Migma AI - AI runs your email marketing. Better with every send.
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Migma creates full campaigns before you ask. It understands your goals and events, creates every email, personalizes and localizes it, and renders consistently across inboxes. It handles sending, preferences and compliance, then learns what drives revenue.
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@carlos_mendez16 Yes! Connect a new domain and Migma warms it up automatically, so you ramp up sending without hurting your reputation.
Come try it for free!
Big congrats team. Love that it factors in the calendar, not just brand and audience. Timing is usually the part founders get wrong, so having that baked into the drafts is smart. Excited to see this one grow.
@ben_kahan Really appreciate that 🙏
Timing is one of those things that's easy to get wrong, so glad it's landing right.
Come take it for a spin
the render consistently across inboxes line is the part i'd test first. outlook has wrecked more of my campaigns than any bad subject line ever did. does it hold up in the older outlook desktop clients or mostly the modern web ones?
@terminal_candy Outlook is exactly the client we built hardest for 🙌 Zinn covers the old desktop versions too, not just the modern web ones, down to Outlook 2003.
Come put it through its paces.
This is more for DTC founders vs GTM teams yeah? Either way looks great
@lucasjpols Thanks 🙏 Honestly both.
The DTC use cases are obvious (Shopify, cart abandon, win-back), but it's not DTC-only. Adobe, Superhuman, and Cloudflare run on us, and none of those are DTC shops.
The core need is the same either way: get on-brand emails out that render right and actually convert, without a huge team.
DTC founders and GTM teams both feel that.
Give it a try on whatever you're sending and see.
@Boey ignoring opens because of Apple Mail's privacy proxy inflating them is the right call, a lot of tools still pretend that number is meaningful. leaning on click-weighted credit across the whole send history rather than trying to nail a single journey makes sense too - single-path attribution was always going to be noise dressed up as precision. makes me want to see what a/b testing two subject lines on the same day-3 email looks like in your dashboard, that's usually where the click data would actually change what someone sends next.
@omri_ben_shoham1 Exactly, you get it. An open you can't trust isn't a metric, it's decoration, and single-path attribution was always noise in a nice outfit.
And yeah, that day-3 subject line test is the exact case where the click data earns its keep. Run both versions on the same email, same segment, and you see which line actually pulled people to the next step, then that feeds what goes out next. That's the loop working the way it should.
Want to jump in and set one up? I'd genuinely like to see what you'd throw at it.
Building your own markup language is a bold move. At what point did you realize existing solutions weren't good enough instead of trying to work around them?
@reda_roqai_chaoui Thanks 🙏 It clicked when we tried the obvious path first: just have the AI write raw email HTML. It broke fast. Expensive to generate, and it still rendered wrong in Outlook and dark mode too often to trust.
That's when the choice got clear: keep patching AI-generated HTML forever, or give the AI a language actually built for it. Patching is a treadmill, every new client quirk is another band-aid and you never get off it.
So we built Zinn. I'll keep the how under wraps 😄 but once it just rendered right every time, going back to raw HTML was off the table. Working around the old way was honestly the harder road.
The drafts-the-campaign-before-you-ask angle from brand plus calendar is the part I would test for launch comms, where timing and segmentation matter more than the copy. When it builds the audience automatically, can I still hard-scope the segment — say only active community members versus a cold waitlist — or does it decide the audience for me? That control is usually the make-or-break for me before hitting send.
@hazy0 Yes, you stay in control, always. The auto-audience is a starting suggestion, not a decision it makes for you.
You can hard-scope it in plain words: "only active community members, exclude the cold waitlist," and it builds exactly that. Want to tighten or swap the segment after? Do it before send. And nothing goes out until you approve the final list, so you're never surprised by who it picked.
For launch comms that's exactly the point, Migma does the grunt work of assembling the segment, you keep the final call on who's actually in it.
Want to try scoping one and see if it matches what you'd pick by hand?
The 'scope in plain words' piece is what I most wanted confirmed — the failure mode I've seen in audience tools is having to translate a human concept (the people most likely to care about this update) into a filter stack. If the natural language scope is the actual query, the workflow clicks. The 'approve the final list before send' checkpoint is also right — trust goes up when there's a human gate, not a blackbox. Will try scoping a real launch segment and report back.
Congrats on the launch. The combination of Zinn plus real inbox and device previews feels like the more durable moat than generation alone, especially since the failure modes only appear after send. I am curious how the learning loop handles conflicting signals across localized versions: if a Spanish variant gets fewer clicks but higher downstream purchases, can the system learn toward revenue quality rather than optimizing the easiest top-of-funnel metric?
@wesc Clicks are actually the core signal, but not just a yes/no. We see how many people viewed it, how many times, how many clicks, and where they clicked on the email. That tells us what's pulling people and what's dead weight, way more than an open ever could.
Then your store data ties it to real orders. So on your Spanish variant, even with fewer total clicks, we can see people clicked the parts that matter and actually bought. Fewer clicks but better ones, and real revenue behind them, so it ranks higher.
Want to run a localized campaign and watch it read the click behavior on your own numbers?
the localization piece is what I'd poke at - Spain gets Spanish, Canada gets French or English, but does the consent model localize with it? GDPR wants a documented opt-in before you can send in the EU, CAN-SPAM is fine with opt-out after the fact in the US. if Migma is picking the send list and timing on its own, does it also know which legal basis applies per recipient, or is that still something the sender has to configure region by region themselves?
@galdayan Migma doesn't invent a legal basis for you. Consent lives with your list.
When you connect your store or ESP, the opt-in status comes with each contact, and Migma respects it: if someone isn't cleared to receive marketing in the EU, they don't get picked, no matter what the segment logic wants.
So Migma automates who and when inside the people you're already allowed to email. It doesn't override consent or reclassify a US opt-out contact as an EU opt-in one. The legal basis is still yours to hold, we just don't cross it.
Region-by-region rules engine that tags legal basis per recipient automatically isn't something I'd claim we do today.
@boeychen that makes sense, thanks for the clear answer. follow-up on the "opt-in comes with each contact" part - what happens when the same contact exists in two connected sources with conflicting status, say Shopify has them opted in but the ESP you synced from has them opted out. does Migma default to the more restrictive one automatically, or does it surface the conflict for a human to resolve before that contact gets touched at all?
Curious how Migma AI handles the analytics side mentioned in the topic tags. Is the focus mostly on generating and launching email campaigns, or does it also help read performance after the send and suggest the next campaign? Email marketing gets much more useful when creation and learning from results sit close together.
@ivory_xuxuxu Both, and you're right that they're useless apart 🙌 Creation and learning are the same loop for us, not two features.
After a send, Migma reads what actually happened: which emails, subject lines, links, and segments pulled, with the bot and fake-open noise filtered out so the numbers are real. Then it uses that to suggest what should go out next, and each new campaign starts smarter than the last because it's building on your own results.
That's the part most tools drop, they stop caring the second you hit send. For us the send is where the useful part starts. Want to run one and see what it tells you to do next?