What is wrong with project management software today?
Most project management tools were designed for a world where humans did all the work and software tracked it. That world no longer exists. Today, a significant portion of code, research, and planning happens inside AI tools — Claude, Cursor, Copilot — yet your project tracker sits in a separate browser tab, untouched by any of it.
That gap is why PlanIT exists.
The problem we kept running into
Before building PlanIT, we ran software teams the normal way: Linear for issues, Notion for docs, Slack for communication. Every AI-assisted coding session ended the same way — close the AI, open the tracker, manually log what happened, switch back. Thirty seconds of admin, repeated dozens of times a day. Multiply that across a team and you lose hours every week to translation work.
The deeper problem is that AI tools have no awareness of your project context. Claude doesn't know what issues are open, what the current sprint goal is, or what your team decided last week. You have to paste that context in manually every single time.
PlanIT was built to solve both problems at once.
What "AI-native" actually means
AI-native doesn't mean we added a chatbot to a traditional project management interface. It means the product was designed from scratch around the assumption that AI agents are first-class participants in your workflow.
In practice, that means three things:
1. PlanIT has a Model Context Protocol (MCP) server. MCP is an open standard that lets AI tools like Claude, Cursor, and VS Code read and write structured data from external services. When PlanIT's MCP server is connected to your AI tool, Claude can create issues, update statuses, log progress, and query your backlog — without you switching context. See the step-by-step MCP setup guide for Claude Desktop, Cursor, and VS Code.
2. The data model is optimized for AI consumption. Traditional project management tools store data in ways that made sense for human dashboards. PlanIT's schema is designed so that an AI agent can construct a useful, accurate picture of project state from a small number of queries.
3. Human workflows remain first-class. AI-native doesn't mean human-optional. PlanIT has a full web interface, kanban boards, sprint planning, roadmaps, and an idea board — everything a team needs to run projects without AI involvement. The AI layer accelerates the work; it doesn't replace the judgment.
Why not just build a Linear integration?
This is the question we asked ourselves before starting. Why not integrate existing tools with MCP instead of building something new?
We tried. The problem is that existing tools weren't designed for AI read/write access. Their APIs are comprehensive but not optimized for the kinds of queries an AI agent needs to make quickly and reliably. More importantly, the data models carry years of accumulated complexity that makes it hard to give an AI a clean, accurate view of project state.
Building from scratch meant we could make every schema decision with AI consumption in mind. PlanIT's issue model, sprint structure, and relationship graph are all designed to answer the question: "What does an AI agent need to know to be a useful participant in this project?"
Who PlanIT is for
PlanIT works best for small software teams — typically two to twelve people — who use AI tools heavily in their daily work. If your team is already using Claude, Cursor, or GitHub Copilot for coding, and you're frustrated by the context-switching overhead, PlanIT removes that friction.
It's also a good fit for solo developers and founders who want the discipline of structured project management without the administrative overhead of tools designed for larger organizations.
PlanIT is not the right tool for large enterprise teams that need complex approval workflows, detailed time tracking, or deep integrations with legacy systems. We're deliberately focused on a specific use case and doing it well.
What we've learned building it
Three things surprised us during development.
First, the idea board became one of the most-used features. We added it late in development as a lightweight way to capture product ideas before they became issues. Teams use it far more than we expected — it turns out there's a lot of value in a structured place for "maybe someday" thinking that doesn't pollute the active backlog. We wrote a full walkthrough of how teams use the Idea Board if you want to see it in practice.
Second, the MCP integration changes how people think about their AI tools. Once Claude can see your issues and update them, you start prompting differently. Instead of "write me a function that does X," you start asking "look at issue PL-47 and implement it." The AI becomes a team member, not just a code generator.
Third, simplicity is genuinely hard to maintain. Every feature request sounds reasonable in isolation. Keeping PlanIT focused — resisting the pull toward feature bloat — requires constant deliberate effort. We say no to a lot of things that would make the product more complex without making it meaningfully better.
What's next
PlanIT is live and actively used by teams today. The immediate roadmap focuses on deepening the MCP integration — more commands, richer context, better AI-facing APIs — and improving the iteration planning workflow based on feedback from current users.
The longer-term vision is a project management tool that actively participates in the work, not just tracks it. That means smarter issue suggestions, automated progress updates from AI coding sessions, and tighter loops between what the AI is doing and what the project record shows.
We're building that incrementally, with real teams, based on what actually makes their workflows better.
If that sounds like something your team needs, PlanIT is free to get started for teams up to three people. See the pricing page for team and business plans.