Post-launch thoughts after reaching Top 10 on Product Hunt
Hey everyone,
After launching Intrascope and finishing Top 10 Product of the Day, we wanted to open a quick discussion.
The biggest takeaway for us wasn’t the ranking, but the conversations. We talked to teams who are already using AI daily and are struggling with scattered tools, separate API keys, lost context, and costs growing without visibility.
That’s exactly why we built Intrascope: a shared AI workspace where teams bring their own API keys, work with shared context and Manifests, and keep usage and costs predictable.
Curious to hear from others here:
How are you organizing AI usage across your team today?
At what point does it start to break down?
Happy to answer questions and share what we’ve learned so far.
Vladimir
Founder @ Intrascope
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Replies
minimalist phone: creating folders
Depth in this case matters more than quantity. Congrats ;)
Intrascope.app
@busmark_w_nika Absolutely agree. That’s exactly what we saw during the launch as well.
Depth of conversations and real usage feedback mattered far more to us than raw numbers.
Lightfern for Email
We're a team of 6, and honestly, we don't 😅. Everybody has their own company card and just expenses whatever AI services they're using.
Everyone's spending is within reason, so we've not had to change this. This might not scale but it's fine for now.
Intrascope.app
Hi,@dougli Totally get this. That’s exactly how we worked as well.
Everyone had their own card, expenses were reasonable, and nothing felt broken enough to fix. We still use other AI tools where it makes sense.
What changed for us was consolidating day to day generative AI work into one place. For that part, we moved fully to Intrascope.
In practice, the workflow stayed the same, but the cost structure didn’t. We went from roughly 5 × $20 monthly subscriptions for everyday tasks to about $10–15 per month for the whole team by using API keys and paying only for actual usage.
The biggest win was token pricing. For reference, 1M tokens is roughly 750,000 words, which goes a long way for real work. Image generation also works well, and having everything in one shared workspace made collaboration much easier.
For us, nothing was “on fire” before. It just became more predictable, cheaper, and easier to manage once usage started growing.
Lightfern for Email
@intrascopeai thanks Vladimir! Really interesting that you see the pricing drop massively - I'd have thought that payment by seat on average might cost less but I'm wrong!
How do you handle the UI / OS integrations? Eg tool calls to memory, browsing, and stuff like Claude code? Coding is critical w/ my use case.
Intrascope.app
@dougli That surprised us too at first. Once usage is centralized and models are chosen intentionally, costs drop fast compared to seat-based pricing.
Intrascope is more focused on general AI workflows for teams rather than deep IDE or OS-level integrations. We don’t try to replace coding-first tools like Claude Code, Cursor, or similar agents that are built specifically for development.
Our strength is in shared daily AI work across multiple people: planning, research, specs, documentation, internal communication, and decision-making. Manifests help preserve and reuse context so teams don’t keep re-explaining the same things, especially when work passes between teammates.
Many teams use Intrascope alongside coding tools. Intrascope handles the thinking, planning, and coordination layer, while dedicated coding tools handle implementation.
GraphBit
Congrats on the Top 10. That breakdown you’re describing usually starts once AI stops being an experiment and becomes part of daily work. The moment multiple tools, keys and contexts enter the picture, coordination and cost visibility turn into real problems. Teams need shared structure around AI usage long before they need “more models.”
Intrascope.app
@musa_molla Absolutely. We noticed the same shift very quickly. Once AI becomes part of daily work rather than an experiment, multiple tools, keys, and scattered contexts create coordination and cost visibility issues. At that stage, teams benefit far more from shared structure around AI usage than from adding more models.
This really resonates, especially the idea that the conversations matter more than the ranking.
I’m gearing up for my first launch soon, and that’s exactly the part I’m still trying to understand: how much of launch day ends up being about listening vs. broadcasting.
For you, what helped you get the right conversations going early on?
Was it mostly inbound on launch day, or things you did before the launch?
Intrascope.app
Hi@ederwii, Thanks for your question!
For us, the conversations didn’t happen by accident on launch day. They were mostly a result of preparation and being very clear about the problem we were solving. We spent time before the launch thinking through our positioning, the language we used, and making sure we could clearly explain why the product exists, not just what it does.
On launch day itself, the key was listening more than broadcasting. We replied to comments quickly, asked follow-up questions, and treated it like a conversation rather than a campaign.
I think believing in the product and really understanding your own workflows helps a lot. If you know your processes and the problem deeply, the right conversations tend to find you. The product kind of speaks for itself when that’s the case.
Happy to share more if helpful, and good luck with your first launch!