the engineering team at one of the biggest textile companies in the city. So yeah, I became an engineer too, majored in Electronics. I grew up observing businesses in different sectors.

But there’s something blowing my mind lately: the entrepreneurial wave happening in Karachi right now is insane.

I stumbled upon this video featuring founders like Maha Shahzad (BusCaro), Rabeel Warraich  (SarmayaCar), Fariel Salahuddin  (UpTrade), Muneeb Maayr منیب مائر (Bykea , ex-cofounder of Daraz which was acquired by Alibaba), Haider Raza (DealCart), and Saim Siddiqui (Procheck) talking about their journeys.

I felt I need to share it. The stuff these people are building, the problems they’re solving, it’s incredible.

There’s a line at the end of the video that really stuck with me: this city is both broken and booming, fragile and ambitious. And that’s exactly it. I don’t live in Karachi anymore, but I really want to see this city thrive and get better. I’ll be visiting whenever I can and will cherish this city forever.

If you want to see what’s actually happening in Karachi’s startup scene, check out the video in the comments. Really worth watching.

If you are a Karachi based founder, please share your journey and your work in the comments ✨

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Python can improve your data ops. Let me explain.

I've watched companies spend $50K on intent data, hire SDR teams, and build entire outreach sequences, only to have everything collapse because 40% of their data is garbage.

Duplicate records. Phone numbers that route to dead lines. Emails that bounce before your message even lands.

The bottleneck isn't your messaging or your ICP. It's the unglamorous work nobody wants to talk about: data cleanup.

Most GTM teams are still operating like it's 2015.

Find-and-replace in Google Sheets. Manual deduping. Copy-pasting between systems. Spending 10 hours a week on work that a script could handle in 90 seconds.

It doesn't scale. And it's burning out your best operators on tasks that shouldn't require human judgment.

The GTM engineers winning right now? They write code.

Not full-stack engineering. Just enough Python to eliminate data bottlenecks and move faster than their competition.

Here's the actual stack I use:

pandas is the workhorse, but most people only scratch the surface. I'm using .drop_duplicates() with subset parameters to dedupe on specific field combinations (email + domain, not just email). .fillna() and .replace() to standardize empty fields across sources. .merge() to enrich lead lists with account data without VLOOKUP nightmares. And .str.strip().str.lower() chains to normalize text before any matching logic runs.

fuzzywuzzy (now thefuzz) solves the "is this the same company?" problem that kills data accuracy. When you're trying to match "Salesforce, Inc." against "Salesforce" against "salesforce.com"—fuzz.token_sort_ratio() catches what exact string matching completely misses.

phonenumbers is criminally underused. It's not just validation—parse() and format_number() let you take "+1 (555) 123-4567" and "555.123.4567" and "15551234567" and standardize everything to E.164 format. Critical when you're feeding numbers into dialers or SMS platforms.

requests + ratelimit for API enrichment without getting blocked. I'm constantly pulling from data sources, or internal APIs to fill in missing fields. The ratelimit decorator prevents me from hammering endpoints. Wrap everything in try/except blocks and you've got resilient enrichment that doesn't fail on bad API responses or rate limits.

The setup: Claude Code + Cursor.

I don't write these scripts from scratch anymore.

Claude Code writes it. When something breaks maybe the CSV has weird encoding, a column name changed, or there's an edge case I didn't anticipate. Claude debugs it in real-time.

And the script is reusable across every campaign, every week, every quarter.




If you're looking to build your GTM engine this year, DM me.

Had a conversation with an AI engineer friend today, and we got into what actually falls under "context engineering" and how it all connects. I thought about sharing some of it here.

Here's what context engineering actually includes:

RAG - Your AI retrieves relevant information from your knowledge base. Past campaigns, customer data, product specs pulled in exactly when needed.

Memory - Store things like your ICP, brand voice, positioning. So your AI doesn't ask the same questions every time.

Prompt Engineering - How you structure your instructions completely changes the output.

State & History - Keep track of where deals are, what's been done, what stage prospects are at.

Structured Outputs - Get responses formatted to plug directly into your CRM, email tools, or dashboards.

Now another question is:

adding .md files to Cursor, isn't that the same as RAG?

When you add files to Cursor, you're manually putting them in the context. AI reads all of them every time. It's static.

RAG is dynamic. It searches through thousands of documents and retrieves only what's relevant to your specific question.

The real difference for GTM:

Imagine building a sales assistant.

With static files, you might add: brand guidelines, ICP definition, product specs. 

But you can't add 500 past sales calls or 1000 customer profiles.

With RAG, when your rep asks "How do I approach fintech companies?" the system searches everything and retrieves only the relevant fintech conversations, wins, and plays.

That's the power of context engineering. It's not just about giving AI information.

It's about giving it the RIGHT information at the RIGHT time.

Now think where in your GTM system could this kind of dynamic context make the biggest difference?




P.S. If you're looking to elevate your GTM systems or document them to work smarter with AI, DM me. Happy to chat about what's possible.

