The Secret Weapon that Makes Anyone A GTM Engineer
The Fastest Way to Build GTM Tables, Insights, and Automations That Actually Work using Sculptor (Clay’s AI Copilot)
By the end of this newsletter, you’re going to feel like a GTM engineer (or at least be dangerous enough to impress someone on your team). This week, we’re diving into Clay’s Sculptor, the AI co-pilot that turns a rough GTM idea into a production-ready table faster than you can say “token cost.”
Last week’s Clay build was one of my most-read posts ever, and every time I publish a Clay breakdown, my inbox fills up with: “Can you do this for me?” and “How long did it take you to learn this?” The good news is that Sculptor gets you 80% of the way there (where was this when I was learning Clay?!).
This week’s Stack:
1 video: Become a GTM engineer with a full Clay Sculptor + Claygent workflow
1 prompt: Humanize any AI-generated draft with a three-level refinement system
1 tool: Mixboard, Google’s new playground for fast, flexible AI audio
3 resources: Smarter GenAI marketers, quality-focused GTM systems, real AI SDR benchmarks
3 jobs: High-impact marketing + AI ops roles at Braze, Gametime & Alteryx
Let’s go!
Workflow Walkthrough: Sculptor Is a Cheat Code
Where was Sculptor when I first started learning Clay? Seriously. This thing feels like a full-on cheat code. But as powerful as it is, it’s not a replacement for actually learning Clay. You still need to troubleshoot, edit, and adjust to get production-ready tables that behave the way you expect. Sculptor accelerates the build, but you’re still the engineer behind the wheel.
In this week’s video, I take you through a full GTM assembly line using Sculptor.
What you’ll see:
What Sculptor is, where to find it and how to use it
Tips, tricks and tactics that I use to get the best output from Sculptor
Quick overview if a Scluptor build that analyzes content gaps and suggests content ideas by industry
Full walkthroughs of a workflow that finds hiring data, analyzes hiring trends, prioritizes high-intent accounts, and offers tips for exactly how to tailor outreach based on those signals
Here’s a Google doc with the exact prompts I used in this build.
Here are the templates for the tables shown in this episode:
Content Gap Finder: Instantly find what your competitors cover, what your audience cares about, and where you can win with high-leverage content your market is missing.
Uncover Hiring Trends for Target Accounts: Turn hiring data into a prioritized list of high-intent accounts, giving marketers a fast, reliable way to spot which companies are growing, investing, or tightening budgets, and exactly how to tailor outreach based on those signals.
Demand Gen Offer Testing Toolkit: build a full offer-testing system for demand gen teams, turning raw persona and industry data into tailored pain points, high-impact offers, conversion-driven hooks, and a prioritized test plan.
Prompt of the Week: Make AI Content Sound Like a Real Human
One of the most common questions I get is: “How do I make AI content not sound like AI?” Most drafts don’t need a total rewrite. They just need the robotic edges softened, the rhythm loosened, and a few human fingerprints added.
This week’s prompt gives you a full system for taking any AI-generated draft and making it sound naturally human, without losing the core ideas. No, this is not the same prompt from Sabrina Ramonov that has been making the rounds.
You are a content authenticity specialist who transforms AI-generated drafts into genuinely human-feeling content. Your goal is to identify and eliminate AI patterns while preserving valuable ideas.
## Input Content
**Original Draft:** [PASTE_AI_GENERATED_CONTENT]
**Content Type:** [SOCIAL_POST/BLOG/AD_COPY/EMAIL]
**Target Audience:** [SPECIFIC_DEMOGRAPHIC]
**Brand Voice:** [CASUAL/FORMAL/PLAYFUL/EXPERT]
**Platform:** [WHERE_IT_WILL_BE_PUBLISHED]
## AI Pattern Detection & Removal
### Phase 1: Identify AI Markers
Scan for and flag:
- Em dashes (—) usage
- Overused transitions (”Moreover,” “Furthermore,” “In today’s world”)
- Perfect grammar and punctuation
- Formulaic structures (intro-3 points-conclusion)
- Generic phrases (”dive into,” “unlock,” “elevate,” “transform”)
- Excessive use of colons and semicolons
- Too-perfect parallel structure
- Hedge words (”arguably,” “potentially,” “seemingly”)
### Phase 2: Human Imperfection Injection
Add strategic imperfections:
- **Intentional casualness**: Contract words, start sentences with “And” or “But”
- **Rhythm variation**: Mix short punchy sentences. With longer, more meandering thoughts that feel like natural speech patterns
- **Voice inconsistencies**: Occasionally break your own rules
- **Specific details**: Replace generic examples with ultra-specific ones
- **Time markers**: “Last Tuesday” instead of “recently”
- **Personal asides**: Brief tangential thoughts in parentheses
- **Emotional tells**: “Honestly,” “Look,” “Here’s the thing”
### Phase 3: Structural Humanization
- Break conventional formatting
- Use unexpected paragraph breaks
- Include sentence fragments. On purpose.
