Construction Cost AI
Causal cost prediction and risk analytics for capital construction projects, from feasibility to handover.
Causal cost prediction for capital construction projects
Construction Cost AI estimates project cost, schedule and risk from feasibility through handover. Instead of extrapolating historic averages, the engine models the causal drivers behind cost — material markets, procurement timing, design complexity, labour productivity and site logistics — so teams can see not only the predicted number, but which decisions actually move it.
Key capabilities
Every prediction ships with driver attribution and a confidence interval, ready for board and lender review.
Simulate design, procurement and phasing changes and read their causal impact on cost and schedule.
Steel, cement, MEP and labour indices are tracked per region so estimates track live market conditions.
Contingency is derived from modelled variance rather than a flat percentage.
Use cases
- Feasibility and business case
Produce defensible early-stage budgets when the design is still 20% complete.
- Tender benchmarking
Compare contractor bids against a causal baseline to spot loaded rates and unrealistic productivity.
- Portfolio governance
Track cost drift across dozens of projects and flag the assets driving overrun risk.
Frequently asked questions
How accurate is the cost prediction?
Typical confidence is 90–95% on projects with comparable historical data. Every output includes the confidence band and the drivers behind it, so estimators can apply judgement where data is thin.
What data do I need to get started?
A project brief, location, gross floor area, typology and target programme are enough for a first pass. Adding a bill of quantities or historic project data sharpens the model considerably.
Does it replace a quantity surveyor?
No. It gives estimators and QS teams a causal baseline and instant scenario testing, so their time goes into judgement rather than spreadsheet rebuilds.
Which regions are supported?
The model is calibrated for Europe and the GCC, with regional cost indices for France, the UK, Saudi Arabia and the UAE.