Who Is the Real 'Cursor for Product Management'?
Every AI PM tool claims to be revolutionary. We examine what actually makes Cursor special—and which product management tools come closest to delivering that experience.
“We’re building the Cursor for X” has become the tech industry’s favorite pitch.
Cursor for sales. Cursor for legal. Cursor for HR. Every vertical wants its own version of the AI-powered code editor that transformed how developers work.
Product management is no exception. A growing number of tools are positioning themselves—explicitly or implicitly—as Cursor for PMs. But what does that actually mean? And does any tool actually deliver on the promise?
What Made Cursor Actually Different
Before evaluating the contenders, we need to understand what made Cursor revolutionary—not just good, but category-defining.
Cursor didn’t succeed because it wrapped GPT-4 in an editor. It succeeded because of two specific architectural decisions:
1. Indexed context at your fingertips
Cursor indexes your entire codebase. When you ask it a question, it can traverse your project structure, understand dependencies, and synthesize information from multiple files—without you copying and pasting anything.
This is fundamentally different from ChatGPT, where you manually provide context with every interaction. Cursor knows your code the way you know your code. It navigates and connects the dots on its own.
2. A side-by-side interface that edits your actual work
Cursor puts the AI conversation on one side and your actual code on the other. When the AI suggests changes, they appear directly in your files. You’re not copying from a chat window into your editor—you’re collaborating on the real artifact.
This isn’t just convenient. It’s a workflow transformation. You stay in your work environment, and the AI becomes an actual collaborator rather than a tool you consult in a separate tab.
The combination of indexed context plus native interface is what makes Cursor feel like magic. Any tool claiming to be “Cursor for PMs” needs to deliver both.
The Contenders
Let’s examine the tools claiming the Cursor mantle for product management—or at least trying to capture that same workflow transformation.
Telos: The Context Aggregation Approach
The pitch: AI that aggregates context from Slack, meetings, GitHub, and documents—then generates artifacts grounded in your team’s actual discussions.
Telos takes a different approach: instead of helping you write once you have context, it attempts to solve the context-gathering problem upstream.
Where it delivers:
- Slack integration captures discussions via @telos mentions
- Meeting agent joins calls and transcribes decisions
- GitHub integration provides technical context
- Side-by-side interface for editing tickets and PRDs
- Direct sync to Jira, Linear, and Asana
Where it falls short:
- Higher price point ($95-250/month vs $15-24 for writing tools)
- Requires integration setup and team buy-in
- Newer product with less refined templates than mature competitors
- Context approach only works if your team actually uses Slack and meetings
The verdict: The only tool that genuinely attempts both halves of the Cursor equation—indexed context from conversations plus a collaborative editing interface. Whether the premium price is worth it depends on how much time you currently spend gathering context.
Notion AI: The Workspace Approach
The pitch: AI integrated natively into your existing workspace, with access to all your docs and databases.
Notion AI has evolved significantly, adding Research Mode (which synthesizes information from your workspace, connected tools, and the web) and AI Connectors that link to Jira, Linear, GitHub, and Slack.
Where it delivers:
- Context from your Notion workspace is genuinely accessible
- The interface is native—you’re working in Notion, not switching tabs
- Research Mode can generate reports that pull from multiple sources
Where it falls short:
- Context is limited to what’s in Notion and connected apps
- Doesn’t capture conversations happening in Slack in real-time
- No meeting transcription—decisions made in calls stay invisible
- Doesn’t understand your codebase architecture
- Works best for documentation, less useful for ticket creation
The verdict: Notion AI is closer to “enhanced search plus writing assistant” than a true Cursor-style experience. The context layer is broad but shallow—it knows about your documents but doesn’t deeply understand your product’s technical reality.
ProdPad CoPilot: The Product Data Approach
The pitch: AI that understands your roadmap, ideas, OKRs, and customer feedback—because it’s built into your product management platform.
ProdPad announced CoPilot in early 2025 as a major AI launch. It has access to your product data and can draft documentation, answer stakeholder questions, and mine feedback.
Where it delivers:
- Deep integration with roadmap and idea management
- Understands prioritization frameworks within ProdPad
- Can generate documents informed by your actual product backlog
Where it falls short:
- Requires ProdPad as your core PM platform
- Context is limited to what’s in ProdPad—no Slack, no meetings
- No codebase integration for technical context
- Focused on roadmap-level work, less useful for sprint execution
The verdict: Strong within its domain, but the domain is narrow. If you’re already in ProdPad, it adds value. If you’re not, you’re not switching platforms for an AI assistant.
Kraftful: The Feedback Analysis Approach
The pitch: AI that analyzes user feedback and generates PRDs, user stories, and Jira tickets automatically.
Kraftful (now part of Amplitude) focuses on the feedback-to-documentation pipeline. Upload app reviews, support tickets, or call transcripts, and it identifies patterns, prioritizes requests, and drafts specifications.
