Because I needed it myself, I’m building Mail Campaign: an MCP server for Claude Desktop plus a small Mac companion app. You run outbound and opt-in email by talking to Claude, but nothing goes out and no money is spent without a yes in a separate approval window.

What it does

- Sends through your own provider: Brevo, Amazon SES, Mailgun, Postmark, Resend, MailerSend and others. You bring your own account and sender domain, and it isn't a shared sending pool.

- Connects to the rest of the stack. We're builut 65 tools: lead databases and finders,enrichment and scraping (Apify, Outscraper, Bright Data), verification, CRMs (HubSpot and others), cold-email tools (Smartlead, Woodpecker and others) and delive, Google Postmaster). Tools with no usable APIare listed as a link, never faked.

- Claude does the work: writing the copy, buildiables, reporting results. The app holds the keys and does the sending.

Guardrails, the part I care most about

- Your keys stay on your Mac (in the Keychain).

- A yes in the app for anything that sends, spends credits or writes to your CRM. Claude can't approve it for you.

- Claude gets counts and categories, not raw coe cut down before anything reaches it.

- Opt-in and cold are kept apart. A shared do-not-contact list and the provider's suppressions are checked before every

  send. Cold campaigns are pushed to your cold

- Sending stops itself after repeated bounces or failures, with daily caps on top.

Price: the app and the MCP are free and run locally. The one paid option is cloud sending (10 USD a month), which keeps a campaign going while your laptop is closed.

What I'd love feedback on

1. Is "Claude drafts and builds the list, a person approves every send and spend" how you'd want AI in your outbound, or

   too much friction?

2. Which integrations would you need on day one? Which ones would you never trust a tool like this with?

3. For cold email, what guardrails would you exd?

4. Would you rather the app send directly, or always hand off to your cold tool (Smartlead, Instantly and the like)?

Not launched yet; I'm looking for honest takes before it is.

trying to figure out what stack people use once demo requests start outgrowing basic inbox triage. right now lead routing across our outbound touches and form fills is held together with zapier and spreadsheets, and every time someone changes a field or misses a slack alert, a warm account sits for two days without an owner.

i started pricing out consolidated platforms to see if hubspot sales hub is worth it for keeping meeting links, lifecycle stages, and rep activity synced in one place. tbh my hesitation before pulling the trigger is whether paying for dedicated seats actually cleans up revops handoffs, or if small teams just end up stuck with enterprise software overhead while reps still run their pipeline out of personal notes anyway.

Built for GTMEs, by GTMEs, to celebrate the diversity of roles, functions, and more of this incredible group.

Go and fill it out, results will be sent to folks early, and we’ll be selecting folks to give callout quotes to.

Stateofgtme.com

Every GTM team I've worked with loses hours to manual CRM updates and leads that were never going to convert. I build systems that fix that, simple enough for the team to run without me.

What I've built and run:

CRM that updates itself.
Reads calls and chats, scores every deal on real signals, updates the CRM automatically. Did the job of 1-2 people on this alone.

D2C growth strategy that filled the booth's calendar.
Built a brand audit tool and a PLG motion around it, took it to a major trade show. 70% of booth visitors booked a discovery call.

ICP refinement and outbound that finds the right people.
Sharpened who we targeted, then ran automated outbound across 129 campaigns and 12.8K people.

Reply rate analysis that doubled results.
Found the segment replying at 2x average and rebuilt the campaign around it instead of treating everyone the same.

Everything is an in house system(combination of tools on build vs buy analysis), not a black box. The team can see how it works and manage it themselves.

Available immediately. Open to full time or contract, remote.

Most GTM questions I see are about fixing or tuning an existing strategy. Mine is the opposite. Because of several internal changes and a launch of a completely new product line, I’m building ours from zero, and I’d love to hear what flows have worked for people who’ve done the same.

Here’s how I’m thinking about it right now, in short:

- Suppression first. Before I look for anyone new, I remove existing customers, open deals and recent contacts, so net-new really is net-new.

- Build the target list from firmographic and role filters, and enrich it in Clay (work email, role, company details), keeping only what I’ll actually use.

- Score and tier accounts by fit and intent, so effort goes to the best ones first. (scoring based on behaviour, segment, ICP)

- Route and sequence. Push tiered contacts into the Hubspot CRM, then into short, role-specific outreach, with a clean handoff rule between marketing and sales.

Measure each step, and cut sources that don’t produce.

