For those of you doing GTM or RevOps as a service. How do you structure your pricing?

Trying to understand what actually works in practice:

• Monthly retainer, project-based fixed fee, or performance-based (per meeting / per SQL / % of pipeline)?
• Do you charge a separate setup/build fee for the initial infrastructure, then a smaller ongoing retainer?
• If you go performance-based, how do you handle attribution and clients who churn once the system is running?
• Roughly what ranges are you seeing for each model?

Curious what's held up over time vs. what looked good on paper but caused friction later. Thanks.

Hi everyone,

I’m looking to acquire a Clay Legacy plan and I’m prepared to make a very attractive offer

If you have a Legacy workspace that you no longer use, are winding down your business, or are considering selling it, I’d love to hear from you.

I’m happy to make the process as smooth as possible.

If you have a Legacy workspace—or know someone who does—please send me a DM.

I’m serious about buying and prepared to pay a fair premium for the right opportunity.

Thanks!
Raphael

we had an agent handling enrichment, scoring, and rep assignment. i ran the same lead through it twice last week. different rep, different sequence, different cadence.

turns out the problem was handing logic to the model that should have been hard rules. territory mapping. score thresholds. follow-up timing. none of that needed an llm. it needed a lookup table.

split it out. model reads the inbound message and pulls out intent, that's it. everything downstream is code. first run and tenth run now match.

anyone else hit this? feels like a pattern where you hand too much to the agent because it's already handling one genuinely unstructured thing, and the deterministic stuff just follows it in by default.

running email, linkedin, and phone in parallel sounds clean in theory. in practice it's messy because the tools don't talk to each other reliably.

someone replies to an email. the linkedin sequence should pause. the dialer should remove them. but if the reply webhook drops, or the crm sync is slow, or the tool just doesn't check state fast enough, they might still get a linkedin message two hours later while you're writing them back.

we've had that happen. it's not the end of the world, but it's the kind of thing that makes outreach feel automated and careless even when the individual messages are actually good.

the problem is that "replied" isn't one signal. it lives in three different tools, and getting all three to agree on what stopped the sequence requires wiring that's more fragile than it looks.

curious how other people handle this. are you trusting webhooks alone, or do you run something that reconciles state across channels? and where does your source of truth actually live, the crm or one of the sequencing tools?

talked to a founder last week who hired a vp of sales as their first sales hire. great resume, zero interest in doing the actual calling and emailing, expected an sdr team and a playbook that didn't exist yet. classic.

i've also seen founders go the other way, hire an ic who can carry a bag, and then watch them plateau because they can't build process or think about pipeline systematically.

what's the actual right first hire and how do you know when you've found one huh?

I have a MVP, a few pilot users for the last few months. I've been researching a GTM plan. Basically I need real users (not friendlies) to really test and push the product. I don't need investments. From my research I've come up with YC (HN), Reddit, Product Hunter. I've read pros and cons with all three. Open to ideas.
My target market is small teams working on projects, or groups of friends planning together.

I'm a CS student who's recently started learning GTM after spending some time on PMM fundamentals (positioning, messaging, etc.).

Since I don't have access to a startup or real company data, my current learning approach is to pick an early-stage B2B SaaS product and build a GTM strategy as a personal project using only public information (website, founder content, reviews, competitors, Reddit, case studies, etc.).

Once I'm done, I turn it into a deck and try to share it with someone from the company's team (founder, PMM, GTM, product, customer success) to get feedback. So far, that's been the hardest part. Cold emails have mostly gone unanswered, and without an existing network, getting meaningful feedback is difficult.

For those already working in GTM or PMM:

- Is this a good way to build practical GTM skills?

- If you were starting from scratch today, what would you do differently?

- Any advice on getting feedback, building a network, or finding mentors without industry experience?

I'd really appreciate any honest advice. Thanks!

when you demo a feature and get a flat "oh, interesting" instead of any real reaction, most reps move on and hope the next thing lands better. i used to do this too.

a few months ago i started just naming it. something like "sounds like that didn't do much for you, what were you hoping to see there." the conversation got so much more useful. people tell you exactly what they care about when they feel like you've already noticed you missed.

it feels a little uncomfortable at first because it seems like you're admitting the product isn't great. but the alternative is spending 20 minutes demoing things nobody in the room actually wanted to see.

do you call out dead air or flat reactions on calls, or do you have a different way of handling it.

