r/ClaudeGTM

Every morning, same ritual. Open the CRM. Select all. Copy. Open Claude. Paste. "Ok who do I chase today."

Embarrassing? A bit. But it worked better than the CRM ever did. It caught a deal going cold that I would have missed. It remembered when a champion switched jobs. Better read on my pipeline than any view I ever set up, and I've set up a lot of them.

The problem was the context died with the tab every night. Next morning, rebuild it by hand. At some point I did the math and I was spending 40+ minutes a day being a data pipe between two windows. Not selling. Not thinking. Shuttling.

That's when it clicked. The thinking was already happening in the chat. The CRM was just where the data slept at night. So why does it live anywhere else?

We're building the fix now. A CRM that lives entirely inside Claude over MCP. No app, no tabs. You say what happened on the call and it moves the deal, updates the contact, logs the touch. The conversation is the CRM.

It's for founders and teams who are in Claude all day anyway.

What's the first CRM chore you'd hand over if Claude could actually write to your pipeline?

Small team in Stockholm. We think the thing that finally kills legacy CRMs isn't a nicer dashboard. It's no dashboard.

We parse structured data out of job descriptions — around 20,000 JDs/day pulled from LinkedIn. Claude Opus does it beautifully, but at ~₹2.20 (~$0.026) per job that's ~₹44K/day, roughly $16K/month. Our unit economics just can't carry that.

What we've tried so far:

• Opus: quality is exactly what we need, but the cost is a dealbreaker at this volume.
• Sonnet: cheaper, but extraction quality drops noticeably on messier, unstructured JDs.
• DeepSeek: much cheaper again, but same story: quality isn't where Opus is.

So we're stuck between "great and unaffordable" and "affordable and not good enough."

For anyone who's run a narrow, high-volume extraction task in production:

• How did you bring the cost down without tanking quality?
• Did fine-tuning a small open model (Llama / Qwen, etc.) actually close the gap to Opus?

This is r/AskGTM , a place for people working on go-to-market. You can ask questions, share what is working, post problems, compare notes, talk about careers, or just write the thing you cannot say on LinkedIn.

What go-to-market means

Go-to-market is the work of getting a product into the hands of the right customers and turning that into revenue. It includes who you sell to, how you reach them, what you say, how you sell, how you retain them, and how the whole system gets better over time.

It is not just outbound. It is not just sales. It is the full path from market to customer to revenue.

Founder go-to-market

For founders, go-to-market usually starts with doing the work yourself. Finding the first customers, choosing an ICP, picking a sales motion, writing the first emails, doing calls, handling demos, closing deals, and learning what people actually care about.

This is the place for questions about first customers, positioning, pricing, distribution, founder-led sales, when to hire, and how to know if a channel is working.

Sales

Sales is a big part of go-to-market. SDRs, AEs, AMs, founders, and sales leaders can talk about prospecting, cold email, cold calls, LinkedIn, sequences, discovery, qualification, MEDDIC, BANT, demos, objections, negotiation, closing, pipeline, quota, and comp.

The small details matter. A bad list kills a good email. A weak discovery call kills the demo. We will circle back usually means something else is broken.

Marketing and growth

Marketing and growth are also go-to-market. Inbound, content, SEO, demand gen, community, brand, product-led growth, ABM, paid, events, and lifecycle all belong here.

This is for the people trying to create demand, explain the product clearly, bring the right people in, and make sales easier before a call ever happens.

Data, signals, and outbound infrastructure

A lot of go-to-market comes down to knowing who to contact and when. That means segmentation, account selection, list building, scraping, verification, enrichment, buying signals, intent, funding, hiring, expansion, tech changes, job posts, and other triggers.

It also means deliverability. Domains, inboxes, warmup, sending volume, bounces, spam, reply rates, and why your emails are not landing.

RevOps and CRM

RevOps is the part behind the scenes that decides whether the team can see what is happening. CRM hygiene, routing, territories, reporting, attribution, forecasting, pipeline stages, handoffs, data quality, and dashboards all matter.

