Research is important to product development -- research is also a pain! Conduct AI (or human) interviews with stakeholders/users and automatically generate comprehensive insights, requirements, and prompts at scale.
Universal-3 Pro by AssemblyAI — The first promptable speech model for production
The first promptable speech model for production
Promoted
Maker
📌
Hey PH! 👋
I'm Mike, and we built Colab Jetpack as a solution to our own frustrations with product design.
We all know user research is critical. But the process is a slow, expensive bottleneck, and the best insights often die in a 50-page document—even with the help of AI.
Colab Jetpack is purpose built to fix this. It bridges the gap from raw insight to sprint-ready execution.
Here's how:
🤖 The Researcher: First, our AI agent acts like a seasoned UX researcher. It interviews your users 1-on-1, building a "mental model" to ask smarter, deeper questions as the conversation goes.
🚀 The Strategist: After the interviews, the agent transforms into your strategic partner. This is where it gets powerful. Forget basic Q&A. You can ask your strategist to do high-level work based on the research:
> "Analyze our top 3 competitors against the frustrations you just heard."
> "Brainstorm 5 concrete solutions for the 'onboarding problem' we identified."
> "Generate a full MoSCoW prioritization for our next feature, using this feedback as evidence."
It even preps the build. It can draft all your Agile documentation (like user stories and epics) and write starter prompts for agentic coding tools.
We know this is a hot space right now. But we’re laser-focused on the complete "insight-to-execution" workflow, not just another summarization tool.
Our Ask:
We’d be honored if you’d try Colab Jetpack.
> How far can you push the strategist?
> If you've used other AI tools, how does our "insight-to-execution" workflow compare?
We're here all day to answer your questions. Thanks!
@mike_78@jon_heinrich Really interesting product! I've got a few Qs: 1. What does your LLM stack look like and (something I've been thinking about a lot with our production models) How often are going to be updating and testing that/those models to evaluate both outputs and consistency?
2. Do you have any examples that people could check out in the thread where they used the product to get (to outcome X) - ex. beyond the demo vid - just some screenshots of Step 1 Step 2 Step 3 and being able to really wrap my head around this (as someone on the non technical side).
Report
Maker
@dzaitzow We are using a variety of OpenAI models depending on the task. If anyone is interested in more detailed documentation, please join our partners program. We will give you an inside look at some testing we have done. Sign up here: https://colabjetpack.com/video
Really interesting launch, automating the research-to-insights workflow has huge potential. Collecting qualitative data is easy; structuring it into something usable is where most teams struggle. Excited to see how you guys bridge that gap.
Report
Maker
@vik_sh Thanks Viktor. The platform builds a mental model of the application described by the interviewees, asking smarter, clarifying questions as it progresses—making sure not to lead the conversation. It is also trained to find opportunities and, more importantly, to identify and point out gaps or disagreements. This capability allows it to work with the user to create good requirements, which are then turned into prompts.
ProblemHunt
Hi Mike!
Wow, I see great potential in your product. Wishing you all the best, my friend. You definitely have my support! 👍
@gostroverhov Thanks for the support!
@mike_78 @jon_heinrich Really interesting product! I've got a few Qs: 1. What does your LLM stack look like and (something I've been thinking about a lot with our production models) How often are going to be updating and testing that/those models to evaluate both outputs and consistency?
2. Do you have any examples that people could check out in the thread where they used the product to get (to outcome X) - ex. beyond the demo vid - just some screenshots of Step 1 Step 2 Step 3 and being able to really wrap my head around this (as someone on the non technical side).
@dzaitzow We are using a variety of OpenAI models depending on the task. If anyone is interested in more detailed documentation, please join our partners program. We will give you an inside look at some testing we have done. Sign up here: https://colabjetpack.com/video
@mike_78 Will do!
Really interesting launch, automating the research-to-insights workflow has huge potential. Collecting qualitative data is easy; structuring it into something usable is where most teams struggle. Excited to see how you guys bridge that gap.
@vik_sh Thanks Viktor. The platform builds a mental model of the application described by the interviewees, asking smarter, clarifying questions as it progresses—making sure not to lead the conversation. It is also trained to find opportunities and, more importantly, to identify and point out gaps or disagreements. This capability allows it to work with the user to create good requirements, which are then turned into prompts.
Atlas
Congrats on the launch!!!