Track: Building the Product Team I Always Wanted Beside Me
- Neha Gupta
- 11 hours ago
- 4 min read
For a long time, I've believed that product management isn't actually one job.
It's six or seven different disciplines that happen to share the same title.
On Monday you're synthesizing user interviews.
On Tuesday you're prioritizing features.
By Wednesday you're writing a PRD.
Thursday is spent questioning whether your competitors know something you don't.
Friday you're defending roadmap decisions with financial trade-offs.
And somewhere in between, you're trying to launch something customers actually want.
The best product managers don't just know these disciplines.
They learn how to switch between them.
The challenge is that switching contexts is expensive.
Every time I move from research to prioritization, from strategy to execution, I carry a different mental model. Different frameworks. Different questions. Different biases that I have to consciously manage.
Over the last few months, AI has become remarkably good at generating content.
But I wasn't looking for another tool that could write a PRD.
I wanted something that could think through product problems the way experienced product teams do.
Not replace judgment.
Improve it.
That question stayed with me long enough that I decided to build the answer.
That idea eventually became Track.
Track isn't a single AI prompt.
It's a collection of specialized product thinking systems.
Behind the interface are six reasoning engines, each designed around a different part of product management:
User Research
Competitor Analysis
Feature Prioritization
Product Requirements (PRDs)
Go-to-Market Strategy
Portfolio Finance & Investment
Each one approaches problems differently because each discipline requires different questions, different frameworks, and different ways of reaching a decision.
But reasoning is only valuable if it leads to something actionable.
So every agent produces an executive-ready summary that captures the key insights, recommendations, assumptions, and decisions. Depending on the need, that output can be downloaded as a PDF or Word document - something that can be shared with stakeholders, discussed in meetings, or used as the starting point for the next decision.
I wanted Track to bridge the gap between thinking and execution. Not just to answer questions, but to create deliverables that fit naturally into how product teams already work.
The goal was never to make AI produce longer documents.
The goal was to make it reason more like a thoughtful product partner.
Building Track also changed how I think about AI products.
Most conversations about AI focus on models.
Which model is better.
Which benchmark improved.
Which release is faster.
But after building Track, I think that's becoming the wrong question.
The real differentiation isn't the model.
It's the system built around it.
The prompts.
The reasoning logic.
The constraints.
The decision frameworks.
The accumulated knowledge that guides the model toward better outcomes instead of merely more words.
The models are incredibly capable.
But the product is everything you build around it.
To build Track, I leaned heavily on Claude.
Not just as an assistant, but as both a design and development partner.
Claude Projects became the place where product knowledge, reasoning structures, and domain-specific thinking came together. Claude Design helped shape the user experience, while a lightweight front end made those capabilities accessible without exposing the complexity underneath.
From the outside, Track feels simple.
You provide context.
You choose the type of product problem you're trying to solve.
The system does the rest.
Behind that simplicity sits weeks of refining how different product disciplines should reason, what they should prioritize, and where they should challenge assumptions.
That's the part users should never have to think about.
Perhaps the biggest realization from building Track wasn't technical.
It was philosophical.
AI is changing what it means to build software.
Not every product needs another feature.
Sometimes it needs a better way of thinking.
As product teams, we've spent years creating systems for users.
Increasingly, we're also creating systems that help us make better decisions ourselves.
That's a very different kind of product.
And I suspect we'll see many more of them over the next few years.
Track isn't trying to replace product teams.
If anything, it's built on the belief that great product management remains deeply human.
Prioritization still requires judgment.
Research still requires curiosity.
Strategy still requires context.
What AI can do is reduce the friction between those activities.
It can help us move faster without thinking less.
That, to me, feels like the most exciting application of AI in product management.
Not replacing the craft.
Giving more time back to practice it.
Building Track reminded me why I enjoy building products in the first place.
Not because of the technology.
Not because of AI.
But because every now and then, you get the chance to build something you wish had existed years earlier.
Track is one of those products.
And I have a feeling it's only the beginning.
This started as a personal project to support my own work in product management. But as I continue using it, I also plan to continue building it. There are still many parts of the product management discipline that can benefit from structured reasoning, and I intend to keep adding new agents, refining existing ones, and expanding what Track is capable of.
In many ways, Track will evolve alongside my own career.
Every new product challenge will shape another part of it.
Every new lesson will become another reasoning system.
Because Track was never meant to be a finished product.
It's becoming the product team I always wanted beside me.
Continue reading
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