Rocket Blogs
AI App Development
By Rahil Shah
Apr 23, 2026
Updated Apr 23, 2026
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Table of contents
What does Solve on Rocket.new actually do?
Who should use this platform?
How is it different from other tools?
Does it replace manual analysis completely?
An overview of how Solve on Rocket.new analyzes customer interview transcripts, extracts key insights, and converts them into structured, scoped feature briefs that engineering teams can immediately understand, prioritize, and build from.
How do you turn messy interviews into something buildable?
You need a system that can take raw conversation data, structure it clearly, and turn it into outputs your team can act on. That’s where AI powered platforms like Rocket.new Solve step in. They help convert unstructured discussions into something usable without slowing things down.
According to McKinsey, companies using AI in product development can reduce development time by up to 30%. That kind of speed matters when teams are trying to move fast and stay ahead.
So, let’s break down how this process works, why teams struggle, and how Solve on Rocket.new changes the flow from raw interviews to real product decisions.
Customer interviews are incredibly valuable. They give direct access to what customers think, feel, and need. But at the same time, they can feel chaotic and hard to manage.
You sit through long interview calls. You collect vast amounts of data. You take notes and try to understand what users are saying. Then comes the hard part, making sense of it all.
This is exactly where most product teams start to struggle.
Raw interview transcripts are hard to manage
Key themes get lost in long conversations
Pain points are scattered across different notes
Teams struggle to write a clear problem statement
Engineers don’t get clear direction from the insights
So even after putting in all that effort, teams often fail to build what customers actually need. And that’s where the real frustration begins.
Most teams rely on many tools, each solving just one small part of the process. On paper, it looks organized. In reality, it creates more gaps than it solves.
Broken workflows
Repeated tasks
Lost context
Slower speed
Poor analysis
Even with AI tools, the experience often feels disconnected.
You still have to piece everything together, and without structure, the outputs from large language models can feel random. Instead of clarity, teams end up dealing with more confusion.
Solve is part of the Rocket.new platform, designed to handle research, analysis, and output in one connected system.
Processes customer interviews and research data
Uses AI and shared context to run structured analysis
Generates summaries, insights, and clear outputs
Helps teams create direction before building
Simple idea. But instead of scattered tools and confusion, your team gets clarity and something they can actually build from.
Let’s break it down in a simple way. This is where raw interview data starts turning into something your team can actually use.
You upload customer interview transcripts or notes.
This can include:
Call recordings turned into text
Written interview summaries
Any relevant background for the call
The system gets access to all this data in one place.
Now the AI powered system starts its analysis. It uses natural language understanding to read through conversations and understand context.
It looks for:
Pain points
Friction points
Key themes
Repeated answers
User behavior patterns
This is where large language models shine. They process vast amounts of data quickly and consistently.
Next, the system works to extract insights. Instead of dumping random outputs, it organizes everything into structured sections.
This includes:
Problem statement
User needs
Key insights
Supporting quotes
Now the team can actually understand what customers are saying.
Solve takes those insights and turns them into something actionable. A scoped feature brief.
This includes:
Clear feature ideas
Defined outcomes
Business context
Suggested strategy
So instead of raw notes, your team gets something they can build from.
Step by step, the process moves from messy conversations to structured outputs. And in the end, your team gets clarity, direction, and something real to work with.
Let’s take a simple example.
| Stage | Before Solve | After Solve |
|---|---|---|
| Interview Data | Long transcripts | Structured summary |
| Analysis | Manual and slow | AI powered analysis |
| Insights | Scattered notes | Clear insights |
| Output | Vague ideas | Feature-ready brief |
| Team Work | Confusing tasks |
That’s a huge shift in how teams manage their workflows.
So what does this actually mean for your team?
It shifts how product managers and founders approach their work, making the whole process more clear and focused.
Less time spent on analysis, more time to focus on strategy
Better decisions based on real insights, not guesswork
Fewer tools to manage, everything in one platform
Clear understanding of what users actually need
When the noise is reduced, teams can move faster and build with confidence. And that’s where real progress starts to happen.
Here’s something interesting directly from X:
“Get a clear recommendation. A structured brief… ready to hand to a developer.” post on X
This perfectly reflects the shift happening right now.
Teams don’t just want raw data or long interview transcripts. They want clear outputs they can actually build from. That’s the real value. Turning conversations into structured briefs that move directly into a
Solve works as a research engine that connects data, analysis, and output into one system.
AI powered analysis of customer interviews
Structured summaries with context
Ability to create feature briefs automatically
Support for multiple workflows
Built-in knowledge handling
You input data
The system processes it using AI
It generates structured outputs
The team uses those outputs to build
It keeps things simple while helping teams move from raw data to clear direction without switching between tools.
Now let’s zoom out a bit.
Today, companies compete on speed and understanding. The faster a team can understand customers, the faster they can build the right features.
Faster understanding of customer needs
Better connection between research and product decisions
Less gap between thinking and execution
Stronger ability to scale research
Solve helps bridge the gap between raw conversations and real product outcomes, making it easier for teams to act with clarity.
Product development is changing, and AI is becoming part of everyday workflows. But not all tools actually help teams move forward.
Focus on the full process, not just one task
Better handling of data and analysis together
Outputs that support real building decisions
Consistent workflows across teams
When tools support real tasks and decisions, teams can move faster, stay focused, and build with more confidence.
Teams run customer interviews but still struggle to turn them into clear outcomes. There’s too much data, too many tools, and not enough clarity. Insights get scattered, analysis takes time, and the team often ends up with unclear direction. This slows down decisions and makes it harder to build what users actually need.
Solve on Rocket.new Converts Customer Interview Transcripts into structured feature briefs by connecting data, analysis, and output into one process. When everything is aligned, teams move faster with better speed and focus. They get stronger insights, clearer strategy, and outcomes that lead to better products and real growth.
Try Solve on Rocket.new to convert customer interview transcripts into structured outputs your team can act on quickly.
| Focused tasks |