Most capital project software is scheduling software
Primavera, MS Project, and the dozens of cloud copies of them are great at executing a plan — entering activities, dependencies, baselines, and reporting variance against them. They are not built for the questions a capital committee actually asks. Which of these eight projects do we fund? Is the schedule the team brought us realistic, or is it optimistic by 8 weeks? How much contingency is defensible? What happens to the portfolio if oil drops to $55?
Capital Project AI sits at that decision layer. It ingests the schedule and cost model from the scheduling tools you already use, runs probabilistic and portfolio analysis on top, and gives you the answer in the language of the capital committee — not the language of activity codes.
What decision-grade capital project software does
Four capabilities you don't get from a scheduler — and the reason the capital committee shouldn't be making decisions on scheduling software outputs.
Portfolio prioritization with constraints
Solve "which projects do we fund" as an actual optimization, not a sort-by-IRR. Capital, engineering hours, fabrication slots, regional exposure — all become explicit constraints. The output is a ranked priority list with the trade-offs quantified.
Probabilistic schedule and cost forecasts
Every cost line and schedule activity gets a distribution, not a point. The platform tells you P50, P80, and the variance drivers behind each. The "$200M and 24 months" line in the executive summary becomes "$215M P50, $260M P80, with long-lead vendor delivery driving 31% of the variance."
Stage-gate readiness scoring
Each project gets an objective readiness score against the requirements of the next stage gate — engineering completion, permit status, vendor commitments, risk-register coverage. The capital committee sees which projects are actually ready to advance, separately from which projects have the loudest champion.
Sensitivity to macro and execution drivers
What happens to the portfolio if oil drops to $55? If steel prices rise 20%? If a key permit slips by 6 months? Capital Project AI runs the full sensitivity analysis in seconds — so the committee can stress-test decisions in the meeting itself, not three weeks later.
Why Capital Project AI
- Built around the capital committee. Outputs are the decisions, the trade-offs, and the sensitivity — not a scheduler dashboard.
- Sits on top of your existing PM stack. Ingest from Primavera, MS Project, Excel, ERP. Write back as exports or via API. You don't replace tools you already trust.
- Fast enough to use live. Re-run a portfolio decision with new inputs in under a minute. The committee gets the answer before the next agenda item.
- Built by an ex-Shell capital owner. Founded by an engineer who has lived through $800M of delivered megaprojects.
See your portfolio at the decision layer
Upload a project list and your active backlog — get the priority list, the probabilistic forecast, and the stage-gate readiness scores in under a minute.
Open the Dashboard →What it looks like in practice
A renewables developer is running 12 projects through stage-gates simultaneously: four solar farms in the U.S. Southwest, three wind sites in Texas, two battery storage projects, and three early-stage utility-scale solar in EMEA. The current scheduler shows everything green; the variance reports show some projects slipping but nothing alarming. The capital committee meets quarterly to decide which projects move to the next stage gate and which gets paused.
Capital Project AI runs the same data through portfolio + probabilistic + readiness analysis. The output: three of the four U.S. Southwest solar projects are highly correlated on transmission interconnection risk — funding all four concentrates exposure. Two of the three Texas wind sites have stage-gate readiness scores below 60 (incomplete environmental permitting); advancing them now means they'll bottleneck the engineering pool through next year. The recommendation: defer one of the four U.S. Southwest solar projects, pause the two Texas wind sites at the current gate until permitting catches up, and accelerate one of the EMEA solar projects whose readiness is unexpectedly high. Net portfolio NPV moves up by $34M; expected stage-gate slippage falls by 4 months.
The same engine drives AI project scheduling on the activity layer and megaproject risk management on the largest projects. For owners optimizing the broader allocation question, see capital efficiency software.
Frequently asked questions
How is this different from Primavera or Microsoft Project?
Primavera and MS Project are scheduling tools — they execute the plan you give them. Capital Project AI sits one layer up: it tells you whether the plan is realistic, where the schedule will actually slip, which projects belong in the portfolio at all, and how much contingency to fund. The two tools complement each other; we don't replace them.
Does it integrate with our existing PM stack?
Yes. Capital Project AI ingests schedules and cost models from Primavera P6, MS Project, Excel, and most ERP systems. It runs the probabilistic and portfolio analysis as a layer on top, then writes the recommendations back as either an export or via API.
Who actually uses it day-to-day?
Capital planners, project controls leads, and capital committee members. The output is built around the decisions those roles make: fund-or-cut, contingency level, project priority, and stage-gate readiness. It is not a tool for individual schedulers.