I built an AI Gantt planner with real scheduling and deadline risk analysis

Hey DEV Community!

I'm Stefano, a software architect who enjoys turning complicated problems into simpler software.

After working on complex projects for years, one thing kept bothering me: creating a project plan is hard, but keeping that plan realistic is even harder.

A Gantt chart might look perfect on Monday. Then one task gets delayed, a dependency changes, someone becomes unavailable, and suddenly the entire schedule needs to be reconsidered.

That's what inspired me to build Planitude.

But I didn't want to create just another task management tool.

I wanted to explore a different approach: what if AI could generate the initial plan, while a proper scheduling engine handled the calculations?

1. Let AI handle the initial planning

Creating a detailed Gantt chart from scratch takes time.

With Planitude, you can describe your project in natural language, and AI helps generate:

• Project phases and tasks
• Suggested task durations
• Dependencies between activities
• Milestones and project structure

Instead of manually creating dozens of tasks, you start with a proposed plan that you can review and improve.

Of course, AI estimates aren't guaranteed to be accurate. They're suggestions, not promises.

And that's where the next part becomes important.

2. Real scheduling, not just pretty Gantt bars

One of the design decisions behind Planitude is to separate AI-generated suggestions from deterministic scheduling calculations.

The scheduling engine handles:

• Finish-to-start, start-to-start, finish-to-finish and start-to-finish dependencies
• Working calendars and task constraints
• Lead and lag between tasks
• Critical-path calculations
• Automatic date recalculation

For example, imagine you're managing a renovation project.

Your supplier announces a three-day delay. That delay affects an installation task, which in turn affects testing and final delivery.

Instead of manually moving every Gantt bar, the scheduling engine recalculates the affected tasks and the project's completion date.

AI helps propose the plan. The scheduling engine keeps its dates consistent.

3. What about the risk of missing deadlines?

A project completion date alone doesn't tell the whole story.

A schedule might say your project finishes on December 15, but how confident should you be?

Planitude uses Monte Carlo simulations with three-point duration estimates to explore different scheduling outcomes.

You can examine:

• P50, P80 and P95 completion dates
• The estimated probability of meeting a deadline
• Tasks contributing the most scheduling risk

The goal isn't to magically predict the future. It's to make uncertainty visible before it becomes a problem.

4. Keep your existing tools

I also wanted to avoid forcing teams to abandon the software they already use.

Planitude supports Microsoft Project imports and integrates with Jira Cloud, Azure DevOps, Microsoft Teams and Slack.

There's also an MCP server, allowing AI assistants such as ChatGPT and Claude to work with project information through supported integrations.

The idea is to connect planning with execution, rather than creating another disconnected system.

5. Why make it free?

Professional project planning shouldn't require expensive software licenses, especially for small teams, independent developers and early-stage founders.

That's why Planitude is free to use, without per-seat subscription fees, and supported by advertising.

I'm still working on improving the experience and learning which features deliver the most value.

Try it and tell me what you think

If you'd like to see the project:

Planitude — Free AI Project Planning

I'd especially appreciate feedback from developers and project managers.

Here's a question I've been thinking about:

If an AI suggests that a development task will take five days, what would make you trust that estimate?

Historical project data? Confidence intervals? Explanations of its assumptions? Human review?

I'd love to hear how you would approach this problem.

Thanks for reading!