- Add conversational callbacks (”Remember that thing I mentioned?”)
- Include real-world specifics (actual brands, places, cultural references)
## Output Format
### Version A: Light Touch
[Minimal changes - just remove obvious AI patterns]
- Key changes made: [LIST]
- AI markers removed: [COUNT]
- Readability impact: [ASSESSMENT]
### Version B: Medium Humanization
[Balance of AI improvement and human authenticity]
- Strategic imperfections added: [LIST]
- Voice adjustments: [WHAT_CHANGED]
- Anticipated performance: [PREDICTION]
### Version C: Full Human Mode
[Maximum authenticity, might sacrifice some clarity]
- Radical departures: [WHAT_YOU_DID]
- Risk assessment: [WHAT_MIGHT_NOT_WORK]
- Best use case: [WHEN_TO_USE_THIS]
## Humanization Techniques Applied
### Style Adjustments:
- Removed: [X] em dashes → replaced with: [ALTERNATIVES]
- Removed: [X] perfect transitions → replaced with: [NATURAL_FLOW]
- Added: [X] intentional typos/casual language
- Added: [X] culture-specific references
### Content Enrichment:
- Generic example: [ORIGINAL] → Specific example: [REPLACEMENT]
- Vague claim: [ORIGINAL] → Concrete detail: [REPLACEMENT]
- AI phrase: [ORIGINAL] → Human alternative: [REPLACEMENT]
### Final Authenticity Check:
□ Would a human actually say this?
□ Does it sound like someone typing, not generating?
□ Are there enough imperfections to feel real?
□ Does it match how the brand actually talks?
Provide all three versions with clear documentation of changes, allowing for selection based on platform requirements and risk tolerance.
I’ve added this to my The Ultimate ChatGPT Prompt Library for B2B Marketing Leaders notion doc. Check it out for 60+ more prompts.
Know someone who might find this prompt useful? Share it with them!
Tool of the Week: Mixboard, Google’s New Playground for AI Audio
Most AI audio tools feel either too limited or too technical. Mixboard hits a sweet spot: drag-and-drop simple, but powerful enough to generate music, soundscapes, voice blends, and weird little audio experiments you didn’t know you needed. It’s perfect for marketers who want custom sound beds for videos, product launches, or social clips without hiring a sound designer.
Suno is still my go-to for AI music, but this is easily my second choice.
Try it: https://labs.google.com/mixboard/welcome
AI Resource Roundup
The GenAI Marketer (MKT1 Newsletter): A sharp breakdown of how marketing teams are actually adopting GenAI, not the hype-cycle version, the operational one. Great framing on where AI accelerates workflows, where humans still need to lead, and why “better inputs lead to better outputs” is now a core marketing skill. Perfect if you’re leveling up your team’s AI literacy. I don’t agree with everything here, but I think it’s a really great way to think about it.
GTM AI Is Solving Quantity — Time to Tackle Quality (Topline): This issue makes a strong case that we’re moving past the “AI = more volume” era and into the “AI = better decisions” era. Helpful lens on AI-driven GTM systems, lead qualification, and how to rethink quality signals when content, outreach, and research are all automated.
AI SDRs, Real Results & What Teams Miss (Jen Igartua on LinkedIn): One of the more grounded takes on AI-powered outbound. Jen breaks down Snowflake’s 15x reply-rate jump (0.5% to 7.5%) and why it worked: split prompts, self-scoring models, human-in-the-loop, and real enablement. The comments are gold too, especially the debate around “AI replacing SDRs” vs. AI finally doing the writing so humans can focus on judgment.
Hot AI Jobs
Marketing Automation and Platform Lead at Gametime
Location: Remote (US)
Pay Range: $155K–$182K/yr
Senior Product Marketing Manager, Braze Intelligence at Braze
Location: New York, NY (Hybrid)
Pay Range: Not Listed
AI Operations Lead, Marketing at Alteryx
Location: Irvine, CA / Remote
Pay Range: Not Listed
This one’s landing in your inbox a little later than usual because winter finally showed up in Colorado, and with it, unexpected snow days. Which means the kids were home… which means I got approximately zero deep work done. But that’s life right now, and honestly, I’m learning to roll with it.
Thanks for making space for Stack & Scale in your week, even when mine looks like a weather-induced scramble.
– Brandon
Great content, Brandon! Gave me a bunch of ideas.
Thanks Brandon - great post and very helpful. I love your practical, support-focused approach.