Bad ops makes good teams look confused. Good ops makes problems visible.

GTM engineering

GTM engineering is the newer technical side of go-to-market. It sits between revenue, data, tools, and code.

People here are building enrichment systems, replacing expensive tools, wiring APIs, scraping data, monitoring signals, cleaning lists, building internal tools, using AI coding agents, and testing whether AI SDRs are useful or just noisy. The role is new enough that some job posts ask for ten years of experience in something that barely existed two years ago.

Post-sale

Go-to-market does not stop when a deal closes. Onboarding, customer success, support, adoption, retention, expansion, renewals, referrals, and net revenue retention are part of the same system.

A bad-fit customer is often created before the contract is signed. Expansion often starts with selling the right thing the first time.

Partnerships and channel

Partnerships, agencies, resellers, affiliates, marketplaces, integrations, and channel deals are part of go-to-market too.

This is where people can talk about partner-sourced pipeline, rev share, co-selling, channel conflict, attribution, enablement, and whether the partnership is real or just two logos on a slide.

AI in go-to-market

AI now touches almost every part of the work. It can research accounts, write drafts, summarize calls, update notes, monitor signals, build tools, enrich data, and act like a 24/7 coworker.

It can also make teams faster at doing dumb things. Five hundred lazy sequences are still worse than fifty thoughtful ones. Deleting the CRM and telling an agent what happened each day might be the future, or it might be chaos. Worth discussing.

Careers, comp, and hiring

Go-to-market is also a career path. Breaking in, BDR to SDR to AE, moving into RevOps, becoming a GTM engineer, switching into marketing, joining an agency, getting laid off, surviving a bad market, interviewing, negotiating comp, and sharing real numbers all belong here.

Post the wins too. First deal, biggest deal, first commission check, new job, better title, clean dashboard, fixed deliverability, first customer, whatever.

Post here

Ask what you are stuck on. Share what worked. Share what failed. Post the messy version.

Use the closest flair so the right people find it: Founder GTM, Sales, Marketing/Growth, RevOps, GTM Engineering, Post-Sale, Partnerships, AI, Careers, Comp, Hiring, or Beginner.

I'll give you the whole system. But I'm going to lead with the thing the volume-flexing posts leave out, because it's the only thing that determines whether any of this works: in 2026, deliverability gates everything and generic copy is worthless, the only part of the message that still moves the needle is relevance, and all of it sits downstream of getting into the inbox at all. You can automate every step below and still send 40K emails a month straight into spam if you get the infrastructure wrong. So I'm building this around what actually moves the number, not what's fun to automate.

I came up doing outbound by hand at an agency. Now I run it mostly solo with Claude Code agents doing the grunt work. The mental model that made it click: outbound is a chain of steps, each step is a skill, each skill calls a few agents, and the whole thing lives in one plugin I can point at any new client. Here's the chain.

Phase 1: Infrastructure, and the part that actually matters. When a client pays and finishes onboarding, an agent provisions domains, spins up inboxes, and starts warmup. Domains on Namecheap, DNS on Cloudflare, inboxes on Google Workspace and Microsoft 365.

Here's what most people automate wrong. They blast from day one. The 2026 reality is brutal and non-negotiable: warmup runs a minimum of 3 weeks, you start at 5-10 sends per inbox per day and ramp over 4-6 weeks, and the deliverability-safe ceiling is 40-50 cold emails per inbox per day, not the 100+ the old playbooks promised. Push past that on a fresh domain and you trip volume-spike detection. So "10-40K a month" isn't one heroic inbox, it's the math of many inboxes each sending a safe 40, and your agent's real job is orchestrating that spread without any single mailbox spiking.

One thing I added this year that paid off immediately: ESP matching. Route Google-to-Google and Microsoft-to-Microsoft wherever possible. Cross-server sending (Google inbox to a Microsoft recipient) raises filter sensitivity, and a 60/40 Workspace-to-365 inbox pool gives the best aggregate placement across mixed B2B lists. Small thing, measurable lift.

