Christ First | AE @Airtable | Host - MSP Revenue Secrets

Rhondre Giscombe

I've been studying GTM engineering workflows in my free time.

Across the stack, I'm seeing three layers:

And across the stack it’s Three Layers.

Layer 1: Data
Your CRM. Data warehouse. Product usage data.
The foundation every software company has.
Mostly accessed by Data, Engineering, and some GTMEs.

Layer 2: Intelligence
Where orchestration happens.
Accounts get enriched, scored, routed.
Messaging gets prepped. Emails drafted.
This is where RevOps, Marketing Ops, Sales Ops, and GTMEs live.

Layer 3: Engagement
Where reps actually work.
Most salestech is fighting to own this layer.

I use five tools daily: Airtable, LinkedIn, Outreach, Claude, Salesforce.
Trying to get it down to three. Ideally one.

Here's the thing: the better my intelligence layer works, the less time I waste figuring out who to talk to and what to say.

Intelligence feeds me the signal. I send smarter outreach. That becomes pipeline. That closes.

Am I thinking about this right? What am I missing?

Would love to hear from folks building or operating in this space.

brendan short Brandon Smith Chris Balestras Joe Rhew

Disciple of Christ | GTM @Airtable | Building apps & agents for Operators

I've been trying to understand how Claude Code impacts GTM work. Still early in my research, but one thing keeps standing out.

The compounding intelligence piece.

From what I'm seeing, it's not just about build speed. It's about how context accumulates over time.

Feed it call transcripts and it updates your ICP definition. Feed it more data and it refines what you thought was true. It challenges your assumptions and suggests new angles you might have missed. The system doesn't just store information. It iterates on what you know.

A few other initial observations:

1️⃣ The build speed is real. Clay tables, n8n workflows, JSON configs all ship noticeably faster.

2️⃣ Every Claude Code project pulls context from markdown files. ICP definition, messaging framework, competitor intel. Modularizing your GTM knowledge this way should make outputs more accurate and consistent across projects.

It's designed to compound intelligence, not just automate tasks. The system learns your business over time and gets smarter with every input you feed it.

3️⃣ It's not as "non-technical" as advertised. You still need to understand APIs, JSON structure, and how to set up proper context. There's a learning curve if you haven't touched these concepts before.

Would love feedback if I'm heading in the right direction here.

Matteo Tittarelli ⚡️ Jordan Crawford Jacob Dietle Joe Rhew Taimoor Tariq Javeria Shah

Over the next 4 weeks, I'm building an AI-powered call brief system in Airtable.

Not because I need another project. Because I used to spend 15 minutes before every call scrambling to remember what matters.

Here's what my prep used to look like:

Open the CRM to check deal history. Fire up Perplexity for recent company news. Review Gong history for previous calls. Check product usage data. Skim LinkedIn for the contact's background. Then tie together the core questions I should ask.

Every. Single. Time.

I built a system that does this for me. One hour before any scheduled call, an automation kicks off. It pulls everything I need, synthesizes it with AI, and drops a call brief in Slack with a link to an Airtable interface.

I already use a version of this, but I'm rebuilding it in public so you can see how this actually works in Airtable.

Quick note on scope: This leans into what Airtable does well: centralizing data from different sources. Company news, CRM deal history, Gong call summaries, product usage signals, all synthesized through AI. If you need third-party intent signals (job changes, website visits, LinkedIn engagement), other platforms handle that better.

Before I touch a single automation or field, I need to answer three questions. Skip this, and you'll build something you don't actually use.

⚫️ What's the desired outcome?
✔️ A call brief one hour before every first discovery call. Not raw data. A synthesized view: What's happening at this company? What's our history? How are they using the product? What should I focus on in this conversation?

⚫️ What's required to make this work?
✔️Company news (AI field agents or Perplexity) 
✔️ Deal history (Salesforce) 
✔️ Gong call summaries 
✔️ Product usage data 
✔️ Contact context (LinkedIn) 
✔️ Core discovery questions 
✔️ AI synthesis on what to focus on

⚫️ What does the workflow look like?
✔️ Trigger: Calendar event detected
✔️ Process: Automation pulls data, AI synthesizes the brief
✔️ Output: Slack notification with link to Airtable interface

I open the interface, scan the brief, and show up prepared.

