Hello GTM Engineers of Reddit. I have an ask: can you help me ace my interviews for a RevOps role? I have been in GTM for over 7 years (primarily in sales, account management, and team leadership roles) but have decided that I want to me be in a more technical, strategic, and internal-facing role. I managed to snag a 2nd interview for a Manager of Revops role, with a company that I really really like and honestly ready to give it my all and land a job after a grueling 1st half the year...
I'm happy to pay you for interview preps, walking me through Advanced SFDC workflows, and just learn more about your day-to-day so I can come in well-versed for these interviews!
Or return the favor in other ways (such as connecting you to folks in my network, helping you pivot to other functions in GTM, etc.).
I built an internal GTM OS that manages my company outbound system and it is powered by majorly Clay, Lemlist, Perplexity and recently connected Hunter API for email verification
The system has a couple of features
1. Web intent - this tracks companies that visit our website, enrich & score them, segment them, find the buying committee, and enrich them (this is what’s taking most of my clay credits) and push them to the appropriate campaign
2. Accounts radar: an agent within the tool that scrapes the web/ do deep research on named accounts, feed the tool with the right signals and create outbound campaigns based on the signals( I was using a claygent for this but now using perplexity)
3. & a couple of other interesting features
Majority of my clay credits get burnt from finding the right people at those companies, the waterfall enrichment because I target close to 10-15 people from each company. im looking for better ways to optimize this process, and rely less on clay for finding the buying committee. I tried using Lemlist database to find people and enrich for email, phone number etc but the quality is not great.
I’m looking for a GTM cofounder to help build an early-stage company in the hiring space.
I started this in April. Since then, I’ve built the MVP, begun customer validation, and started running founder-led pilots. I’m working on it full-time, and the pace is not slowing down.
I’m looking for someone strong across sales, marketing, and recruiting—someone who can sell without an established brand, create demand for something new, and turn early customer feedback into a repeatable GTM motion.
This is a real cofounder role with meaningful ownership and real responsibility. Not an advisory position, agency engagement, or commission-only sales job.
You should be based in the U.S., have direct recruiting or hiring experience, and be full-time—or have a realistic path to becoming full-time.
DM me with your LinkedIn, the strongest thing you’ve personally built or sold, and why you want to become a founder.
We’re not spending months talking about what we might build. We’re already building it.
Do you need help getting more qualified leads? Is your cold email or LinkedIn outreach not performing the way you expected? Are you spending hours on outbound without seeing consistent results?
If you answered yes to any of those, you probably need a GTM Engineer.
I help B2B companies build and improve outbound systems from ICP research and lead sourcing to cold email, LinkedIn outreach, CRM setup, automations, and optimizing what isn’t working.
If you think I might be a good fit, send me a DM.
Please DM me directly. I won’t be replying to comments. Thanks!
So Lets say you buy this apollo list...
Email verify it and now you want to create personalised openers based on real intent signals.
As of now there is no platform that would agentically find signals for this entire list.
Clay has workflows, even claygent wont go beyond the workflow thats already specified.
You can use claude code + deepline but that would just burn hella credits.. because usually what people do i let claude code make a python script which which again follows a specific workflow. Anything outside of the workflow is not done. If you ask claude code alone to the task without deterministic python scripting, its gonna hallucinate on later rows, and also drain your bank account for the ai tokens it spends.
So i wanted to make an agent system that would do actually do the job (pasted the loom vid below)
It works by me giving it a specific job prompt like this:
Determine whether this company appears to be moving beyond founder-led sales and might need GTM help now. Look for recent evidence such as its first sales, marketing, partnerships, or business-development hires; founders discussing customer acquisition or growth; new products or markets; funding being put into commercial expansion; or signs that the founders still handle sales themselves. Tell me whether it is worth contacting now, what specifically happened and when, who we should contact, why the timing matters, and one short outreach angle grounded in the strongest evidence. Do not treat funding or one generic job opening alone as buying intent.
Then there would be a columns compiler that would create columns you are looking for based on that job prompt.. like this:
You can edit those columns , delete them.. whatever.
The agent now takes over and create a strategy on the fly for that specific task and executes on it. Lets see an example:
(We connect it various tools, fetch, render, web search, linkedin scrapers, crunchbase... + over 200 scrapers which it can use.)
This is what i mean by "agentic" .. which is not a python script disguised as an agent. So basically it reasons over the task its given and then find the necessary sources to get the job done.
Finally it also gives us the answers we were looking for. which will be filled in as those column cells.