Where it delivers:
- Excellent at processing qualitative feedback at scale
- Generates PRDs with real customer context
- Direct sync to Linear and Jira
Where it falls short:
- Feedback has to be explicitly uploaded or connected
- No awareness of team discussions or decisions
- Doesn’t capture the why behind requirements
- Context is product-facing (customer feedback) not team-facing (internal discussions)
The verdict: A powerful feedback synthesis tool, but it solves a different problem than Cursor solves. Cursor understands your codebase context. Kraftful understands customer feedback. Neither understands your team’s conversations.
ChatPRD: The Writing Assistant Approach
The pitch: AI that helps you write better PRDs, faster, with templates and structure.
ChatPRD is probably the most popular AI PM tool with 50,000+ users. It’s essentially a writing assistant trained on product documentation, with a good template library and clean interface.
Where it delivers:
- Mature, refined output quality
- Affordable and accessible ($15-24/month)
- Integrations for exporting to Notion, Confluence, Linear
Where it falls short:
- No context aggregation—you bring the context manually
- Doesn’t know your architecture, your team’s discussions, or your history
- Output quality depends entirely on input quality
- Classic “AI writing assistant” pattern, not Cursor-style collaboration
The verdict: ChatPRD is good at what it does, but it doesn’t claim to be Cursor-like. It’s a focused writing tool. The context problem is explicitly left to you.
Cursor Itself (For PMs)
There’s an interesting meta-answer here: some PMs are using actual Cursor for product work.
A course called “Cursor for Product Managers” teaches PMs to use the code editor for non-coding tasks—PRD writing, data analysis, strategy work. The argument is that Cursor’s interface and context-handling works just as well for documents as for code.
Where it delivers:
- Cursor’s context indexing actually works on any file type
- The side-by-side interface is genuinely excellent
- If you can work in markdown, the experience is solid
Where it falls short:
- Doesn’t integrate with PM-specific tools (Jira, Linear, Slack)
- No meeting transcription or conversation monitoring
- Requires comfort with a developer-oriented environment
- Context is limited to files—doesn’t understand conversations
The verdict: An interesting hack, but it’s bending a developer tool to PM needs rather than building for PMs natively.
The Honest Comparison Matrix
| Capability | Telos | Notion AI | ProdPad | Kraftful | ChatPRD | Cursor |
|---|---|---|---|---|---|---|
| Indexed context | Slack, meetings, code, docs | Docs only | Product data | Feedback | None | Files only |
| Side-by-side editing | Yes | Yes | Limited | No | No | Yes |
| Slack monitoring | Yes | Via connector | No | No | No | No |
| Meeting transcription | Yes | Yes (2025) | No | Limited | No | No |
| Codebase understanding | Yes | Via connector | No | No | No | Yes |
| PM-native interface | Yes | No | Yes | No | Yes | No |
| Price | $95-250/mo | $10/user/mo | Enterprise | $49+/mo | $15-24/mo | $20/mo |
The Pattern Worth Noting
Here’s what’s interesting: the tools that come closest to the Cursor experience are the ones that prioritize context aggregation over document generation.
Cursor didn’t win because it generated better code. It won because developers stopped spending time explaining their codebase to AI. The output quality came from the context quality.
The same dynamic applies to product management. A tool that perfectly formats your PRD is less valuable than a tool that already knows:
- What was discussed in the design review
- Why the technical approach was chosen
- What constraints the engineer mentioned in Slack
- How this feature connects to the customer feedback from last month
Document-first tools assume you arrive with context organized. Context-first tools help you stop spending 40-60% of your time on “context archaeology”—digging through Slack threads and meeting recordings to reconstruct what happened.
Who Should Choose What
If you’re a solo PM with organized workflows: ChatPRD or Notion AI. The context problem is smaller when you’re the only one making decisions. A good writing assistant adds real value.
If you’re already invested in a platform: Use that platform’s AI features. ProdPad CoPilot for ProdPad users. Notion AI for Notion-heavy teams. Switching platforms for an AI assistant rarely makes sense.
If you want to experiment with Cursor: Try it on your markdown docs and see if the interface clicks. Some PMs love it. Others find it too developer-oriented.
If your team’s knowledge lives in conversations: This is where context-first tools matter. If decisions happen in Slack threads and meetings, and you spend significant time reconstructing that context, the premium for aggregation-focused tools may be worth it.
The Real Question
The “Cursor for X” framing invites a specific question: which tool delivers the feeling Cursor delivers?
That feeling isn’t “AI helps me write.” It’s “AI already knows my context, so I can just work.”
By that standard, the honest answer is: no PM tool fully delivers the Cursor experience yet. The market is still figuring out how to index the messy, conversational context that product management runs on.
But the tools attempting to solve context aggregation—rather than just document generation—are at least working on the right problem.