Once I have clarity on those three, then I get into the fun stuff: table structure, automations, integrations, and the interface layer.

Next week: Starting the build. I'll show you the data model and first automation.

Most Airtable users don't know you can enrich LinkedIn profiles natively.

Here's how:

Use Airtable Field Agents to pull LinkedIn profiles as long as you have their Full Name and Company Name.

Back in November, one of my customers was manually finding LinkedIn URLs for 3,000 contacts. Every time a new prospect was logged into their system, they'd pull the profile by hand.

We built with an Airtable Field Agent and ran it across all 3,000 records in 5 minutes.

Coverage landed around 60-70%. Accuracy on matches was about 80%.

Not perfect. But good enough for their use case.

Now every new contact logged into Airtable automatically pulls their LinkedIn profile.

I'll drop the exact prompt in the comments.

Call sentiment alone doesn't tell you if a customer is at risk. 

You need the full picture. 

Airtable integrates with Gong so RevOps teams can cross-reference call transcripts with product usage, CRM health scores, and support ticket trends in one place. 

→ Spot sentiment shifts in calls 
→ Layer in product adoption signals 
→ Surface real risk before it shows up in churn reports 

Complete customer intelligence in one system. 

Check it out today.

I'm going through HubSpot's RevOps Academy in my free time.

As I'm learning more about GTM engineering, I realized I needed deeper RevOps knowledge to understand how revenue operations actually work across the business.

Here's what's landing so far:

1. RevOps makes growth exponential by improving operational efficiency across the business

It's not just sales efficiency. It's marketing, customer success, product, everything that touches revenue.

2. Flywheel model beats funnel model

Customers don't disappear after they buy. They fuel growth if your systems are set up right.

3. Force and friction benefit the organization, even when it doesn't feel like it

The right friction in the right place keeps bad data and bad process from spreading.

4. You need the right number of leads as you scale, not just more
Volume without qualification breaks everything downstream.

5. The buyer's journey: Awareness (problem aware), Consideration (solution aware), Decision (provider aware)

Targeting certification by late Jan / mid Feb.

That said, what else should I be paying attention to? Any core lessons or principles I need to add?

Brandon Smith Jeff Ignacio Haris Odobasic Stefan Mersch 💭 Sebastien van Heyningen

I've been building AI-native apps on Airtable, Lovable, and Replit.

Here are 9 lessons that changed how I approach rapid app development:

1️⃣ Build in layers, not one-shot builds
Nail one component, then move to the next. It's easier to fine-tune and catch issues early.

2️⃣ Clarity of outcome is essential
AI platforms need clear targets. Vague inputs get vague outputs.

3️⃣ Edge cases require knowledge, not just prompts
AI handles standard paths well. Edge cases need platform expertise to close the gap.

4️⃣ Platform flexibility is the real unlock
Airtable lets you reshape your data model mid-build. That flexibility is the foundation of rapid app development.

5️⃣ AI is core to how these platforms work
It's not tacked on, it's foundational to what's possible and how fast you can move.

6️⃣ Advanced functions require platform familiarity
Formulas, automations, and integrations aren't plug-and-play. Expertise accelerates shipping.

7️⃣ Building happens in minutes. Production-grade takes weeks.
The first version is fast. Making it reliable and scalable is where the real work is.

8️⃣ Operators can build without waiting on IT
Quick wins in weeks vs waiting quarters for engineering to pick up your ticket.

9️⃣ The best builds are simple
The apps that stick solve one problem really well.

Rapid app development still requires expertise; it just shifted where it matters. But platforms like Airtable, Lovable, and Replit are unlocking what operators can build on their own.

Build with Airtable today. Link in the comments.

I asked AI to build a competitor to SpaceX.

It came back with a full competitive analysis deck in 20 minutes.

We just launched Superagent, a whole new Airtable product.

I gave it one prompt: "Create a competitive analysis deck of the commercial space market and opportunities for differentiation."

Multiple specialized AI agents deployed. Each tackled a different piece: market analysis, competitive positioning, timeline synthesis, regulatory requirements, technology comparisons.