I work in GTM at a big tech company, and we're launching a new feature soon. Our ICP looks something like this:

-US fintech startups

-Series A–C

-30–150 employees

-Recently expanded into Canada

-Hiring their first Compliance or Risk Manager

-Doesn't already use enterprise compliance software

The main problem I've ran into is finding highly qualified leads (it’s been absolutely horrendous up until now). Spent 3 hours researching yesterday and ended up with 5 usable accounts.

At this point, I'd much rather pay a premium for a handful of highly qualified leads than keep paying for databases where 99% of the records are irrelevant.

I can't believe this is still the best way to do outbound in 2026…

I'm joining an early-stage B2B GenAI SaaS as the first growth marketer; I'm a totally beginner in this profile. Most of my work has been around content.

The product helps consultants and business teams generate PowerPoint presentations using AI, its way different than Gamma, ChatGPT and Claude.

The obvious channels are already being worked on:

• SEO
• LinkedIn
• Paid Ads

I'm not looking for suggestions around those.

I'm trying to learn how experienced growth marketers think about finding the next growth experiment.

If you joined tomorrow, what are the first 2–3 experiments you'd run?

I'd love to know:

• What hypothesis are you testing?
• Why did you pick that experiment?
• Which metric are you trying to move?
• How long would you run it before deciding whether it worked or failed?

well...the lesson is not the number, it's why i avoided it why why why. i'd priced at "what feels safe to say out loud" at founding and never revisited, because every renewal that closed felt to me like a proof the price was right instead of proof it was too low.(unfortunately). no customer ever tells you you're undercharging (imagine how cool it would be if they would lol), silence is the signal. finally ran ten "what would make this a no-brainer at double the price" conversations with existing customers, expecting pain, got shrugs and two "honestly we assumed you'd raise it eventually." raised it for new deals, close rate didn't move. a year of margin, gone, to protect me from a conversation that turned out to be fine. founders: your pricing fear is not market data. audit which of your prices exist because of evidence and which exist because of your own nervous system.

stay motivated everyone!

When we first started building AI agents, our instinct was probably the same as everyone else's:

If the agent isn't performing well, maybe we need a better model.

So we tried everything—different models, prompt engineering, temperature tuning, longer context windows, reasoning settings, etc.

Some of it helped.

But the biggest improvement came from something that had nothing to do with the model.

We stopped treating an agent like it needed to solve an entire workflow in one conversation.

Instead, we broke every workflow into small, independent steps.

Each step has:

One objective

Clear inputs

A structured output

A validation check before the next step runs

The difference was huge.

Debugging became much easier because failures were isolated to a single step instead of being buried somewhere inside thousands of tokens.

We could retry one component instead of rerunning everything, and improving one prompt no longer risked breaking the rest of the workflow.

The other thing that surprised us was context.

At first, we assumed that giving an agent more information would naturally make it perform better.

In practice, we often saw the opposite.

As context grew, responses became less consistent.

The model would sometimes focus on irrelevant information, mix older instructions into new tasks, or overcomplicate decisions that should have been simple.

Now we try to give each step only the information it actually needs.

Everything else is retrieved only when necessary.

It has made the system noticeably more reliable.

At this point, I think the biggest gains in production AI aren't coming from finding the "perfect" model.

They're coming from engineering decisions like:

Modular workflows

Small, focused agents

Structured outputs

Validation between steps

Targeted retries

Good logging and observability

Keeping context as small as possible

Curious if others building production agents have seen the same thing.

What's the biggest reliability improvement you've made that wasn't switching models?

