r/gtmengineering

ok rant incoming because I genuinely can't anymore.

Has anyone here actually used sales signal tools? You know the pitch: this company just hired a VP of Sales → contact them now. This one opened a new facility in Rotterdam → call today. This founder posted about scaling → they're ready to buy. Sounds great on paper. Here's what actually happens:

You need a tool to find the signals. A tool to enrich the signals. A tool to write emails based on the signals. A tool to verify the emails you found with the previous tool. A tool to research the company before you write the email. A tool to tie your shoes. A tool to check yourself in the mirror before the call.

And the actual ROI of this entire stack? Who's really measuring it? I wasn't for a long time.

Quick context: I work at a warehouse management software company, running outreach solo, but it's actually full funnel, from finding the prospect to booking the call. My monthly bonus is tied to qualified meetings I set. So I'm pretty motivated to not waste time on tools that don't move the number.

Used to run Apollo + whatever else to build lists. 1000+ contacts, full spray, spoiler: completely useless.

Now I use two things.

Karhuno for opportunities (full disclosure: it's the GTM tool I work on, so bias acknowledged, but it works, around 15 real qualified signals per day, not lists, actual buying signals) and Smartlead to manage sending. That's it.

1 in 15 becomes a qualified meeting. Not magic, there's real business intelligence behind each signal I receive. But I don't need to orchestrate 8 tools to get there.

One thing I've learned in GTM: if you have a real process, you're not inventing anything. It's linear. Repetitive sometimes, yeah, very. But the results hold. And when your paycheck depends on meetings booked, that consistency matters more than any shiny new tool.

Genuinely curious, how many tools are you actually using day to day. Not the ones you've tried. The ones you'd never remove from your stack.

What the title says. I have a Clay account with 108,000 unused credits. Willing to sell to the highest bidder.

After burning a few domains, and testing too many tools, this is the outbound stack I’ve finally settled into this year. it’s been stable and scalable enough for me so far.

Current setup:

Lead sourcing:

• Linkedln Sales Nav for targeting
• Icypeas for contact data
• occasional enrichment/cleanup through

Infra:

• 4 secondary domains
• 12 inboxes total
• relatively conservative sending limits
• custom tracking domain
• mostly plain-text emails

Sending:

• Plusvibe for warmup + sequencing

Writing/process:

• keeping emails shorter
• less personalization theater
• more relevance + timing
• avoiding heavy HTML/templates completely

Simpler setup works better for me. Every time I added too much automation or too many moving parts, smth broke.

What everyone else here is running now

disclaimer, i'm the data engineer dlthub cofounder., You might know us from dlt, the open source pythonic ingestion library

We built an agentic data pipeline builder for data teams. The first people who lean into it weren't data engineers, but roles that wanted to take the fast lane.

They were GTM engineers, data agencies, and solo data scientists. People paid on outcome, not on stability. A mature data team has rigor, process, and a tool footprint that took them two years to negotiate. They don't want to jump into something new without having validation of outcomes, which for an industry can take years.

A GTM engineer has performance goals in mind - you win on getting things done, not defending the way we do things. A consultancy that can deliver 1.5x faster sees their revenue from 33% (margin) to 83%, a 2.5x increase. These people don't wait around for someone to push them, they are professional builders with different incentive structures who choose performance over process.

What product am I talking about?

dltHub Pro provides Claude/Cursor/Codex users a way to build and run high quality custom code data engineering /GTM pipelines in python, without leaving your chat session. With end to end agentic context and toolkits, most data roles work is possible from simple prompt, including deployment to our infra. Our infra is lightweight serverless, priced slightly above what you'd pay yourself for the same serverless infra. Transparent pricing, no predatory surprises.

This is essentially for individuals or small teams who want to build data stacks quickly without worrying about tools, infra, or even architecture - everything comes with "training wheels" for agents to ensure quality generation.

We use it internally for our gtm ops around attio, enrichments, outreach, community, you know the stuff.

How to try it

run uvx dlthub-start on your agentic cli. there's a no card required trial that includes 30h free run time. If you want to upgrade, the entry tier is $119 (we designed it to be a cheap place to run small-medium ops)

You can find more details if you are interested all over our website and blog.

Thanks for reading!

Most platforms I've evaluated have a version of account history but when you dig into it the intelligence doesn't really persist in a meaningful way. Campaign ends, the account context resets. A rep leaves, the signal history goes with their activity log. You run a new sequence six months later and the platform treats the account like it's never been touched. I am looking for a platform where account intelligence compounds over time. Every signal, every engagement, every buying committee change accumulates into a profile that gets more useful the longer you're on the platform, not one that resets every time you change a campaign or a rep moves on.

Are these people who are jobless and trying to reinvent themselves in a new role?