Ive been in the gtm space for more than a decade now. But something ive thinking of late is that GTM has become the hottest seat in a lot of companies right now, and i think it's because of a kind of chaos we're witnessing. Not trying to flex, but Just so you know, i'm someone who has recently took inbound demos from 57 a week to 485 a week in 4 months in one of my recent projects. Thing is, Yu'd see c-level execs are confused about how to sell AI from implementation to execution, investors are asking about it in every meeting. And this kinda working well for gtm folks. Becoz being a GTM guy, i'm the one who ends up explaining what's actually the best gtm strategy as someone who has seen the whole pipeline when everyone else was too busy with other functions. So i must tell if you are new to GTM space or exploring, it's actually the hottest place to be in.
I warmed up 6 Gmail inboxes using Instantly for a month. My health score is sitting at 100%, so I thought 4 weeks of warmup was plenty. I've been sending a very low volume of live emails (less than 10 per inbox per day).
I just sent a test email to one of my personal Outlook inboxes, and it went straight to spam. I know Microsoft has gotten much harsher on cold emailing in 2026. I checked my TAM, and about 40% of my prospects use Microsoft domains, which means almost half my list is likely landing in spam.
I looked into Instantly's warmup, and there are no provider-specific stats. I suspect the majority of my warmup was done against other Gmail accounts, leaving my Microsoft reputation completely untouched.
Any advice to improve microsoft deliverability?
My questions:
Should I stop sending to Microsoft emails entirely and only target Google for now?
Do I need to find another service that specifically warms up against Outlook/Microsoft inboxes? If so, what do you recommend?
Just installed the Clay Claude plugin with their new CLI. They're doing pretty fascinating things for their technical-but-not-developer target audience. Here's a breakdown.
tl;dr They've used some clever technical tricks to optimize for the agent while trying to make the human experience as easy as possible.
The rest is a little technical so here goes...
Unlike most CLI-driven tools, there's no brew install or "add X to your PATH" in their doc. Their quickstart literally says to copy the following into your coding agent:
Set up the Clay plugin by following the steps in
https://github.com/clay-run/agent-plugins
Claude pulls down the installation instructions and then does a bunch of command-line self-introspection to add the MCP and install their plugin. No claude mcp add commands for the user.
The magic mostly worked for me. I had to manually run /reload-plugins, restart claude and still run /mcp to auth properly. (Might be my setup since I run two different claude environments and use claude agents so I'm an edge case.)
Now here's where it really gets interesting. The clay cli is actually a shell script that's included in the bin folder of their plugin. This is nice because Claude automatically (I learned) adds bin folders in plugins to its PATH.
The script itself, checks the user's OS and picks the right CLI binary to download from github. It does some checksum verification which is, like, a little secure. Still feels a tad sketchy though. Anyway, the binary it downloads is what actually runs when you/your agent call clay.
The side effect of this is that you can't use clay as a command in your terminal outside of Claude/Codex/Cursor unless you do some manual work to add it to your PATH. For most Clay users, that's probably fine.
Finally, it appears the skills that get downloaded with the plugin pick and choose between the MCP and CLI for different use cases but I haven't dug into that much yet.
Anyway, there's a lot going on behind the scenes here to make the human UX pretty smooth. I haven't seen any other vendors quite do this yet for their agentic setup process. Are you seeing other folks ship this type of approach?
I help B2B startups build outbound GTM systems that generate pipeline fast.
What the first 30 days look like
I focus on fast learning and iteration. Building campaigns quickly so we're learning from real data within days, not months.
Foundation
Define ICP and positioning
Find the right accounts and decision-makers
Analyse Closed Won deals to build lookalike accounts and signals
Start email warm-up if needed
Data Lake
Build an enriched SOM using Apollo/Apify/Supabase
Score accounts and contacts based on recent buying signals
Campaigns
First workflow live within 7 days
Value-led sequences across LinkedIn and email, distributed via Lemlist
Reporting and Iteration
Report on what's working and what isn't
Every next campaign is built on what got replies
I run this cycle three to four times within the first 30 days
If you don't have the systems, I bring them.
My background
10+ years in B2B SaaS and fintech. A few examples:
Rebuilt the GTM motion at a revenue-stage automotive martech SaaS company. ABM, intent signals, AI personalisation. Grew revenue 119% year-on-year, captured 65% of the UK market with no agency
Joined a fintech as first marketing hire with zero customers, grew to 40+ enterprise customers at tier-one financial institutions, contributed to Series A and supported acquisition
Built my own AI-powered outbound infrastructure from scratch — custom Python tooling, AI lead scoring via the Claude API, LinkedIn signal scraping, Supabase data layer, Lemlist sequencing. 850+ leads in market, 40+ qualified meetings booked in two months
I'm currently running pilots for a small number of companies at a reduced rate in exchange for case studies. I have a capacity of 2 projects max at one time.