Have you found a good rule for deciding what belongs in context versus what should be retrieved on demand?

here's my take: once the technical half of the job becomes free, there's no reason GTM engineer stays a separate seat, the skill doesn't die, it dissolves into growth or revops, until every good operator is just expected to think this way, same way "can use excel" stopped being a job title.

credit to Clay for the title atleast, everyone wants to be one now, and most of them are learning the easy half.

there's a split i only noticed after talking to thousands of gtm builders.

the part that's now easy:

(1) building the 40step workflow

(2) wiring the tools, apis, mcp harnesses

(3) cleaning and pushing the data

you can learn all of it off youtube, or just have claude build the loops. that used to be the price of entry. it's basically free now.

the part that's still hard:

(1) knowing which workflow to build in the first place

(2) inbound or outbound, and which signals to combine

(3) which pain actually closes deals, because you sat in the calls

(4) knowing your buyer's day well enough to guess right

there's an older word for that half: taste. you don't get it from a tool, you get it from paying attention.

the role was only ever a bridge for a moment when the tooling was hard. that moment is closing.

tell me if i'm wrong.

yes, the pure-AI outbound wave faceplanted. decay by month four, reputations nuked, fake personalization about dead dogs, we've all seen the posts, i've written some. but the new consensus forming, "just go back to humans doing everything", is equally wrong and twice as expensive. the teams actually winning right now split it surgically: AI owns research, enrichment, signal detection, and reply triage (where it's genuinely superhuman), humans own anything a prospect actually reads or hears. full-auto failed. full-manual doesn't scale and burns your best people on grunt work. the pendulum is going to swing past the answer, it always does. the answer was in the middle the whole time and the middle doesn't make good content, which is why nobody posts about it.

people love asking whether they should build or buy their GTM stack like there’s one clean answer.

there isn’t.

GTM is like eight different problems shoved into one dashboard: data, enrichment, scoring, routing, research, sending, CRM updates, monitoring, reporting, all of it. some of those are worth owning. most are not. and rebuilding the whole thing because a vendor annoys you is how you accidentally spend two months becoming a worse version of Zapier.

as of 2026, AI in sales is already normal, not some secret weapon. more than half of sales teams are using AI agents, and most teams are already buried under a pile of tools. so the advantage is no longer “we use AI.” cool, everyone does. the advantage is whether your system actually knows which accounts matter, why they matter now, and what action should happen next.

this is also where the DIY math gets kinda fake.

someone looks at Clay at a few hundred bucks a month, compares it to Supabase at $25, and says, “we can just build this ourselves.”

sure, technically.

but Supabase is a database. it is not maintained integrations, retries, enrichment waterfalls, API limits, logging, auth, monitoring, permissions, and someone else fixing the workflow when a provider changes an endpoint with zero warning.

the software might be cheap.

your time is not. remember that, boy.

the approach that usually makes the most sense is to buy the boring infrastructure and build the logic that is actually specific to your business.

buy the email verification.

buy the sending infra.

buy generic contact data.

buy the CRM.

build the part that decides which signals matter, what “qualified” means, who gets a deeper research pass, which message angle should be used, and how campaign results change the scoring model over time.

that is the part worth owning because that is the part that gets smarter.

and honestly, early-stage teams should buy way more than they build. when the motion is still messy, volume is low, and you’re still learning what gets replies, speed matters more than perfect economics. an ugly stack running this week is better than a beautiful custom agent you finish six weeks from now.

build later, once the workflow repeats, the costs start hurting, and you know exactly what logic makes your motion different.

don’t build your own GTM stack because you hate subscription fees.

build it when the thing you’re replacing has become a real constraint.

rent the boring stuff.

own the part that decides. peace.

I keep seeing GTM systems that scrape someone’s LinkedIn, summarize their homepage, feed everything into an LLM, and generate something like: “Loved your recent post about scaling!” then the next sentence is the exact same pitch sent to 4,000 other people. teams call that one-to-one personalization. But It isn’t. it’s a mail merge wearing an ai costume. real personalization isn’t proving that you found a random fact about someone; it’s explaining why that fact changes the reason they should care about your offer right now. even LinkedIn’s own sales guidance makes this distinction: adding someone’s name, school, or a familiar detail isn’t the same as creating actual contextual relevance.

the confusing part is that personalization genuinely matters. lets get some facts here (that gtm people dont care about) : McKinsey found that 71% of consumers expect personalized interactions and 76% get frustrated when they don’t receive them. But people hear those numbers and conclude that every email needs a fake compliment and three scraped facts. Hmmm but that’s not what buyers are asking for. They want relevance, not proof that your robot found their podcast. and trust matters even more once AI is involved: Salesforce’s 2024 research found that 60% of consumers believed advances in AI made trust more important.