I just collaborated with my former classmate and now AI expert, Tauqeer Ali Khan to launch an AI series on YT.

Tauqeer and I studied Electronics Engineering together at NED University of Engineering and Technology. After graduation, he pursued his Master's in AI at the Teesside University while I became a GTM & Automation Engineer and  built DFY Automation. Now, we've reconnected to share our combined expertise with business leaders navigating the AI landscape.

Episode 1 is now LIVE! 

In this episode, we break down: 

✅ AI Terminologies in simple language 
✅ Why data quality matters for AI success 
✅ Building reliable data collection mechanisms 
✅ Practical AI implementation pathways

This series is designed specifically for entrepreneurs, business leaders, and decision-makers who want to understand and implement AI without the technical jargon.

Why we created this: We've seen too many businesses struggle with AI adoption - not because the technology is lacking, but because the fundamentals aren't clearly explained. We're changing that.

What's Next: This is just Episode 1 of an ongoing series. We'll be diving deep into real-world AI applications, implementation strategies, and practical frameworks you can apply to your business.

🔗 Watch Episode 1: Link in comments ⤵️

💬 What AI challenges are you facing in your organization? 

Drop them in the comments - we might cover them in upcoming episodes!

We're bringing Clay's complete research infrastructure to Claude, powered by Anthropic 🤝

You can now run end-to-end prospecting without leaving your Claude chat.
Here's what this unlocks:

1️⃣ Find ICP contacts with verified emails and phone numbers
Ask Claude to surface VP- and Director-level contacts at target accounts who joined in the last 6 months

2️⃣ Run complete account research in one prompt
Pull leadership changes, hiring signals, exec perspectives, and tech stack data from a single question

3️⃣ Draft personalized outreach based on real insights
Reference specific product launches, public commentary, and company priorities in your messaging

4️⃣ Map entire buying centers
Identify economic buyers, technical evaluators, and end users across complex org structures

5️⃣ Multi-thread with stakeholder intelligence
Get verified contact details and individual priorities for everyone involved in the deal

In simple terms, just go to Claude and try asking:
“Find the head of GTM at [payments company X]."
"Now pull their recent thought leadership and verified email."
"Draft a personalized email based on what we learned."

The Clay connector is now live in Claude. Proud to be joining Claude’s connectors ecosystem as one of the first tools built specifically for go-to-market workflows.

Read more below to try it out! We loaded up free Clay credits for everyone who connects 😉.

Why Most GTM Strategies Fail: The Lukewarm Tea Problem ☕

I have been going through Wharton's marketing course lately, and there's this one concept that I would like to share.

The analogy - Mix hot tea with iced tea, and you get lukewarm tea (I can imagine it would taste awful). Nobody wants it.

That's exactly what happens when we try to build products and GTM strategies for "everyone."

What actually works is "the STP Framework":

Most of us want to jump straight into execution mode. 

But here's the thing:

→ Segmentation: Actually map out who your customers are and what they care about → Targeting: Pick the segment where you can genuinely win → Positioning: Build everything around solving THEIR specific problem

Why this matters:

The course used roof tiles as an example.

One group of buyers obsesses over durability and how the tiles look. Price is not their main concern.

Another group just wants the cheapest option. Don't really care if it lasts forever.

Now imagine you try to satisfy both. You'd build something "pretty good" at a "reasonable" price. And what happens? Both groups pass on you.

The GTM lesson:

Stop trying to be everything to everyone. 

Instead:
Find the segment with the most painful problem you can solve
Make sure you can actually win there (competitors matter)
Go deep on solving for them specifically

The best GTM strategies aren't about reaching the maximum number of people. They're about being the obvious choice for the right people.

Just saw that Icypeas dropped their reverse email lookup feature, and I think this could be useful for a lot of workflows.

The numbers are pretty impressive - 76.6% match rate. I have been thinking about how many different ways you could actually use this.

Here are 4 scenarios where I think this could be really valuable:

1. Recruiting passive candidates You have email addresses from past applicants or referrals but no LinkedIn connection. Match them to see their current role, recent activity, and whether they'd be open to new opportunities now.

2. Partnership outreach that actually works Found emails of people at companies you want to partner with through scraped data or mutual contacts. Get their LinkedIn to understand their actual role, interests, and mutual connections before reaching out.

3. Turning support tickets into upsell opportunities Your support team has emails from users asking questions. Match them to LinkedIn to see their seniority level and company size - high-level person at a big company asking basic questions? Potential enterprise upsell.

4. Cleaning up your CRM with actual profile data Those thousands of contacts with just emails and outdated info, bulk enrich them to see who's still at the same company, who got promoted, and who's actually worth your sales team's time.

The speed is wild too, apparently under a second per lookup and can process 2,000 emails in about 10 seconds.

Only pays credits when it finds a match, which seems fair.

Anyone else already testing this out? Would love to hear what you're using it for.

taplio

The Battle

Dawid Stasiak

Javeria Shah

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taplio

The Battle

Dawid Stasiak

Javeria Shah

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Javeria Shah's LinkedIn Performance

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