Came from RevOps, spent the last few years going deep into GTM engineering. I build outbound systems, enrichment waterfalls, Clay workflows, AI agents, data pipelines, n8n, Smartlead, Claude Code, Supabase. I can also deploy and ship things end to end.

Looking to get my hands on real problems. If you are building something or stuck on something, DM me. Happy to help for free.

So it's been a hot topic of discussion in the past few weeks within my network and we all are coming to the same conclusion. "GTM Engineers" are becoming super saturated as Clay is becoming easier to use by the day.

The step comes when being able to use Claude Code and do much more with AI.

What do you guys think, are GTME'S becoming old news and is AI Growth Engineers becoming GTME 2.0?

Looking for guidance on how to handle emails. Imagine this situation

- enrichment says email is: x@company.com

- prospect form fills on website: b@gmail.com

- later on prospect requests demo: xy@company.com

I keep running into situations where I have personal emails muddled up with work emails and then work emails from enrichment tools mixed up with user submitted work emails. I'd like to not have 1 CRM field per diff email source and then create one "parent" field? but that is increasingly feeling the cleanest. Curious what others think

Looking for a GTM to help/consult on a project. We have 250,000 small business type companies in our db, name and address only, need to find decision makers with verified mobile numbers and emails. Only getting a 40% success rate atm, we’d like 75%.

Many of them won’t be on Apollo, Prospeo etc.

Hi I have enterprise AI products which I want to sell to mid-market and enterprise US customers. Leadgen is still possible but cold calling seems like it doesn't work - people are either not answering or not interested (we are using a twilio number to call), linkedin is not adding a lot of value (very slow) and therefore want to understand what would be a good way to do GTM (Product is voice ai for the sales/calling teams and will probably be evaluated by the Client's Engg teams as well).

I understand that voice ai is crowded, but still very interested in enterprises/midmarket GTM playbooks (if not for this product, then something else later)

How have you made this work.

While I was searching for a more creative gtm strategy for detecting intent signals I came across a few like using satellite images and ....

But then I figure out mostly all of these strategies where first implemented by the hedge funds to collect company data

Are hedge funds single handedly responsible for innovations in gtm .

Love to know your insights guys , lemme know

Not used chat gpt so bear with me

What are the best resources to learn gtm through ?
Are cohorts/ courses really necessary , pls help me understand , if there are gtm engineers here your advice would be really helpful .

Hey,

So we’re building an AI-native GTM agency (I like saying team, less pushy) and I have no experience in GTM engineering, but I have in outbound and sales and I have an incredible technical team of AI and software engineers and a co founder beside me as we build the internal agent that will handle most outreach.

We will be targeting a very specific ICP in B2B SaaS

Where do I start? Is it too saturated to jump in every founders inbox and pitch “15 meetings or you don’t pay”

Maybe when it comes to GTM engineering teams it’s a different type of offer?

I’m looking to make a very strong offer where it’d be difficult for them to refuse

I thought of small set up fee + rev share? Pay per qualified demo we get?

Would appreciate some insights from you guys

Which plugins/connectors/etc have you used with Claude Code that you really like? If you haven't installed any, what about MCP Servers that you have used?

Hi,

B2B sales company here. Looking to send 4-500K cold emails per month with personalized copy based off of leads website. We have a vendor that we are currently using but not thrilled with their overall lead lists and ability to accurate scrape data.

Pls dm me if you can help and have a proven track record.

I’m 8 months into my GTM engineering journey and just signed my fifth paying customer. Looking back at the first 90 days I spent a lot of time building things that didn't matter and almost no time building the infrastructure that actually runs my GTM motion now.

Giving back to the community and for some good karma :) here's what i would do differently starting Monday if i had to start all over again:

Record every single call. i didn't start recording and reviewing calls until month 4, which means i had zero transcript data on my first 30 conversations. Those were the most important conversations i had with early prospects. Buildbetter went in at the end of month 4 and should've been day 1. The compounding effect of reviewing 30 calls worth of transcripts versus 0 is not small, I can tell you that.

Build the automation layer before you need it. i built relay into my stack in month 3 when things started slipping through the cracks. By then i had already lost at least two deals to follow-up lag. The automation should be there before you have enough pipeline to need it and building it under pressure means building it badly.

Have a prioritization model before you have accounts to prioritize. This sounds backwards but it isn't. I spent the first 3 months treating every account in my pipeline with roughly equal energy. Yalc forced me to build a scoring model i should've built on day 1 with fake accounts and no data, then refined it as real signals came in.

Now i have to say what i actually spent week 1 doing lol. I was obsessing over a website and rewriting positioning docs over and over again. I optimized for things that felt like building a company instead of things that would tell me whether i had a company worth building.

Infrastructure built late costs more than infrastructure built early because you spend the middle months flying blind.

if you're in the first 60 days of a GTM motion, i would genuinely prioritize these over almost everything else including the pretty website.

Has anyone gone through something similar? How were your early GTM days?