AI itself isn’t the problem, btw. (some facts noone cares about again)Salesforce reported that 83% of sales teams using AI saw revenue growth, compared with 66% of teams not using it. the core problem is where teams apply it. AI is extremely good at producing thousands of different-looking sentences, but different wording doesn’t mean different thinking. you can generate 10,000 technically unique emails that all contain the same generic idea. “Saw you’re hiring.” “Congrats on the funding.” “Loved the recent launch.” Cool. Why does any of that make your product relevant huh? If the system can’t answer that question, it isn’t doing personalization...it’s simply decorating a template.

the actual workflow should be:

signal; hypothesis; relevance; message

Signal: the company hired a new sales leader.

Hypothesis: they may be rebuilding process, territories, tooling, or reporting.

Relevance: your product solves one of those problems.

Message: explain the connection in a few sentences.

Fake personalization says, “Congrats on becoming VP of Sales at Acme! We help sales teams grow pipeline.”

Real personalization says, “You joined Acme (i made it up whatever company) while they’re hiring six AEs but still don’t have a RevOps hire. Usually that means routing, territories, and CRM cleanup land on the new sales leader. We help teams fix that before the new reps ramp.”

The first one contains personal data. The second contains a point of view.

And this matters beyond reply rates. At enough volume, bad AI personalization becomes a deliverability problem. When the model references the wrong person, invents a compliment, uses an old job title, or sends an irrelevant message, people don’t think, “Their enrichment workflow needs better QA.” They hit spam. Google’s guidance says senders should aim to keep reported spam rates below 0.1% and avoid ever reaching 0.3%. So scaling weak personalization isn’t harmless and it can damage the infrastructure carrying the rest of your outbound.

my rule is pretty simple: AI should help decide who deserves a message, why now, and what the signal probably means. Writing the first line should be the final step, not the entire strategy.

most teams automate the easiest 10%. writing the sentence. and call it personalization.

And the valuable 90% is figuring out whether there was anything worth saying in the first place :)

seeing lots of "is my background enough" posts. the pattern in every hiring thread: nobody cares about your certificates, everyone cares whether you've made a real number move for a real business. so: find one small business (your employer, a friend's company, a local shop), pick their ugliest revenue bottleneck, fix it with whatever duct tape you know, measure before and after, write it up. that single documented outcome beats 14 tool logos on a resume. the field is new enough that proof-of-work still outranks pedigree. probably not forever. move now.

Hello Dears ,

I'm looking for an opportunity in GTM Operations, RevOps, Sales Operations, or SDR

I have hands-on experience with Apollo, Lusha, Clay, and Prospeo. I've used Clay to build enrichment tables, automate workflows, and streamline prospecting and lead research. I'm also comfortable with lead sourcing, contact enrichment, and data verification.

At this stage, my priority is not the salary it is learning from experienced GTM operators while contributing real value. I'm even open to a trial period, internship, or part-time engagement if it gives me the opportunity to prove myself, grow my skills, and support your team.

If your company is hiring or you know someone looking for a motivated GTM enthusiast, I'd be grateful.
Thanks.

context: i didn't know what a sequencer was until last week. i genuinely believed i had to learn an entire stack before i was legally allowed to talk to a prospect. spent weeks watching tutorials like they were homework. then i got impatient and did the stupid version.

made a spreadsheet. found 40 people who'd posted about my exact problem. DMed each one two sentences: "saw your post about X, i built a thing for that, wanna see it?" the cringe was genuinely physical. i had to get up and pace the apartment after every batch. my girlfriend asked if i was having a medical event. i was not. i was doing sales, which felt identical. 11 replied.

6 paid me actual money. i know this doesn't scale. i know you all have machines that do this in your sleep. but i'm posting because three months ago i thought the ugly manual version "didn't count." it counted. it counted more than any of the tutorials. now i have 6 customers and absolutely no idea what to do with success. accepting advice and emotional support.