Daniel | GTM Engineer

I build AI-powered GTM systems that help companies find, qualify, and convert more opportunities.

[Daniel | GTM Engineer @Danny_buildss]

I built an outbound system where the prospect list is basically the easy part.

The interesting part is everything that happens after the list exists.

Here’s the workflow:

1. SIGNALS

Instead of pulling 10,000 companies and hoping some are interested, the system watches for things like:

→ New funding
→ Hiring activity
→ Product launches
→ New executives
→ Expansion
→ Technology changes
→ Website changes

Those signals become reasons to prioritize an account.

2. RESEARCH

When an account gets flagged, an AI agent researches it.

Company → product → ICP → recent changes → relevant people → likely pain.

No generic “I saw your company is growing” research.

3. QUALIFICATION

The system scores the account against the actual ICP.

Good fit ≠ good opportunity.

The goal is finding accounts that have fit + timing + a reason to talk.

4. PERSONALIZATION

Only after qualification does the system generate outreach.

The message is based on the signal and research not five variables merged into a fake-personalized paragraph.

5. ROUTING

High-intent accounts get pushed to the right place automatically.

CRM.

Slack.

Email sequence.

Rep queue.

Whatever the team’s workflow requires.

6. FEEDBACK

Replies, meetings, conversions and rejection reasons flow back into the system.

So the next batch isn’t built from the same assumptions as the last one.

That’s the part I’m most interested in.

Not:

“AI can send cold emails.”

We’ve known that.

It’s building a GTM system that can continuously answer:

Who should we target?
Why now?
What should we say?
Who should handle it?
What did we learn?

That’s where GTM engineering gets interesting.

[Daniel | GTM Engineer @Danny_buildss]

@Gsandec Hmm true

[Daniel | GTM Engineer @Danny_buildss]

being a small team changes which GTM systems make sense.

you don’t need a 12-step autonomous outbound machine if you’re still figuring out who actually buys.

at 500 target accounts, manually researching your best prospects might beat building a giant enrichment stack.

at 50,000 accounts, automation becomes a different conversation.

i’d rather build a simple system that helps me find the right 50 accounts, learn why they respond, and then automate what actually works.

don’t engineer for a scale you haven’t earned yet.

[Daniel | GTM Engineer @Danny_buildss]

@thedbeaudoin The same principle applies to sales too. You don’t build pipeline because you need it today; you build it so you’re never desperate for it.

[Daniel | GTM Engineer @Danny_buildss]

@NickAbraham12 Pay-per-lead is a strong way to prove yourself, but it creates a weird relationship at scale.

[Daniel | GTM Engineer @Danny_buildss]

AI + backend build log

pretty quiet today.

spent most of the day building and fixing backend stuff.

one part is looking really promising, but i’m letting the data run before changing anything.

tomorrow = more building + testing.

keep shipping.

[Daniel | GTM Engineer @Danny_buildss]

10 steps to build an ecom creative intelligence system

1. pull every competitor ad you can find, not just the obvious winners.

2. store the creatives, copy, landing pages and performance data in one place.

3. deduplicate everything before analysis starts.

4. use AI to tag every ad by hook, angle, offer, format and awareness level.

5. cluster similar creatives so you’re finding patterns, not counting duplicates.

6. score the patterns against your own winning ads.

7. surface the angles your competitors keep repeating.

8. turn the strongest patterns into new creative briefs automatically.

9.push the briefs straight into your team’s production workflow.

10.feed performance back into the system so the next batch gets smarter.

This is to turn thousands of ads into a creative intelligence system your team can actually use.

[Daniel | GTM Engineer @Danny_buildss]

My actual ecom creative research routine:

Pull every new competitor ad into one database
Remove duplicates
Save the hook, offer, format and landing page
Tag each ad by angle and awareness level
Group similar angles together
Compare them against our own winning creatives
Flag angles competitors keep repeating
Turn the strongest patterns into new creative briefs
Send the briefs to the creative pipeline
Feed performance back into the database

It’s mind numbingly repetitive.

Which is exactly why I built a system to do it.

[Daniel | GTM Engineer @Danny_buildss]

AI creative is moving ridiculously fast.

A creative workflow that used to take hours can now go from:

idea → script → variations → production

in a fraction of the time.

And we’re still early.

The interesting part isn’t making one good AI ad.

It’s building the system that can keep producing and testing them.

Q4 hasn’t even started yet.

[Daniel | GTM Engineer @Danny_buildss]

@scalewebinars I think people underestimate how often the ceiling is just the acquisition system.

[Daniel | GTM Engineer @Danny_buildss]

@jordan_ross_8F The low competition is probably more valuable than the 4,600 businesses.

[Daniel | GTM Engineer @Danny_buildss]

@brett_fish I’d trust these diagnostics a lot more if they separated severity from spend exposure.

[Daniel | GTM Engineer @Danny_buildss]

@mannyyreyyes @HubSpot @andyaustinads This is a good example of turning content into an actual GTM system.

[Daniel | GTM Engineer @Danny_buildss]

@thedbeaudoin I like the shift from time-based ramping to capability-based ramping.

[Daniel | GTM Engineer @Danny_buildss]

@JamesonCamp There’s something powerful about turning customer language into the creative itself.

[Daniel | GTM Engineer @Danny_buildss]

Because a 90% close rate can look amazing while the business is actually underpricing the work. If you’re winning almost every estimate, the better question is whether you’re leaving margin on the table. A 50–60% close rate at a much higher ticket can be a far healthier business than 90% at a price that’s too low.

[Daniel | GTM Engineer @Danny_buildss]

@akothari I like this as an objective function because it forces you to work backwards from the rep’s calendar.

[Daniel | GTM Engineer @Danny_buildss]

@eCom_Amin This is why blended performance matters so much.

[Daniel | GTM Engineer @Danny_buildss]

@wahg_one I’d keep every failure Codex makes in the skill-building loop.