20 minutes later: rich visuals, interactive charts, full citations. Ready to present.
This isn't another AI summary tool. It's a research and design team working in tandem.

If your team is blocked on competitive analysis or market research that requires real depth, give Superagent a try: bit.ly/3ZC0m40

Most AI roadmaps in 2025 started with the wrong question.

They asked "What can AI do?" instead of "Where are my teams spending time on work that doesn't require a human?"

With AI adoption moving past experimentation in 2026, companies are deploying actual use cases and getting tremendous gains.

So let's look at where AI is getting deployed:

1️⃣ Summarization & Synthesis 
Product teams are condensing product feedback from support tickets, sales calls, and user interviews into actionable themes that inform roadmap prioritization.

2️⃣ Classification & Prioritization 
Product Operations teams are ranking feature requests by revenue association so teams build what impacts the bottom line first.

3️⃣ Personalization at Scale 
Marketing teams at retail companies are generating email copy drafts across multiple campaigns, each one tailored to customer segments and purchase history.

4️⃣ Workflow Automation 
Marketing Operations teams are turning campaign performance data from static reports into automated budget reallocation recommendations for next quarter while there's still time to act on it.

5️⃣ Data Enrichment 
Revenue Operations teams are finding LinkedIn URLs and contact data for prospect lists in minutes instead of hours of manual research.

6️⃣ Generation & Content Creation 
Marketing teams are creating localized campaign content across 12 regions while maintaining brand voice and compliance standards.

If you're building an AI agenda in 2026, start with the workflows you already have. The best AI implementations don't require new systems. They make your existing ones smarter.

Harvard studied 100,000 AI agent users. The pattern is clear: AI that's siloed in chat gets abandoned. AI that's integrated into actual workflows gets stickier.

The reason?

Context engineering.

Most companies buy AI tools that can't touch their CRM, their Google Drive, or their project tracker. Then they wonder why adoption stalls.

Here's the thing: AI's real value isn't answering questions. It's completing tasks. But to complete tasks well, it needs the right information at the right time. When AI is disconnected from your tools, you're getting 10% of its potential. You're stuck with a Q&A bot, not a system that can actually execute.

The breakthrough happens when AI can pull context from across your tools, take action based on that context, and learn as it completes more tasks.

This is why platforms that integrate deeply with Google Drive, email, and project management tools see higher stickiness. Users don't just ask questions. They complete workflows. The AI has context, so it can act. And as it acts more, it gets better.

When evaluating AI tools, ask yourself: Can it access the context it needs? Can it take action, or just suggest it?

Context plus action equals 10x the impact of chat alone.

(Read Will Sullivan’s full breakdown of the Harvard study, link in comments)

Week 1 of building the call brief system. If I mess up the data model now, I'll be fighting this thing for months.

And the whole point of this is to stop scrambling before calls, so I need the data to flow cleanly from the start.

I'm building seven core tables:
1️⃣ Calls (the main hub): Tracks every scheduled call. Links to Call Briefs, Contacts, and Accounts.
2️⃣ Call Briefs (the generated output): Stores the synthesized intelligence for each call. One brief per call. This is what I actually read before the call.
3️⃣ Accounts: Company-level data. Links to Deals, Product Usage, and Company Intelligence.
4️⃣ Contacts: Individual people I'm talking to. Linked to Accounts and Calls.
5️⃣ Deals: CRM history. Deal stage, notes, last activity. Linked to Accounts.
6️⃣ Product Usage: For existing customers. Bases, users, activity trends, automations created. Linked to Accounts.
7️⃣ Company Intelligence: AI-pulled company news, strategic priorities, recent developments. Linked to Accounts.

Each table does one thing. Calls are for tracking the event. Call Briefs store what I actually need to know. Accounts are where all the context lives.

Calls and Call Briefs are linked one to one, so every call gets exactly one brief. There's no duplicates or confusion on which brief belongs to which call.

Linked records mean when I update deal history in Salesforce, it flows through to the brief automatically. When product usage data refreshes, the brief reflects it. I'm not copying and pasting data across tables.

The Accounts table is the hub. Everything connects through it. Company Intelligence, Product Usage, Deals, all linked to Accounts. So when I pull a call brief, it knows which account I'm calling and it pulls everything associated with that account.

