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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"Better with every send" implies real learning loop, not just A/B testing dressed up — curious what's actually feeding that improvement. Is it optimizing on opens/clicks per send, or does it get smarter about audience segmentation and timing over a longer horizon? That's usually the difference between a marginal optimizer and something that meaningfully changes strategy over time.
Migma AI
@abhineetarora Good distinction to draw.
Each send adjusts things right away based on opens and clicks. Over time it also gets better at segmenting your audience and picking the right timing, so the strategy itself improves, not just one campaign at a time.
Come run a few cycles and watch it move.
"Creates before you ask" is the real pitch — most email tools wait for you to know what to say.
Migma AI
@ringo_td5 Love how you put that 🙏 That's exactly the shift we're going for, Migma doesn't wait around for a brief.
Come see what it drafts for you
Trovio For Brands
I love this – congrats on the launch. What signal does it actually learn from: opens, clicks, replies, or revenue?
Migma AI
@derrickshowers Thanks so much! It's not just one signal, we look at a mix of engagement data, opens, clicks, heatmaps, and how people actually interact with the email, not just a single number.
Come give it a try for free.
Migma AI
@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.
Migma AI
@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?
Migma AI
@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
Migma AI
@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.
Migma AI
@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?
Migma AI
@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.
Migma AI
@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.