r/ClaudeGTM

A16Z, YC, Sequoia, my own newsletter list, the whole portfolio map.

Each one ends up as a CSV with 3000 to 5000 contacts, scored, enriched, with a "why this person" column and an outreach draft column.

Every CRM I've tried wants me to push the raw list in and then live inside their UI. I don't want that. I don't want my scraped database to live in HubSpot. I don't want to pay Clay a thousand a month to move columns around.

Attio is close but it's not Claude-native and I do most of my actual work in the terminal.

Right now my workflow is: scrape into SQLite, push to Google Sheets, run Claude Code against the Sheet to enrich and score, then push the scored output to my sequencer.

Sheets is functionally my CRM because it's the only thing that round-trips cleanly with my terminal. It works. It also feels like I'm two years away from the right tool existing.

Claude Code natives. How are you running this?

Are you also bouncing between Sheets via (gws CLI), your own scripts, and a sequencer?

Has anyone found something that's actually local-first and agent-native without being a sales-team CRM in disguise

My girlfriend works in marketing and runs the social for a couple of small brands plus our own. Since Claude Design launched about a month ago her workflow has shifted to being mostly Claude-driven, and the way the stack ended up sitting together has some lessons I think are on-topic enough for this sub to write up. Posting this partly to share, partly because I want to hear what other people running GTM through Claude have figured out.

Claude Design is doing more than I expected it to.

Honestly the thing that changed her workflow most isn't a clever orchestration trick, it's just that Claude Design got good enough to handle real brand-quality output. If you've used it for social you probably already know this, but for anyone who hasn't tried it on actual brand work, the trick is being aggressive about the brand context up front. She loads a small brand voice document, the exact hex codes, the font, the tone reference (formal/playful/scrappy), and a one-line description of the post structure she wants (3-slide intro-meat-cta, single-image hook, etc). With that context it sticks to brand pretty well across iterations. Without that context it drifts toward generic SaaS-pastel pretty fast.

She prompts Claude Design, iterates 2-4 rounds inside the chat to tighten the copy and layout, and ends up with a finished design in HTML. For an Instagram carousel that used to mean a Canva session and a day of fiddling. Now it's 20 minutes.

The export gap is real and it's where I lost evenings.

Claude Design's output is HTML. The "send to Canva" button is the only export path, it requires a paid Canva account, and from our Anthropic account it just doesn't work. Click the button, nothing happens. So for a while the workflow was: she'd finish the design, send me the HTML zip, and I'd manually convert it to PNGs using a Puppeteer script Claude Code had helped me write. 10-15 minutes per post, all on me, mostly at the wrong time of day.

After a couple of weeks of that I built a small tool so she could do the conversion herself (called it TryRenda, link in comments if anyone wants it. Not going into it here, it's not the interesting part). The interesting part is what it freed up: she stopped batching designs to send to me, started iterating on individual posts in real-time, and the volume of stuff she could ship roughly doubled. The bottleneck wasn't design speed, it was the manual hand-off.

Where I tried to push AI further and it didn't work.

This is the part I'd actually like input on from this sub.

I tried to AI-assist more of the workflow beyond the asset step. Specifically:

1. Reply drafting for comments and DMs. I'd take the source thread, pass it through Claude with a "here's the context, here's what we'd want to convey, draft a reply that sounds like a real person" prompt. The drafts were grammatically perfect and contextually accurate. They also sounded like marketing. Every single one. We sent a handful early on and engagement dropped immediately. Fewer follow-up replies, more "this feels like a bot" responses. We switched back to her writing them herself and the numbers recovered.
2. Outbound DMs. Same pattern. Even when the model had the recipient's profile and the angle was relevant, the message read as templated to a human reader. The signal that gets through on social DMs is "specific person responded specifically to me" and that signal collapses the moment a model writes it.

So the line we've ended up drawing: AI is great on asset creation (design, copy variants, sizing, repetitive transforms). AI is currently bad at anything where the recipient is consciously or unconsciously evaluating whether the sender is a real human. We assumed this line would soften over time and it just hasn't, at least not for our use cases.

Where I'd put a Claude agent next, if I were building it.

The gap I keep wishing for: an agent that takes one master design from Claude Design and emits N hook variants automatically. Same layout, same brand, 5 different opening lines for A/B testing. Right now she does this manually by iterating inside Claude Design 5 times. It's clearly automatable; I just haven't built it because the manual version is 10 minutes and not painful enough yet.