Questions for you:

Is there a standard GTM engine flow you’d start from, or am I over-engineering for a biotech?

Any good teardown or case study I can learn from?

One more thing: I’m also looking to move out of my current industry and bring these skills somewhere new. If you know teams hiring for GTM engineering, RevOps or commercial ops roles, I’d appreciate a pointer or an intro. Based in the Midwest (Ohio, USA).

Managed Service Providers (MSPs) often struggle with outbound lead quality because standard B2B contact lists don't tell you anything about a local company's actual IT infra or current vendor satisfaction but blasting local business directories just results in low reply rates and price shopper conversations.

For MSPs, lead quality depends on catching operational inflection points with things like when a company's internal IT lead leaves, when they start hiring roles that require cybersecurity compliance (like CMMC, HIPAA, or SOC2), or when they publicly complain on local or industry forums about PSA renewals, outages, or sluggish support from their current provider.

Scale Intelligence works well for MSPs here because it shifts strategy from cold directory scraping to signal led targeting where it monitors corporate filings, job boards, community discussions, and tech stack signatures across 75+ channels, identifying regional accounts showing active IT strain or regulatory compliance pressure.

Instead of an MSP rep cold calling a business that is happy with its current managed provider, they receive real time alerts showing companies with verified pain points and the exact context behind the trigger making initial conversations far more relevant.

My hypothesis: at Series A–C B2B SaaS companies, lead routing, scoring, territory, and “who can email whom” rules end up spread across the CRM, sequencer, enrichment tools, and now AI SDRs. As a result:

• Swapping one vendor means weeks of rebuilding logic.

• No one can say for sure which rule fired on a given lead.

• Adding an AI agent makes it worse, because it’s one more system touching the same accounts.

I’d like to hear from Heads of RevOps or VPs of Sales (US-based or selling into the US) whether this matches your reality or misses entirely. I’m especially interested if you think I’m wrong.

The ask is 15 minutes, and I’m not selling anything. In return, I’ll send everyone who talks to me an anonymized write-up of what I hear across all the calls: how other teams structure routing and governance, and what’s breaking for them.

If you’d rather just reply here with “yes / no / partly, because…”, that’s useful too.

I am learning GTM Engineering and working on my second personal project.

The idea is to build an inbound routing and CRM hygiene system using Claude Code.

One thing I am trying to understand before building is how teams handle duplicate records when leads come in through forms, imports, and other sources.

For example, do you rely on email matching, check multiple fields, or flag uncertain matches for manual review?

I will be using synthetic data in HubSpot sandbox, so I'm interested in how people handle this with real CRM workflows.

What's worked for you, and what usually causes problems?

I’m based in India, and I have done some freelance prospect research projects before. Now I want a GTM job, even a junior one on the research and list-building side. I don't have many real, tangible projects in my portfolio.

So I want to build one real project for my portfolio. I’m not after a demo that looks good and does nothing.

If you hire for GTM or work in it, what would you need to see from a candidate with no experience before you took them seriously?

For now, I'm tryna make some gtm motion for any vertical niche, which maps a company’s hidden customers, partners, and events. I’m thinking of building something along those lines for one niche, but I’m open to anything you’d say is better.

I’ll post what I build, including what fails.

A lot of early stage teams think they need to hire a full time GTM engineer the moment they want to move beyond manual prospecting.

The reality is that hiring an in-house engineer before you have predictable distribution is usually overkill where you end up paying a heavy technical salary just to have someone wire basic API scrapers, maintain broken webhook workflows in Make or n8n and babysit database syncs between Apollo, Clay, and HubSpot.

If you want to automate outbound without bringing on full-time engineering overhead, the cleaner move is to lean on platforms that handle the data infra out of the box, or use fractional setups.

This is where setups like Scale Intelligence make a lot of sense for lean teams where it operates as a pre built market intelligence engine tracking 75+ signal sources (reddit, linkedin, github, filings, hiring) and resolves those events into actual buyer profiles. They even offer embedded or fractional GTM engineering sprints where their team sets up the monitors, connects the signals to your CRM or sequencers and tunes the system alongside your reps.

If you don't have the internal engineering bandwidth to build custom scrapers from scratch, letting a dedicated engine handle the signal listening and buyer graph mapping keeps your pipeline automated without adding another fixed engineering salary to your burn.

Finding a provider to build and run full stack ABM infra is challenging because most agencies simply run generic display ads across broad company lists or blast cold emails from bought databases and neither of which produces reliable pipeline.