I tested this with a fake call record. Linked it to a contact and an account. Added some deal history. Data flows where it should.

Next week: Building the first automation. Can I actually pull company news and deal history on a schedule?

If your tables don't talk to each other cleanly, your automations will break. And you'll be right back to scrambling before every call.

Built this entire base structure with Airtable's Omni in 10 minutes. The hard part isn't building the tables anymore. It's knowing how they should connect.

Most AI projects fail midway through.

Not because the tech doesn't work. Because the scope was never clear.

"Measure twice, cut once" was a principle from traditional trades. Carpenters and tailors used it to avoid irreversible errors and wasted materials.

Today's builders are AI engineers, FDEs, GTM operators, and non-technical people shipping apps and agents.

AI collapsed build time from months to hours. But it also means you can build the wrong thing really fast.

For AI builders, it's "Scope twice, build once."

Before you commit to a critical build, scope what you're actually trying to solve:

⚫️ The Workflow 
Not "automate project management."
But, "Our team spends 2 hours every Monday syncing status across HubSpot, Sheets, and Slack."

⚫️ The Impact
Not "save time."
But, "Cut sync to 15 minutes. Give the team 1 hour 45 minutes back."

⚫️ The Capabilities
What does "done" look like? Data sync? Auto-updates? Notifications?

⚫️ Time to Value 
Days or weeks? If it's 6 months, maybe it's not the right first project.

⚫️ The Initiative it Accelerates
Does this help you hit Q1 revenue targets? Reduce operational costs by 20%? Scale without adding headcount?

Tie it to a real OKR or strategic goal. Otherwise, you're just building because you can.

⚫️ The Phases
V1: Automate the sync. V2: Add notifications. V3: Build the dashboard. 
Don't solve everything at once.

Bad scoping: "Let's build an AI agent for project management."

Good scoping: "Our PM updates 3 systems manually every Monday. That's 2 hours. Automate the sync, give them their Monday back, help us hit our Q1 efficiency goal."

The worst thing teams do? Build fast because they can. Excitement takes over. Then they realize midway they're solving the wrong problem.

Resources wasted. Timelines blown. Sometimes people lose their jobs over a bad scope.

Clarity beats excitement.

Scope it twice. Then build it once.

Most teams are deploying AI without asking the right question.

It’s not “can we automate this?” 
It’s “what are we willing to lose?”

I’ve been building with AI for months now, across customers I consult with at Airtable and my own projects. And I keep seeing the same pattern.

AI is incredible for volume and speed. If you need 500 product descriptions written, or you’re generating e-commerce images at scale, it’s a no-brainer. The output is good enough, and you get it fast.

But the moment you need something that reflects your brand, your specific visual identity, your tone, your creative direction, AI falls short.

I’ve tested this with podcast graphics, brand assets, creative campaigns. The output is generic. It doesn’t carry the theme. It doesn’t have taste.

Here’s the tradeoff most people don’t think through. AI gives you efficiency, but it costs you originality. It gives you speed, but it costs you brand equity.

And that’s fine if you’re optimizing for volume. 
But if what you’re building is brand-critical, you can’t afford that loss.

Before you automate something, ask yourself: 
- What am I optimizing for? 
- Speed or originality? 
- What’s the tradeoff?
- What do I gain, and what do I lose?
- Can I live with that loss?

Zoom in on the task. Zoom out on the impact. 

Then decide what you’re willing to sacrifice.

Not everything should be automated. Some things still need a human in the loop.

Prob one of the best decisions I made in 2025. 

Switched from Apple Podcasts to Snipd. 

I listen to 10-15 hours of podcasts a week across faith, mindset, leadership, AI in business, RevOps/GTME. 

Snipd lets me capture insights as I go. Nothing disappears into the ether. 

Check it out if you aren't using it already.

Over the past year and a half, I’ve had the privilege to work alongside Alex. 

Among other things, three things stand out:

1 - He’s a leader that is in the trenches with you

2 - He’s a leader that challenges growing in your skillset

3 - He’s a leader that cares not just for your performance but, you personally

Joining his team is easily one of the best decisions I’ve made since, moving to ATX.