Curious what other people running social or GTM with Claude have found. Especially:

• Anyone get AI-drafted replies/DMs to actually land? What did you do that I didn't?
• Anyone built an asset-variant generator on top of Claude Design? Are you happy with it?
• Where's the next thing you'd automate in your stack?

I replied to a thread the other day about AI coworkers running 24/7 and realised it is pretty close to the thing I have been trying to run, just from a different angle.

I don't really think of it as a coworker though. That framing makes it sound like a little employee waking up and deciding what to do. I don't want that, at least not for client work where mistakes cost money.

What I want is simpler: every client becomes readable by AI.

Each client has their own folder. Emails, meeting transcripts, call recordings, offer docs, pricing, website content, CRM notes, tracking notes, ad account data, conversion data, previous tests, all of it lives in one place. Most of it is pulled in automatically through n8n, Codex automations, or whatever connector makes sense for that client.

The folder structure matters more than I expected. Same rough layout across clients, same naming conventions, same instruction files, same connection notes. When I open a client folder in Claude Code or Codex, the model is not starting from a blank chat. It can read the business first.

The repeatable work becomes small workflows.

I don't mean some grand agent framework. I mean boring jobs I have done enough times that they deserve their own instructions and scripts.

Search term review. Tracking audit. Daily account check. Broken conversion handoff check. Meeting transcript into open actions. Drafting ad copy against the actual landing page. Looking at CRM lead quality before trusting what the ad platform says.

That is the part that compounds. If I improve the tracking audit once, I can run a better version of it across every client. If a weird edge case comes up in one account, it usually becomes a note or rule I can reuse somewhere else later.

I trust schedules more than wake-up-and-decide agents.

I tried the version where an agent wakes up, looks around, and decides what matters. It sounds cool. In practice I don't really trust it that much yet (give it 6 months tbh).

Most of the useful stuff in my setup runs on a fixed cadence. Morning account checks. Weekly search term reviews. Monthly reporting passes. Tuesday and Thursday deeper account work. Some of it runs through Codex automations, some of it through n8n, some of it is still me manually kicking off the workflow.

The point is that the agent is not the router. I am. The agent does the read work, runs the checks, drafts the output, and tells me what deserves attention.

My alerts are mostly email and Telegram, not Slack. Daily account summaries go to my inbox. Telegram is useful when I want a quick pulse or to trigger something from my phone. If I need detail, I open the folder.

Tools are mostly APIs and files.

Google Ads API, Meta Marketing API, GA4, Search Console, Tag Manager, GHL, website repos, CMS data, spreadsheets, whatever the client actually uses. GHL handles a lot of the CRM side. n8n handles deterministic pipes. Claude Code and Codex sit on top when the task needs reasoning or code.

I have become pretty allergic to adding another SaaS dashboard just because it has AI in the name. Every tool between me and the source data is another layer making decisions for me. Sometimes that is worth it. Most of the time I would rather connect to the API directly and have the model work from the raw context.

Writes stay gated.

This is the part I think people underplay when they talk about autonomous agents.

Budget changes, paused campaigns, negative keywords, CRM writes, conversion settings, website deploys, anything that changes state or can cost the client money. The model can draft, stage, queue, explain. I still review before it goes live.

That is not me being scared of automation. It is just the only version that survives contact with real accounts, platform policies, messy tracking, delayed conversion data, and clients who understandably do not want an agent freelancing inside their business.

I stopped trying to build a dashboard.

I had the instinct to make one. Nice overview, all clients, all tasks, agent activity, source health, the whole thing.

Then I realised I barely wanted to look at it.

The folder is the view. The morning emails tell me what needs attention. Telegram gives me a quick pulse when I need it. If something looks off, I open the relevant client folder and inspect the files, logs, and outputs. A dashboard would mostly become another thing I have to maintain.

So the version I am aiming for is less "AI employee running around 24/7" and more "the business is structured enough that AI can read it and help operate it."

For services work, that is already extremely useful. I don't need the model to decide my whole day. I need it to keep the client context current, run the boring checks, find the weird stuff faster than I would manually, and draft the next thing I should review.

Curious if anyone else is building it from this angle, especially for client/services work rather than a product. What does your client folder or context layer look like, and where do you draw the line on approvals?