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

Most post-PMF B2B companies sit on valuable first-party and third-party data they're not using. CRM records, product usage, public business signals—all dormant while competitors turn the same data into advantages.

I build three layers:
→ Data Foundation - Structure and enrich your data with agentic custom research
→ Systems Infrastructure - Build the right GTM stack that scales
→ GTM Plays - Deploy proven campaigns that generate pipeline

What makes this work:

I sit at the rare intersection of deep RevOps expertise (10+ years, tier-1 VC/PE backed companies, three M&As), cutting-edge GTM engineering (AI-first since late 2023), and community involvement

Taimoor Tariq

Last week was our first full week using Claude Code as GTM Base's operating system.

Biggest realization? It feels like having a high-agency team member who's just a really good problem solver. Not a tool you prompt. More like a colleague you work through problems with.

Robert Jett and I had a bunch of "holy shit" moments. Here are the ones that stood out:

1. 𝗥𝗲𝘃𝗲𝗿𝘀𝗲-𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗲𝗱 𝗚𝗿𝗮𝗻𝗼𝗹𝗮 We love Granola for meeting notes but there's no API or MCP. No way to automatically pull transcripts into our workflows. When we explained this to Claude Code, it figured out the transcripts live in a local cache and wrote a script to extract them. Who needs an API?|

𝟮. 𝗦𝗲𝗹𝗳-𝗮𝘀𝘀𝗶𝗴𝗻𝗲𝗱 𝘁𝗮𝘀𝗸𝘀 𝗶𝗻 𝗟𝗶𝗻𝗲𝗮𝗿 After a client call, we had a working session to map out next steps. It suggested an action plan, created the tickets, and assigned some to itself.

𝟯. 𝗻𝟴𝗻 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗮𝗿𝗲 𝟭𝟬𝘅 𝗳𝗮𝘀𝘁𝗲𝗿 𝗻𝗼𝘄 Claude has context from all our previous work, so it gets workflows 90% right first try. I attempted this 6 months ago with ChatGPT and Claude. Nowhere close.

𝟰. 𝗧𝗿𝗮𝗶𝗻𝗲𝗱 𝗶𝘁𝘀𝗲𝗹𝗳 𝗼𝗻 𝗖𝗹𝗮𝘆 I sent it through Clay University and the docs, and had it build itself a skills.md file. I asked if it could get HubSpot certified too. It refused to watch the videos and just read the docs. Fair.

𝟱. 𝗤𝗔'𝗱 𝗮𝗻𝗱 𝗳𝗶𝘅𝗲𝗱 𝗮 𝗖𝗹𝗮𝘆 𝘁𝗮𝗯𝗹𝗲 Classification table had issues. Claude Code found the root cause in the Claygent outputs, then wrote a script to correct the data directly. No need to re-run the agent and burn through credits.

𝟲. 𝗕𝗲𝗰𝗮𝗺𝗲 𝗼𝘂𝗿 𝗖𝗵𝗶𝗲𝗳 𝗼𝗳 𝗦𝘁𝗮𝗳𝗳 Went through Slack and Google Docs via browser, flagged what needed my attention, mapped out what info we had vs what was missing.

𝟳. 𝗠𝗶𝗴𝗿𝗮𝘁𝗲𝗱 𝗼𝘂𝗿 𝗖𝗹𝗮𝘂𝗱𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗚𝗶𝘁𝗛𝘂𝗯 We're moving off Claude Teams, so it pulled all the context from our projects, sorted it, and uploaded to the right repo folders. 

𝟴. 𝗔𝘂𝗱𝗶𝘁𝗲𝗱 𝗮 𝗻𝗲𝘄 𝗰𝗹𝗶𝗲𝗻𝘁'𝘀 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 before an onboarding call
We were given some documentation, access to Clay, and the collective learnings from our past clients. Using table summaries generated by Sculptor, it did a full audit of an entire flow and created notes to discuss in our call.

𝟵. 𝗕𝗲𝗰𝗮𝗺𝗲 𝗼𝘂𝗿 𝘀𝗶𝗱𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 We always have ideas we want to test. Claude Code has compressed the idea-to-MVP cycle so much that we can validate something in an hour instead of a weekend.

We're going to record video walkthroughs of some of these. Drop a comment with which ones you want to see.

Turning data into strategic GTM assets | RevOps 🤝 GTM Engineering as an extension of your team | Clay Club Berlin Lead

We always wished Clay had a way to export and import tables as JSON.
One of our enterprise clients requested this exact thing - a way to backup workflows and tables.