Phase 2: Offer research. Agents trained on offer fundamentals generate a batch of direct offers, guarantees, and lead-magnet angles on day one. I use a scraping layer (FireCrawl plus Brave Search plus residential proxies) to pull competitor sites and similar pages so the offers are grounded in what's actually running in the space, not invented in a vacuum. The goal of this phase is just maximum context on the company before a single line of copy gets written.

Phase 3: TAM mapping, the one place I refuse to fully automate. If Apollo is your only database, that's a problem, you're fishing the same pond as everyone emailing your prospect. I start broad, find the obvious companies, then loop on lookalike expansion until no new relevant companies surface. But a Growth Manager kicks this off and stays in the loop, because every so often a client has a genuinely weird TAM that breaks the standard pattern, and an agent confidently mapping the wrong universe is how you waste a whole month. Agents handle the tool calls; a human still owns the judgment.

Lead list and enrichment. Identify companies first, then enrich. For email finding I waterfall across multiple sources (Apollo, Prospeo, and a couple others) rather than trusting one, then verify internally. This is where the Clay bill died, by the way. Once the enrichment and waterfall logic lives in your own agent calling the APIs directly, the $350/mo abstraction layer stops earning its keep. Worth saying plainly though: this only pencils out at real volume across multiple clients. If you're running one campaign a month, just pay for Clay, your time is worth more than the rebuild.

The verification step is doing more work than your copy. Set an auto-pause at a 2% bounce rate and target spam complaints under 0.1%, not the 0.3% Google publicly allows. By the time you hit 0.3% the reputation systems are already suppressing you. A clean list isn't hygiene, it's the highest-leverage thing in the whole operation, and it sits one phase before anyone argues about subject lines.

Campaign strategy and copy. I start with ~5 near-identical campaigns plus 1-2 genuinely different angles, so I'm testing real variation, not cosmetic tweaks. A copywriting skill drafts against a knowledge base of what's worked before. Two data-backed constraints I hard-code: keep emails under ~80 words (short, plain-text, conversational beats long pitches in every 2026 benchmark) and cap sequences at 3-4 emails, because spam complaints more than triple by the fourth email. Longer sequences don't add pipeline, they add reputation damage.

A warning on the AI-copy part, because this is the 2026 trap nobody flexing volume wants to admit: the filters now read content, not just headers, and inboxes are flooded with copy generated by the same models off the same prompts. Generic AI output creates its own detectable pattern. Spintax helps only if the variation touches sentence structure and order of ideas, not "Hey" swapped for "Hi." If your 40K emails all share a model's fingerprint, volume just means you get pattern-flagged faster. The teams winning don't win on clever copy, they win on relevance, the right message to the right account at the right moment. That's the one piece of the message worth your attention. Everything else about copy is just avoiding the spam filter.

Daily analytics and the campaign analyzer. A skill summarizes performance daily. The one I'm still building, and the one I think matters most, analyzes performance biweekly and tries to explain why a campaign underperformed. The bet is that the myths we all carry (long vs short, weird subject lines, send times) are testable, and over enough volume the patterns surface and the analyzer can start killing styles that don't work. This is the piece that turns a sending machine into a learning one.

The honest through-line. Almost everyone optimizing outbound is optimizing the wrong half. They obsess over clever copy and automate sending, when in 2026 the leverage is the reverse: deliverability and list quality decide whether you're in the inbox at all, and once you're there, relevance is the only thing about the message that moves a reply. Polished copy that isn't relevant is just decoration on an email nobody asked for. Automate the infrastructure ruthlessly. Keep a human on TAM judgment. And treat the campaign analyzer, not the send volume, as the actual asset.

We've built a skill that helps you build lists along with enrichment (finding emails, firmographics, Linkedin data, etc) natively inside Claude.

The idea is how can we make it as easy as possible to people to do this where they are already working vs going to Apollo, Clay etc to get the data.

Curious to hear if other people have faced a similar annoyance. We have free credits for a real use case if people want to try it out!

How long do you think it will take for Anthropic to restore Claude fable 5 access to US customers?

How about international access?

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