Apply today 👊🏾

P.S. You’re welcome to hmu too

Commercial Sales Leader @ Airtable | AI + Automation ⚡️

My team is growing! 🚀 I’m looking for a seller with natural curiosity, a strong desire to learn, and the drive to make a real impact. This is a hybrid role for someone who enjoys winning new business and thoughtfully expanding existing accounts over time.

You’ll partner closely with customers as they scale, helping them unlock more value with Airtable through strategic, long-term growth.

Airtable is in a pivotal phase as we build toward IPO, and this role offers real ownership and the chance to help shape what’s next.

📍 Austin (Hybrid)

If this sounds like you (or someone you know), I’d love to connect. Feel free to send me a DM.

Account Executive, Commercial Accounts  

How many internal tools is your team building that you could've bought off the shelf?

And what customer value did you trade for that distraction?

Kyle Norton talked about this on his podcast with Everett from Clay. He broke down a framework on GTM Engineering that clicked for me:

1️⃣ Buy is about 
→ Speed 
→ Broad organizational impact 
→ Resources required to support it

2️⃣ Build is about 
→ Fast execution 
→ Low org impact 
→ Or high enough strategic value to justify dedicated resources

Most things? Buy them. Very few things should you actually build.

If you're a sales tech company building an internal HR tool, you're spending engineering cycles on something Deel or Rippling already solved. That's a distraction, not a strategy.

HubSpot CTO Dharmesh Shah put it clearly: "Spend every calorie possible on creating value for your customers."

Build only when it's so unique to your workflows that it becomes a strategic asset. Something no off-the-shelf tool can do.

Everything else? Buy it and move on.

Stop writing better prompts. Start building better context. 

Most teams think the problem is their prompts. They're tweaking inputs and refining instructions, hoping AI gets smarter. It doesn't work that way. 

AI gets smarter when it has better context to work from. 

The problem is your data lives everywhere. Campaign performance in one tool. Customer intel in another. Product catalog somewhere else. 

When you ask AI to help, it's guessing because it can't see what worked last quarter or connect your product data to your customer data to your performance data. 

Every project starts from scratch and every output feels generic. 

Airtable changes this because it acts as the context layer. When everything lives in one system, AI has the full picture with relational data, linked records, and historical performance tied to real-time inputs. 

I built a demo for a fashion brand that was spending weeks aligning on every creative brief. Past performance was buried, brand guidelines lived in someone's head, and competitor research was scattered across tools. 

We moved everything into Airtable as their single source of truth. 

Now when someone starts a brief, they input the basics and the system pulls the rest: past campaigns, personas, product data, and competitive signals. 
AI drafts using what actually happened before instead of generic best practices.

Performance feeds back after launch, so the next brief is smarter because of it. 

The operators seeing results from AI aren't writing better prompts. They consolidated first and stopped fighting tool sprawl by building on one foundation. 

AI doesn't improve because the model changed. It improves because your context got better, and your context gets better when it lives in one place.

One of the features I've been pushing to every customer I work with.

With the latest improvements Airtable just shipped, there's never been a better time to explore what's possible with custom elements.

Network graphs. 3D models. Custom dashboards. If you can imagine it, you can probably build it.

Check out what builders are creating…

Introducing Built with Omni: a new showcase for builders to share bespoke AI apps 🚀

Get inspired: Browse bespoke elements created by Airtable builders.

Learn: Inspect real code and prompts to uplevel your app building skills.

Contribute your creations: Share your own apps and interfaces for others to learn from.

Plus, we launched a few key improvements to the custom elements experience:
✔️ Code inspection
✔️ Public interface support
✔️ Multi-developer support

Dive into Built with Omni →   https://lnkd.in/gDY2Kdij

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 ✨

No alternative text description for this image

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

Rhondre Giscombe

Javeria Shah

Followers

Posts

Engagements

Likes

Performance ScoreRhondre Giscombe's LinkedIn Performance

Performance ScoreJaveria Shah's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Rhondre Giscombe

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);}Rhondre Giscombe's LinkedIn Performance

Performance ScoreJaveria Shah's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreRhondre Giscombe's LinkedIn Performance