Hey PH community,
Today is an exciting milestone as we launch for the second time.
I started this journey based on my 20 years as a product strategist. My persistent pain point was never having enough integrated data to build the right product. This created an endless loop of misalignment between growth and product teams.
Inspired by the build in public movement, we launched our market intelligence module last October. Since then, 500 customers have helped us understand exactly what to build next. Their feedback led us to a powerful realization: the world needs a product intelligence platform that doesn't just analyze market and customer signals, but actually executes.
We have now evolved that vision into Cursor for Product Managers.
While we cannot provide a freemium version as a bootstrapped team, we want to support this community. Use the code MIA50 for a 50% discount. This will give you full access to see how we are redefining product strategy by connecting market data, competitor moves, and customer signals into one seamless flow.
We are currently building the integrations to fully close the loop with coding platforms but we can assure you that we will build fast.
Love you guys!
Sevil on behalf of the mia team
@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 :)
@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.
@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.
'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?
@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.
Report
@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.
@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.
Report
Well done. Will you be able to trace a shipped feature back to the original customer signal that triggered it? That traceability layer would be huge for making the case internally that PM-led discovery actually moves the needle
Report
Great idea. I’m still waiting for someone to build an AI service that analyzes user behavior on a website, understands their goals and doubts based on the entry point, visited pages, mouse movement, etc., and then takes actions that increase the chances of conversion. This could be a popup with relevant information, an email, or even a dynamically changing interface tailored to the user’s intent. That would be gold for product managers!
@natalia_iankovych Appreciate this 🙌 totally agree, it feels like we’re one layer away from this becoming real. The shift from static analytics to real-time understanding + action is going to be huge. Would be a game changer for PMs.
Report
@natalia_iankovych@sevil_kubilay As a PM, if you're on top of your data in smt like Amplitude, you're pretty close to real-time understanding, but for me the gaps are: 1) understanding to actionable insight to reqs to code - and I think you're on it 2) prioritization between multiple sources: you will typically have other inputs on top of product analytics, like marketing campaigns performance, qualitative research, surveys, support tickets, and - my favorite - executive injection. It's hard enough to have all these in one analysis plane, but even when you do - how do you put weights on these signals? Do you trust user interviews or the conversion funnel? I don't have a good answer, but these are the questions on my mind.
Report
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?
@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.
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.
Report
This feels especially useful for teams without dedicated product ops or research synthesis layers. Smaller teams will likely get the most immediate value.
Report
Congrats on the launch! Been following closely this space as it's probably a big gap in between Coding Agents and all the data produced by the business. The only thing that is really integrated is the ticketing (e.g linear). Whats is your take in the right UX for the long term of the product? More of a self-service insight platform, or more integrated directly into Slack and Coding agents. I'm asking because I think the problem is very genuine but the UX is hard to nail.
Report
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?
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.
Report
Love the direction. I can see how it works when a solid growth team actually sit next to Product.
As a PM in a legacy org, I'd be happy if I even got actionable insights based on multimodal inputs - I'll write the PRD, hell I'll even write the code - but please help me make sense of the hairy data. Something I'm gonna try solving for myself.
Replies
mia
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
'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.
Well done. Will you be able to trace a shipped feature back to the original customer signal that triggered it? That traceability layer would be huge for making the case internally that PM-led discovery actually moves the needle
Great idea. I’m still waiting for someone to build an AI service that analyzes user behavior on a website, understands their goals and doubts based on the entry point, visited pages, mouse movement, etc., and then takes actions that increase the chances of conversion. This could be a popup with relevant information, an email, or even a dynamically changing interface tailored to the user’s intent. That would be gold for product managers!
mia
@natalia_iankovych Appreciate this 🙌 totally agree, it feels like we’re one layer away from this becoming real. The shift from static analytics to real-time understanding + action is going to be huge. Would be a game changer for PMs.
@natalia_iankovych @sevil_kubilay As a PM, if you're on top of your data in smt like Amplitude, you're pretty close to real-time understanding, but for me the gaps are:
1) understanding to actionable insight to reqs to code - and I think you're on it
2) prioritization between multiple sources: you will typically have other inputs on top of product analytics, like marketing campaigns performance, qualitative research, surveys, support tickets, and - my favorite - executive injection. It's hard enough to have all these in one analysis plane, but even when you do - how do you put weights on these signals? Do you trust user interviews or the conversion funnel? I don't have a good answer, but these are the questions on my mind.
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.
This feels especially useful for teams without dedicated product ops or research synthesis layers. Smaller teams will likely get the most immediate value.
Congrats on the launch! Been following closely this space as it's probably a big gap in between Coding Agents and all the data produced by the business. The only thing that is really integrated is the ticketing (e.g linear).
Whats is your take in the right UX for the long term of the product? More of a self-service insight platform, or more integrated directly into Slack and Coding agents. I'm asking because I think the problem is very genuine but the UX is hard to nail.
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.
Love the direction. I can see how it works when a solid growth team actually sit next to Product.
As a PM in a legacy org, I'd be happy if I even got actionable insights based on multimodal inputs - I'll write the PRD, hell I'll even write the code - but please help me make sense of the hairy data. Something I'm gonna try solving for myself.