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Building GitHub for Product Management: How Momental Uses AI to Find Merge Conflicts in Strategy

Building GitHub for Product Management: How Momental Uses AI to Find Merge Conflicts in Strategy

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What if an AI could spot the moment two product teams start pulling in opposite directions -- before it derails a quarter?

In this episode of Just Now Possible, Teresa Torres talks with Matthias and Charlotte Kleverud, co-founders of Momental, about their vision for building what they call "GitHub for product management." Momental ingests documents, meeting transcripts, and voice recordings across an organization, then uses AI agents to map them into a structured context layer—a set of interconnected trees covering goals, decisions, learnings, and who's doing what. When it finds a conflict—say, one team betting on retention while another is prioritizing conversion—it surfaces the misalignment for humans to resolve, just like a merge conflict in code.

You'll hear how their journey started in 2022 with GPT-3, pivoted through a multi-agent team that exposed the real problem (agents need aligned context, too), and landed on an OODA-loop-driven document processing agent that builds and maintains a living knowledge graph. It's a detailed look at how domain-specific context modeling, human-in-the-loop conflict resolution, and self-improving agents come together to tackle one of the hardest problems in product organizations: keeping everyone aligned.

Show Notes

Guests

  • Matthias Kleverud - Co-Founder, Momental
  • Charlotte Kleverud - Co-Founder, Momental

What we cover in this episode:

  • What "GitHub for product management" means: finding merge conflicts in strategy, not code
  • The product chain: signals → learnings → decisions → principles, and how AI maps it
  • Three trees that model an organization: the product tree (OKRs to epics), the wisdom tree (decisions and their reasoning), and the people/time tree
  • How a document processing agent uses OODA-loop thinking to extract and connect context across documents
  • Why traditional chunking and RAG breaks down at scale and what Momental does instead
  • The origin story: building a team of AI agents in 2024, only to discover agents hit the same alignment problems as humans
  • Starting in 2022 with DaVinci 002 and learning that the market wasn't ready for AI-assisted product thinking
  • How conflicts are detected, auto-resolved, or escalated to humans with merge options
  • Why metadata—who said it, when, and in what context—is critical to preventing hallucinations
  • The self-improving agent: collecting user feedback weekly and rewriting its own prompts
  • Moving from chat-first to UI-first to proactive agents as an AI product design pattern
  • Design partner strategy and what's next for Momental's public launch

Chapters

00:00 Meet The Founders
01:14 GitHub For PMs Explained
03:19 Strategy Merge Conflicts
06:49 Product Chain Model
09:49 Capturing Context Fast
12:17 Context Graph And RAG Limits
16:52 Origin Story Since 2022
20:01 From Agent Team To Foundation
25:26 Three Trees Of Context
28:42 Two Agents Secret Sauce
31:37 How Document Processing Works
34:55 Agents Ask Better Questions
35:41 Human In The Loop Context
36:38 Data Models And Graphs
39:21 Beyond Documents And Vectors
42:25 Specialized Tools Win
44:50 Quarterly Planning At Scale
49:38 Discovery Versus Vibe Coding
51:00 Tree Building And Conflicts
53:49 UI Over Chat Interfaces
56:01 Proactive Agents In Practice
58:00 Quality Evals And Feedback
01:00:22 Launch Plans And Mission
01:03:19 Eliminating Shadow Work
01:03:56 Closing Thanks

Full Transcript

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