By nPlanLearning Objectives
- Understand what nPlan does and why infrastructure schedules slip
- Evaluate how machine learning forecasts schedule risk
- Assess where nPlan fits on the planning side of major projects
What Is nPlan?
nPlan is a British AI company that forecasts risk in construction project schedules. Large infrastructure projects — rail upgrades, transit lines, water and energy works — are notorious for running late and over budget. A big reason is that their schedules are built largely on expert assumptions about how long each task will take, and those assumptions tend to be optimistic.
nPlan takes a data-driven approach instead. It has trained machine-learning models, including graph neural networks, on one of the world's largest datasets of real project schedules — more than seven hundred thousand as-planned and as-built programs — to learn how construction work actually unfolds versus how it was planned.
💡Key Concept
As-planned versus as-built: The original schedule (as-planned) compared against what actually happened (as-built). Learning the systematic gaps between the two — which kinds of activities reliably slip, and by how much — is the foundation of nPlan's forecasting.
How AI Changes the Workflow
Given a project's schedule, nPlan predicts how long each activity is really likely to take, flags the segments of the program that carry the most risk, and quantifies the overall probability of delay — before the work begins. Instead of discovering that a sequence was unrealistic months into construction, a team can see the risk up front and adjust.
On a multi-billion-pound rail upgrade in northern England, nPlan has reported surfacing risk insights worth many millions of pounds in potential delay exposure. For civil and infrastructure engineers, this turns scheduling from an exercise in expert guesswork into a forecast grounded in how thousands of past projects actually played out.
Who Uses nPlan?
nPlan is aimed at the owners, contractors, and project-controls teams behind large infrastructure and capital projects — rail, transport, utilities, and energy. Its value is highest on big, complex programs where a delay translates into very large cost, and where better foresight into risk materially changes decisions.
Company Details
| Detail | Info |
|---|---|
| Product | nPlan — AI construction schedule risk forecasting |
| Company | nPlan (founded 2017, London, United Kingdom) |
| Method | Deep learning and graph neural networks on past schedules |
| Training data | More than 700,000 as-planned and as-built project programs |
| What it predicts | Activity durations, risky schedule segments, delay probability |
| Notable use | A multi-billion-pound rail upgrade in northern England |
| Target users | Owners, contractors, and project-controls teams on major projects |
| Website | nplan.io |
Strengths
- Data-driven forecasting — grounded in how thousands of real projects unfolded
- Risk before it bites — flags risky activities and delay probability up front
- Built for infrastructure — strongest on large rail, transport, and energy programs
- AI-native — deep learning is the product, not a feature added to scheduling software
- Quantified exposure — translates risk into potential cost and delay days
Limitations and Considerations
- Big-project focus — most valuable on large, complex capital programs
- Schedule-quality dependent — needs a reasonably structured project schedule to analyze
- Forecast, not control — predicts risk; teams still decide how to act on it
- Enterprise engagement — sold to owners and contractors with custom pricing
Pricing
nPlan is enterprise software sold to project owners and contractors, with pricing based on project scope and scale. There is no public list pricing. Contact nPlan for details.
Key Takeaways
- nPlan forecasts risk in construction schedules using deep learning trained on more than 700,000 past project programs
- It predicts realistic activity durations, flags the riskiest parts of a schedule, and quantifies delay probability before work starts
- On a major UK rail upgrade it reported surfacing millions of pounds in potential delay exposure
- It is one of the clearest AI-native tools on the planning and risk side of civil and infrastructure megaprojects
