This Product Leader Built Her Own PM OS with Claude Code: LIVE Demo | Parul Goel (ex-Indeed/PayPal)
Parul Goel, a former Senior Director of Product Management at Indeed, built an open source PM operating system with Claude Code — and today she visits LaunchPod to give a live demo.
In this episode, we’re joined by Parul Goel, most recently Senior Director of Product Management at Indeed, where she led the orders and billing platforms behind a multi-billion-dollar business. Before that, she spent nearly nine years at PayPal, building a zero-to-one payments platform for marketplaces that landed clients like AliExpress and Facebook Marketplace.
About a year ago, Parul hit the same wall a lot of senior product leaders are sitting behind right now: reading everything about AI, absorbing none of it. So she started building. The result is PM OS: an open source system she and three other product leaders built with Claude Code to automate four core PM functions. This episode is also a LaunchPod first: Parul screen-shares and demos the OS tool live!
In this episode, Parul shows:
A live demo of the PM operating system she built with 3 fellow product leaders and the four-layered architecture behind it
Why the “context library” and codifying company identity, stakeholders, and voice is the layer most people skip, BUT the one that matters most
And how the system handles core PM workflows like exec updates, cross-functional communication, customer interview synthesis, and PRD drafts
1. The cure for AI FOMO is to start building with it
Parul’s entry into building with AI wasn’t a strategy offsite. It came after a year of obsessive reading that left her feeling worse, not smarter.
“When AI showed up, I was like everybody else. I would read every LinkedIn post. I would read every article obsessively. And then I realized it’s not giving me conviction – it’s giving me anxiety. It’s giving me FOMO.”
Her fix was to stop consuming and start shipping. She now spends time building almost every day — PM OS plus a handful of personal tools.
“The only way to really feel what AI can do, where your job would stay versus what parts would disappear, is to have hands-on experience.”
Product takeaway: You can’t read your way to AI fluency. If your leadership team is still debating AI strategy in the abstract, the fastest path to conviction is for the people making the decisions to build something. Start small, and use it on real work. The anxiety most leaders feel isn’t a knowledge gap — it’s an experience gap.
2. The idea started as grunt work, not strategy
When Parul’s co-builders proposed PM OS, she knew immediately which piece she wanted: the exec update. Because, thanks to her experience, she knew what it was like to write the same one over and over.
“I was just doing the same status update for different audiences. And I realized that I am just doing the thinking once. A lot of it is just re-projecting, changing the tone and the length and just going and talking about what they care about.”
Once she framed it that way – thinking once, re-projecting five times – it stopped being an unavoidable tax on the job and became something she could build an automated process for.
The team picked four core PM functions and automated most of each:
Executive updates
Cross-functional updates
Customer interview synthesis
PRD drafting
They built the first version over a weekend.
Parul also picked this problem for a specific reason:
“One of the reasons why I thought this was a good problem to solve via AI is it’s low risk. I’m always going to check before I send something out. It’s never going to be automatically sent.”
Product takeaway: The best first AI use case in your org is probably not your most valuable workflow. It’s the one where the thinking is already done, the output is repetitive, and a human reviews it before it goes anywhere. Look for tasks where you do the reasoning once and reformat it many times — those are where AI compounds, and where a bad draft costs you nothing.
3. The context library is the actual product
The PM OS has a four-layer architecture:
perception (what it knows)
execution (the four skills)
critique (how it evaluates its own output)
continuity (what it remembers)
Parul calls this the soul of the system, but she’s blunt about which layer matters most and which one people skip.
The context library is a folder of markdown files describing the company’s business model, target clients, past decisions, user personas, the company voice, and – most importantly – the stakeholders.
“This is an interesting one because it’s beyond just the name and role. It’s what do they care about? What are their pet peeves? Things you actually learn about people as you work for them, things you actually think about before sending them something.”
That’s why the same status update comes out differently for the CEO than for the CTO. In the demo, the system doesn’t just relabel the audience – it reframes it appropriately.
“If you’re building something like this, don’t skip [the context layer]. It is the most important.”
Product takeaway: Generic AI output is almost always a context problem, not a prompt problem. The unglamorous work – writing down what your stakeholders care about, how your company actually talks, which decisions have already been made – is the part that determines whether output sounds like your team or like a language model. Budget real time for it and treat it as infrastructure.
4. Make the AI critique itself before you ever see it
The most interesting moment in the demo isn’t the draft. It’s what happens after.
Parul defined three sub-agents — an engineer, a designer, and an exec — that review every update before it reaches her.
“These are your coworkers weighing in before you send something out.”
In the live demo, the engineer sub-agent flags that the team has already missed this date twice, and that claiming high confidence in the new date without explaining why will read as overstating. Which is exactly the question she’d have gotten in the meeting.
She also built in a hard constraint on the input side. The skill can ask clarifying questions only once.
“When you are in the process of working on something, the last thing you want is to get into a back-and-forth with an AI assistant. So we limited this. You can ask questions only once, and then you have to draft.”
Product takeaway: Adversarial review is a design pattern, not a nice-to-have. Instead of asking a model to write well, ask it to predict how a specific reader will push back – then fix that before you ship. And design your interaction budget deliberately: an assistant that interrogates you is worse than one that drafts something imperfect and flags its own gaps.
5. The build got easier, but the judgment didn’t
When asked how she thinks AI will affect the PM role going forward, Parul’s clearest evidence that PMs aren’t going anywhere came from an AI-drafted email she caught just in time – one that told the recipient the meeting had been more useful than she’d anticipated.
“Thankfully, I didn’t send it. But that’s where the judgment is so off. That gave me a lot of comfort that my job is not going anywhere soon.”
Her broader read: the engineering has gotten dramatically easier, but deciding what’s worth building and what’s safe to send has not.
“AI can give you options, it can give you ideas, it can tell you the trade-offs. But the actual judgment, the actual decision, still needs to live with the person – because a lot of times these decisions are made based on company values or an individual’s values.”
She does expect the shape of the job to change. Teams will get smaller, the work will get higher-leverage, and writing the same status five times will disappear.
Product takeaway: The threat to your PM org isn’t AI doing the judgment. It’s your PMs spending so much of the week on re-projection and reformatting that they never get to the judgment. Automate the grunt work first, then measure whether the reclaimed hours actually go toward better decisions.
Chapters
00:00 Introduction
04:38 Why Parul and her collaborators built a PM OS using Claude Code
09:05 Exec update live demo
15:18 The importance of the context library
22:28 The future of PM with AI
23:36 Conclusion
Links
Parul's collaborators:
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