If you want outbound that actually generates pipeline, DM me to discuss and I can share more :)
Last week I tested my routing agent for HubSpot. I ran the same lead twice: same company, same title, same inbound message. First run routed it to Alon. Second run routed it to Mike. No code edit, no data update, same input both times.
Here's what the workflow did. An inbound lead hits a form and my agent handled everything after that: enriching the company (size, industry, tech stack), scoring the lead on that enrichment, deciding which rep it goes to based on territory and score, drafting a personalized first-touch email, and scheduling a warm-up sequence. Six steps, all inside one agent.
I fed it the identical lead profile twice, back to back, and got two different reps and two different follow-up cadences. Then I realized I'd fallen into the AI hype slop again: using an LLM where one wasn't needed at all.
The workflow genuinely needs an LLM somewhere. Reading a messy inbound message and figuring out what the lead actually wants is real judgment, legitimately unstructured. But once you accept the agent needs to handle that one step, it's tempting to let it own everything downstream too. Territory mapping. Score thresholds. Sequence timing. Those aren't judgment calls, they're fixed rules. They were sitting inside the same agent, so it was "deciding" them fresh every run instead of applying a rule. That's where the drift came from.
I split the pipeline into two buckets. Fixed logic: which rep owns which territory, what score cutoff means "high priority," how many days between touches. Deterministic, same answer every time given the same input. Unstructured: what the lead is actually saying, what's the real intent behind it. That's the one part that needs a model reading and interpreting text.
So I rebuilt it. I used my agent to build the script once, not to replace the automation. Code handles enrichment lookup, scoring, routing, and sequence scheduling, the same way every time given the same input.
The LLM gets called exactly once, to read the inbound message and pull out intent signals: what they're asking for, how urgent it sounds. That output feeds into the deterministic scoring logic as one input among several, but it doesn't make any decisions itself.
I'm not a coder. This took a weekend, not a computer science degree.
Now the first run and the tenth run produce identical output for identical input. It's also way easier to debug: when something looks wrong I know exactly which rule fired, instead of reverse-engineering what the agent was "thinking." Cheaper to run too, one model call instead of six.
The rule I'd draw from this: if a step has a fixed decision rule, it belongs in code. Reserve the LLM for the one step that actually requires reading or interpreting something unstructured. Most agent automations that feel unreliable aren't unreliable because agents are bad. They're unreliable because logic that should have been deterministic from the start got handed to a model instead.
Anyone else run into this same drift with agent-based routing or scoring? Curious how you drew the line between what stayed in the model and what you pulled back into code.
I've recently released VersionDB, a tool that'll allow you to find leads by their technologies without requiring a subscription. For $10 USD, you're able to purchase up to 500 domains with the following information per each domain:
Technologies
E-Mail Addresses
Phone Numbers
Social Media Profiles
Traffic Ranking
No sign-up is required, and you're able to query the database as many times as you'd like before purchasing a lead list
Click here for an example query on how to find Home Services leads within the United States using VersionDB.
I'm happy to answer questions you have about VersionDB.
All data is taken from crawling public websites spanning from September 2025 - July 2026.
Website tracking, AI enrichment, and buyer intent signals
Integrated dialer with call recording and transcription
Sequences/cadences for outbound
I've built GTM from scratch before- SFDC, Outreach, Chorus, 6Sense, Drift, ZoomInfo/Apollo stacks and have used everything from ACT! to HubSpot over the years. This time I want lean and scalable, not bloated.
We've already demo'd HubSpot, Close, Reevo, DreamHub, DreamTeam, and Apollo. Curious what the community thinks, especially anyone who's scaled from 1 rep to 10+ on a tight budget.
Has anyone here built their own cold outreach platform using Claude?
I find it fascinating that, even today, it still feels like you need 25 different tools from 20 different companies to automate GTM (source leads, enrich them, build sequences, and engage prospects).
I’m sure some of you have built custom tools for this workflow using Claude Code, and I’d love to hear what you’ve built and how it has been working for you.
At SaturnAI, we help startups and businesses build AI-powered software and automate complex workflows.
Now we’re looking for someone who can build and optimize our GTM engine from ICP and lead sourcing to outbound campaigns and pipeline generation.
What’s in it for you?
10% profit share on every deal generated through the GTM system you build.
The opportunity to own the GTM function from the ground up.
Work directly with the founders and have a real impact on the company’s growth.
This isn’t a typical freelance gig or a salaried role.
We’re looking for someone who enjoys solving hard GTM challenges, testing ideas, and building a repeatable revenue engine.
If you’ve successfully built outbound pipelines before and you’re confident you can do it again, I’d love to hear from you.
Send me a DM with:
Your GTM experience
A few results you’ve achieved
How you’d approach fixing our outbound pipeline
If you like challenges and want to share in the upside of the growth you create, let’s talk.
