Transform customer signals into executable specs. Bridge the gap between raw feedback and shipping-ready requirements with AI.
This is the 2nd launch from mia. View more
mia
Launched this week
Allowing product managers to turn their customer signals into ship-ready requirements for AI to develop.
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Launch Team / Built With
Perplexity
ChatGPT by OpenAI
Gemini
mia
As a developer, I usually spend half my day clarifying what a PM actually wants. If this can bridge that gap by translating spec-speak into something actionable for my dev environment, that’s a win for me. Does it export directly to Linear or Jira, or integrate with any other tools?
mia
@ritikgupta_01 Really appreciate this perspective, that PM ↔ dev gap is exactly what we’re trying to reduce. Translating intent into something actually actionable is a big focus for us.
On integrations: not fully there yet, but Linear/Jira + dev workflow integrations are definitely on the roadmap.
mia
@ritikgupta_01 Hi, Ramazan is here.
You just described the exact reason we built this. The 'what does the PM actually mean' tax is brutal and we wanted to kill it.
On integrations: yes, mia exports to Linear and Jira today. Tickets come over with the context block attached, so you see the underlying customer quotes, the related themes, and the acceptance criteria in one place instead of a one-line title and a Slack thread you have to dig up. We also push to Notion for spec docs, and Slack for lightweight async handoffs.
The goal is that by the time a ticket hits your board, the 'what and why' is already answered, so your clarifying questions are about implementation, not intent.
'Cursor for PMs' is a bold tagline! Can mia help with drafting technical specs and PRDs by referencing an existing codebase, or is it focused on the ideation stage?
mia
@rivra_dev The current version of mia excels at the ideation stage. We are currently integrating with existing codebases to ensure your product intent translates perfectly into shipping-ready code.
@rivra_dev @sevil_kubilay Sevil, I think you should work on positioning of Mia. Agree with Rivra the tagline is bold but the current feature set doesn't deliver 5% of what a PM would expect from "Cursor for PM" ;) From the website, it seems it's more competitor / market intelligence which is really a small subset any PM's work.
mia
@rivra_dev Great question, and a fair push on the tagline.
Honestly, the bigger PM pain we kept seeing wasn't 'I need help writing the spec', it was 'I have no idea what to put in the spec because context lives in 14 places.' So that's where mia starts today: pulling signal from your external sources (user feedback, support tickets, sales calls) and internal ones (existing docs, tools, conversations) and turning that mess into a prioritized, defensible backlog.
Codebase referencing for spec and PRD drafting is the next layer we're building, and we're close. But we made a deliberate call to nail the upstream context problem first, because a beautifully written PRD for the wrong feature is still the wrong feature.
Starnus
@sevil_kubilay nice, congrats, btw, what do you mean by "customer signals" exactly? Curious to know more how it works and delivers the value and btw, great work :)
mia
@khashayar_mansourizadeh1 Thanks for the interest with great question! :) mia connects your customer interviews, support tickets, sales calls and usage data to build a single, traceable chain of product evidence.
Starnus
@sevil_kubilay nice, thanks for explanation
mia
@sevil_kubilay @khashayar_mansourizadeh1 Hi, Ramazan is here. By customer signals we mean any input where a real user, prospect, or internal team is telling you something about the product, even if they don't realize it. That includes support tickets, sales call notes, churn reasons, feature requests, NPS comments, app store reviews, Slack threads, Intercom chats, and Gong calls.
The problem is most of this lives in tools the PM doesn't open every day, so the signal sits there unread. mia pulls it all into one place, clusters it by theme, and surfaces what's actually trending. So instead of a PM scrolling through 200 Zendesk tickets on a Friday, they see 'checkout friction is up 40% this month, here are the 18 tickets behind it' and can decide what to do with it.
Starnus
@sevil_kubilay @ramazan_ovali Great, thanks for explanation
Mockin
Hey team. Getting an error at the signup "Access blocked: This app’s request is invalid"
mia
@lipkovskiy Hi, Ramazan is here. The code we gave is a Stripe discount code rather than a direct access to the portal. Kind Regards
The gap between "raw feedback" and "something an AI can actually build from" is real and painful.
Curious...does mia handle conflicting signals from different customer segments, or does it surface them and leave the call to the PM?
mia
@dmitrii_volosatov Hi, Ramazan is here.
You're poking at the exact problem we obsess over, so thank you for asking it.
Our stance: mia surfaces conflicts, but doesn't resolve them. And that's a deliberate call, not a limitation we're hiding.
Here's why. When enterprise asks for SSO and SMB asks for a lower price, that's not a data problem, it's a strategy call tied to who you want to win with. An AI making that call for you is an AI quietly running your company. What mia does instead is make the conflict visible and quantified: 'this theme is driven 80% by your top 10 ARR accounts, this other theme is 200 free users on Reddit', so the PM walks into the prioritization meeting with evidence instead of vibes.
The gap you mentioned, between raw feedback and something buildable, is exactly where we live. We just think the last mile of judgment should stay human, at least until the AI has skin in the P&L.
The "Cursor for PMs" framing really clicked for me, it immediately communicates that this isn't another dashboard, it's a tool that executes.
Sevil, curious: of the 500 customers you mentioned, what's the workflow change they report most often after using mia for the first 30 days?
mia
@muhamm3d Hi, Ramazan is here.
The pattern we hear most often in the first 30 days: PMs change what they bring to their cross-functional meetings. Before mia, the prioritization conversation starts with the PM's opinion. After mia, it starts with a themed view of what customers are actually saying, and the room argues about the response instead of the diagnosis. That's a smaller shift than it sounds and a bigger one than it sounds, at the same time.
The other change, which we didn't expect: PMs report fewer pings from sales and support asking 'did you see this customer complaint'. Because everyone can see the same signal feed, the human relay of feedback drops.