So Robert Jett built it. Overnight.

Introducing Claymate - a Chrome extension that lets you export and import any Clay table as JSON.

It also unlocks something we love about n8n: the ability to have Claude Code generate importable workflows with full context.

Edit: We decided to opensource the Lite version of it- you can use it right away  :https://lnkd.in/dNAHXkA9
Open for feedback

Week 2 of using Claude Code as GTM Base's operating system.

Week 1 was exploration. Week 2 was infrastructure. Turning one-off wins into repeatable systems.

𝟏. 𝐂𝐥𝐚𝐲𝐌𝐚𝐭𝐞 
Robert Jett had been exploring API injection through other work. Last month, I built a Chrome extension to send LinkedIn connections to a Clay table. Unrelated projects. Combining these unrelated topics in a discussion with Claude led to a Chrome extension that reads our internal Clayskills. md and generates tables automatically. We use it internally to generate and document Clay tables. Stripped-down version is now public as ClayMate Lite.

𝟐. 𝐌𝐞𝐬𝐬𝐚𝐠𝐢𝐧𝐠 𝐩𝐨𝐬𝐭 𝐜𝐨𝐦𝐦𝐞𝐧𝐭𝐞𝐫𝐬 𝐚𝐭 𝐬𝐜𝐚𝐥𝐞
Asked Claude to message everyone who showed interest in my post about ClayMate with the download link once we published. I'd tried something similar before with Comet and ChatGPT Agent. Both timed out after 2-3 messages. Claude finished the full run using the Chrome extension.

𝟑. 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐟𝐨𝐫 𝐥𝐞𝐚𝐝 𝐬𝐜𝐨𝐫𝐢𝐧𝐠
For a client, we analyzed which factors actually correlate with conversion rates and deal size. Claude helped build and run the Python regression fast enough to iterate in real time.

𝟒. /𝐜𝐥𝐢𝐞𝐧𝐭-𝐧𝐚𝐦𝐞 𝐜𝐨𝐦𝐦𝐚𝐧𝐝
Built a Claude Code command that loads all context for a client before we start working. Latest Granola transcript summary with next steps, Linear task updates, and relevant information from the client context.md file. (see example image)

𝟓. 𝐖𝐞𝐞𝐤𝐥𝐲 𝐫𝐞𝐜𝐚𝐩 + 𝐩𝐥𝐚𝐧𝐧𝐢𝐧𝐠 𝐥𝐨𝐨𝐩
Friday: Slack summary of calls, client progress (via git commits), Linear updates. 
Sunday: Upcoming meetings, promised deliverables, what's still outstanding.

𝟔. 𝐏𝐫𝐨𝐩𝐨𝐬𝐚𝐥 𝐝𝐫𝐚𝐟𝐭𝐬 𝐟𝐫𝐨𝐦 𝐩𝐫𝐢𝐨𝐫 𝐞𝐱𝐚𝐦𝐩𝐥𝐞𝐬
Used the Google Docs MCP to pull similar proposals we've sent before and draft new ones after injecting the Granola discovery call transcript.

One pattern after two weeks: We are not building fully autonomous workflows. These are systems we trigger when needed. We stay in control. We just move faster.

We're recording video walkthroughs this week. Drop a comment with which ones you want to see.

2026 is the year Claude Code goes mainstream beyond engineering teams.

I have a dozen things I could talk about coming out of this break. I'm starting here because this is the bet we're making: building our operating system on Claude Code

I haven't felt this level of excitement since I first discovered Clay in December 2023.

In 2025, Claude Projects was the core of our client work. Every client had a project where transcripts, docs, and frameworks lived.

But it came with real friction: keeping projects updated was constant overhead, learnings never transferred across similar clients, and every new engagement meant building from scratch.

Skills changed that. We could finally package our frameworks and reuse them across projects.

But Claude Code takes it further.

We can now build an operating system that learns and updates itself. Context that compounds. Frameworks that evolve as we learn.

Everything we figure out on one client automatically makes us better for the next one, instead of starting over each time.

Our focus for 2026 is growth through exceptional client experience. Excited to have Claude Code power that.

If you're in RevOps or Business Ops and haven't looked at Claude Code yet, this is your sign.

—
Shoutout to Andreas Wernicke for the push to explore this over dinner a few weeks ago. And to Jacob Dietle, we've never spoken but I learned the most from his content in this space.

If you're afraid AI will take your job, stop documenting what you do.
Seriously.

Look at what AI is best at today: writing code.

