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

34 snips

Mar 5, 2026

Charlotte Kleverud, product and design lead who built PM tools at Spotify; Matthias Kleverud, product and AI builder with years at Spotify and Google. They talk about treating product strategy like GitHub to find merge conflicts in strategy. They explain the product chain from signals to principles. They describe document-processing and retrieval agents, metadata and temporal context, and how conflicts get auto-resolved or escalated.

Ask episode

AI Snips

Chapters

Books

Transcript

Episode notes

INSIGHT

Detect Strategy Merge Conflicts Automatically

Momental treats strategy misalignment like GitHub merge conflicts by surfacing logical contradictions across PRDs and briefs.
Example: one team prioritizes retention while another prioritizes conversion, and Momental flags potential coexistence or harmful misalignment.

INSIGHT

Product Chain Links Signals To Decisions

Momental models a product chain: signals → learnings → decisions → principles to trace the root of decisions.
The chain lets AI map a decision back to the supporting signal and alert when new data invalidates a decision.

ADVICE

Record Tacit Knowledge Immediately

Capture context beyond documents by recording audio and standups so tacit knowledge in people's heads becomes searchable context.
Matthias: allow recordings now and plan agent interviews to scale capture of tacit insights.

Get the Snipd Podcast app to discover more snips from this episode

Get the app

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

Resources & Links

Momental - GitHub for product management
Spotify - Where both founders started their PM careers
Claude Code - AI coding tool discussed in the conversation
Perk episode on Just Now Possible - Referenced episode about eliminating shadow work

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