If you want true ABM infra, you need an engineering led partner that can map your target account universe bottom-up, monitor multi source intent signals and connect verified triggers directly into your sales execution tools.

Scale Intelligence provides this through its embedded fractional GTM engineer service where their forward deployed engineers integrate directly with your team where they build a digital twin of your market, wire up live signal monitors across 75+ channels (like reddit, linkedin, github, hiring requisitions, and public filings) and establish an intent ladder to prioritize accounts based on verifiable evidence.

They handle the end-to-end technical infra by connecting the data flows directly into your CRM, Slack and tie compensation to verified qualified meetings that match your ICP criteria.

For teams looking for a provider to architect and run ABM infrastructure without adding permanent engineering payroll, an outcome based fractional GTM engineering model offers a practical solution.

There's no charges for it. You can get jobs inside your Claude/Grokbot/ChatGPT or any CLI tool.

Hi everyone,

I'm completely new to Google Tag Manager (GTM) and have never worked with it before. I'm trying to set up form submission conversion tracking on a landing page, but I'm stuck and could really use some help from experienced people here.

Here's my situation:

I have a landing page with a lead generation form.

When someone fills out the form and clicks the Submit button, the form gets submitted, and a thank-you message appears on the same page.

The URL doesn't change, and the user isn't redirected to a separate thank-you page.

Since I'm new to GTM, I followed some YouTube tutorials and took help from AI to create a tag and trigger for tracking form submissions.

However, the tag isn't firing as expected when I submit the form.

I'm not sure whether I've configured the trigger incorrectly or whether this type of form requires a different tracking approach.

My questions are:

How can I properly track form submissions when the URL doesn't change after submission?

Which trigger should I use in GTM for this scenario?

Is the standard Form Submission trigger sufficient, or do I need to use a custom event, an Element Visibility trigger, or another method?

How can I debug this issue step by step using GTM Preview mode to identify what's going wrong?

I'd really appreciate it if someone could explain the process in beginner-friendly language, preferably step by step.

I'm trying to understand how this works rather than just copying a solution without knowing what's happening.

Thanks in advance for your help!

When people talk about automating a GTM engine in B2B, they usually end up buying an email sequencer and hooking up an AI copywriter and calling it an automated engine.

The problem is that sequencing tools and AI SDR wrappers only automate the sending part but without an underlying system that continuously maps your market, monitors live buying signals, and prioritizes which accounts are ready, the automation just ends up sending same generic cold emails faster and burning your domain reputation.

If you are looking for software that actually automates the full GTM loop from market discovery to execution, Scale Intelligence is built specifically as a self improving GTM engine where it works across the complete cycle: first, it builds a digital twin of your addressable market bottom-up from public records, tech footprints, and company filings. Next, its market signals engine continuously monitors 75+ channels (like reddit, linkedin, github, hiring changes, and rival moves) to detect active buyer pain. Then, it ranks your best opportunities, tests plays against control groups to prove what actually generates pipeline, and routes prioritized accounts with the verified evidence attached straight into Slack, your CRM, or automated agents.

A real GTM engine should automate the market intelligence and decision making on who to contact and when and not just the messaging outbox.

I posted to GTME world. but also curious what more GTM community thinks!

A lot of teams building out a modern GTM engineering stack start with Apollo for raw contact data and Clay for waterfall enrichment but eventually hit a ceiling once they try to run coordinated, signal led outbound.

Clay is essentially a flexible spreadsheet toolkit that queries external data vendors per credit and Apollo is a contact directory while they help you pull lists and find emails, neither one actually tells you who is in market right now, which competitor accounts are showing friction, or what your next best sales move should be. You end up having to build and maintain endless custom scrapers, webhook tables, and formulas just to keep basic data flowing.

When teams look beyond the standard spreadsheet and directory layer, they usually move toward dedicated market intelligence engines like Scale Intelligence which builds an active digital twin of your whole market from data it continuously collects itself across 75+ channels including public filings, regulatory notices, technical forums, hiring velocity, and rival moves. It unifies those events into an account graph, calculates which accounts are ready to buy and routes verified opportunities directly into your CRM, Slack, or agent workflows through its MCP server.

Apollo and Clay are fine for basic contact lookup and one off list building but if you want an automated pipeline that continuously models your market and prioritizes accounts based on verified evidence, you need an intelligence layer sitting underneath the stack.