Why? Developers are obsessive documenters. Every function, every edge case, every process. All written down. AI had the perfect training data.

Now look at where AI struggles. Jobs where knowledge lives in people's heads. Where "how we do things" is tribal wisdom passed through Slack threads and coffee chats.

So if you work in a specialized field with little documentation? Keep it that way. 
Guard your knowledge. 
Never write a process doc again.

I'm kidding. Obviously
That's how you become a bottleneck.

But "document everything" isn't the answer either.

Over the past few weeks, working with Claude Code and building context engines, one thing became clear: 

the real skill is knowing what to document and what not to.

What's core vs. what's noise. 

That clarity is the moat now.

The people who'll thrive aren't the ones documenting everything.

They're the ones who identify the 20% that matters and let AI scale their output from there.

Over the break, I went deep into context graphs.

Two posts on X got me thinking. One by Aaron Levie  on the era of context. Another by Jaya Gupta and Ashu Garg on context graphs as the next trillion-dollar platform.

The thesis: in a world where everyone has access to the same AI, competitive advantage comes from context. Decision traces, exception logic, precedent, tribal knowledge.

Capture everything. Make it queryable. The companies that accumulate the most context win.

It sent me back to Alan Watts.

Watts talks about how what makes us unique isn't what we remember. It's what we forget. Two people at the same event walk away with completely different memories.

That's not a bug. That's identity.

Your selective attention, your filtering, your ignoring is what makes you, you.

These ideas kept bumping against the context graph thesis.

The assumption right now is more context = better agents = competitive advantage. So everyone is racing to capture everything. Every decision trace. Every Slack thread.

You might argue: Just capture everything. Storage is cheap. Filter later.

Sure. But the filter still has to come from somewhere.

If you don't have clarity on your ICP, your values, or how you make decisions, then you're just querying a pile of data with no point of view. You can slice it a hundred ways. But you won't know which slice matters.

Think about the best companies you've seen. They don't win because they captured more.

→They have a tight ICP definition that filters out noise.
→They have clear qualification criteria.
→ They have values that make most decisions obvious.

That clarity is the moat. Not the data.

The companies that win won't be the ones with the biggest context graphs. They'll be the ones with the clearest point of view on what to pay attention to and what to ignore.

The question isn't just "what context should our agents have access to?"

It's "what kind of mind are we building?"

GTM engineering isn't a fad. It's not RevOps rebranded. It's here to stay.

I started offering fractional RevOps in 2023, added GTM engineering in 2024. Working at that intersection, one pattern keeps showing up.

Most companies are realizing they need this capability in-house. They typically look at two options: hire for it, or train an internal resource.

Hiring is tough. The talent pool is small. You either find someone experienced (rare and expensive) or someone new who picked up the tools but lacks the depth.

Training works. It could be someone from RevOps, a system thinker in marketing, or an SDR who wants to go deeper on the technical side. But some people learn best by doing - in their own environment, on their own problems.

That's what led us to add "Build With You" advisory at GTM Base 

Your team implements. We guide.

In practice:
→ A strategic workshop to set the roadmap.
→ Weekly calls to unblock execution. 
→ Async Slack support for day-to-day questions.

Your team does the work. We help them avoid the expensive mistakes.

Some clients start with managed service (we build it for them) then transition to advisory once they're ready to own it.

Either way, we work as an extension of your team, not an external resource.

The goal is the same:

Your team ends up owning everything.

No black box. No dependency.

How are you thinking about building this capability on your team?

I used AI to calculate how much carbon I'm burning by using AI

If you're anything like me, Claude, Cursor, and Clay have become how you work. But I wanted to know: what's my actual footprint from all this usage?

Over the holidays, I finally had time to stop wondering and start building.

I created a simple tool that: 

→ Takes your Clay 2025 Wrapped or Cursor usage data (or manual input) → Calculates your estimated carbon footprint 
→ Shows how many trees you'd need to plant to offset it 
→ Lets you plant them directly through Ecologi

Why Ecologi | B Corp™? I looked at a few different partners in this space and they stood out for two reasons: they have the most credibility when it comes to verified carbon offsetting, and they have a proper API. That meant I could actually build this with Claude and Lovable without hacking together workarounds.

Is it a proof of concept? Yes. 
Is it perfect? Definitely not. 
Did it feel good to finally ship something I've been sitting on? Absolutely.

This is what I love about the times we're living in. An idea that's been collecting dust in my backlog for months went from concept to working prototype in a few hours.

If you're curious about your own AI carbon footprint, I'd love your feedback.

Try here: https://offsetai.world

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