[Clay Template] Automate Intros to Target Accounts from Your Investors
Build a repeatable system to turn one company URL into a warm investor intro in under 5 minutes
Early-stage companies ask for investor intros all the time.
And it makes sense. Your investors already have relationships with the exact companies you’re trying to reach. One good intro can turn into pipeline faster than anything you’re running on your own.
The problem is I’ve watched this break down in the same spot over and over. You know the accounts you want. You know your investors can help. But connecting those dots takes time, and most of the asks never get sent.
I built this workflow to fix that.
And once it was working, I realized it’s not limited to investors. You can run the same play with LinkedIn connections, advisors, customers, anyone sitting in the middle of the network.
This is one of those systems you set up once and keep using.
This week’s Stack.
1 workflow: Turn target accounts into warm investor intros with Clay
1 prompt: Generate a paid landing page that converts
1 tool: Fix weak prompts with Claude’s prompt optimizer
3 resources: AI content strategy, workflow design, and agent reliability
3 jobs: Growth, lifecycle, and product marketing roles in AI
Let’s dive in.
Workflow Walkthrough: Get Warm Intros to Target Accounts from Your Investors
I’ve watched a lot of investor outreach stall at the same point.
You have a list of accounts you want to get into. You know there are investors in your network who could help. And then you sit there trying to figure out who knows who and whether it’s even worth asking.
I built this workflow because I got tired of that step.
Now I drop in a company URL and get back a list of relevant investors, the partners tied to my space, the people we both know, and a forwardable email I can send without rewriting anything.
Here’s what you’ll learn:
How I build a live investor dataset inside Clay using enrichment data
How I narrow down to partners already investing in your category
How I map shared connections without digging through LinkedIn tabs
How I generate a clean intro email someone will forward without editing
How I reuse the same setup for advisors, customers, and partner intros
Once this is running, you’re not sitting there trying to “figure out outreach.” You’re reviewing intros that are already written.
Watch the full breakdown here: https://app.clay.com/shared-table/share_0te8icw2aXwU4eCECR6
If you raise capital or run partner motions, you’ll use this more than you expect.
Subscribe if you want more workflows like this each week.
Prompt of the Week: Build a Paid Landing Page That Converts
I’ve seen a lot of paid traffic wasted on decent-looking pages that don’t convert. The page has broad messaging, vague value, and no clear path to action. But paid traffic isn’t browsing – they clicked for a reason, and if the page doesn’t match that intent fast, they bounce.
Use this prompt in Claude Design to act like a conversion-focused marketer.
You are a senior conversion copywriter and growth marketer.
Your job is to create a high-converting landing page for a paid acquisition campaign.
This page is NOT a homepage. It must be tightly aligned to a specific audience, offer, and intent.
Before writing, think through:
- Who is clicking this ad?
- What problem are they trying to solve right now?
- What would make them hesitate?
- What proof would reduce that hesitation?
Then build the page.
INPUTS:
- Product: [Describe your product]
- Audience: [Who is this for? Be specific]
- Pain point: [What problem are they actively trying to solve?]
- Offer: [What are you asking them to do? Demo, signup, download, etc.]
- Traffic source: [Google Search, LinkedIn Ads, Meta, etc.]
- Campaign angle: [What promise or hook got them to click?]
- Proof points: [Customer logos, case studies, metrics, testimonials]
- Competitors or alternatives: [What else are they considering?]
OUTPUT:
Write a complete landing page with the following structure:
1. Headline
- Clear, specific, and aligned to the ad they clicked
- Focus on the outcome, not the product
2. Subheadline
- Expand on the promise
- Add clarity on who it’s for or how it works
3. Hero section CTA
- One clear action
- Button copy should reflect the offer
4. Problem section
- Describe the exact situation the reader is in
- Make it feel familiar (meetings, workflows, frustrations)
5. Solution section
- Show how the product solves that problem
- Keep it grounded in real usage, not abstract claims
6. How it works
- 3 to 5 steps
- Simple, concrete, and easy to visualize
7. Proof
- Include metrics, outcomes, or social proof
- Make it believable and specific
8. Objection handling
- Address common concerns (time, cost, complexity, risk)
- Answer them directly
9. CTA section (repeat)
- Reinforce the value
- Make the next step feel easy
10. Optional: FAQ
- Include 3 to 5 real questions a buyer would ask
WRITING STYLE:
- Use plain language
- Write like a practitioner, not a brand
- Avoid hype and generic phrases
- Be specific about outcomes and use cases
- Keep sentences tight and readable
GOAL:
The reader should feel:
- “This is exactly for me”
- “This solves the problem I came here for”
- “This seems easy enough to try”
Now write the full landing page.Tool of the Week: Fix Your Claude Prompts
Claude’s prompt optimizer cleans sloppy prompts. You drop in a rough prompt, and it rewrites it with better structure, clearer instructions, and tighter constraints so the model knows what to do.
I don’t know why, but it feels like we’ve gotten away from good prompting. And if there’s one thing I know, it still matters.
Try it: https://claude.ai/public/artifacts/422bb5fc-c03e-4488-9e49-9ad4239398fe
AI content all sounds the same (Kieran Flanagan): So much AI content feels interchangeable. In this issue, Kieran gets into distribution, taste, and why better inputs (not more outputs) are what separate teams that stand out from the ones flooding the feed. Worth reading if you’re publishing anything with AI in the loop.
Head of Claude Code: What happens after coding is solved | Boris Cherny (Lenny’s Podcast): This episode shows how to think about chaining models, structuring inputs, and getting more consistent outputs across workflows. If you’ve been stitching tools together and hitting reliability issues, I think you’ll like this one. And I mean it’s in interview with the head of Claude Code, so it’s good.
Agents need to survive the second run (Hiten Shah): A lot of AI tools (agents and AI harnesses) look good on the first pass, but they fall apart when you try to reuse them. This post takes a look at durability, iteration, and what it takes to make agent workflows hold up over time.
Hot AI Jobs 🔥
This week leans into growth, lifecycle, and product marketing roles inside AI-native and AI-adjacent companies.
Growth Marketing Manager at Level AI
City / Remote: Not listed
Pay Range: Not listed
Lifecycle Marketing Manager at Delinea
City / Remote: Not listed
Pay Range: Not listed
Product Marketing Manager at Curri
City / Remote: Not listed
Pay Range: Not listed
Even if you’re not on the hunt, these job specs are a good pulse check on where AI marketing teams are investing.
Short one this week. I’ve been spending more time tightening systems like the one above and less time creating from scratch. It’s a better trade. Once something works, I want it running without me.
More soon.
– Brandon