By industry
Oil & Gas
Upstream, midstream, and integrated operators with capex programs ranging from $200M brownfield revamps to $5B LNG trains. Decision cadence: quarterly capital committee, annual plan, stage-gate reviews per project. Typical value: 200-300 bps of capital efficiency and 15% tighter sanctioned schedules.
Engines used: Forecast, Optimize, De-risk (most active use)
Related: AI capital project risk management, megaproject risk management, capital allocation software
Infrastructure
Transmission, highways, water, rail, and public-private partnerships. Capex programs driven by long-horizon regulated returns and permit-constrained schedules. Typical value: early-action recommendations on right-of-way acquisition and environmental permitting, which drive 15-25% of schedule variance.
Engines used: Forecast, De-risk (most active use)
Related: probabilistic project planning, Monte Carlo project simulation
Power & Nuclear
Utility-scale thermal, nuclear new-build and refurbishment, grid-scale storage. Capex programs exposed to regulatory timing, long-lead forgings, and commissioning risk. Typical value: variance decomposition on commissioning activities that typically explain 30%+ of schedule variance.
Engines used: Forecast, De-risk, Deliver (most active use)
Renewables
Utility-scale solar, onshore and offshore wind, battery storage, green hydrogen. Portfolio programs with many projects at various stage gates, exposed to PPA timing, interconnection queues, and vendor concentration. Typical value: portfolio-level correlation handling that surfaces concentration risks individual-project analysis misses.
Engines used: Optimize (most active use)
Related: project portfolio optimization with AI, capital efficiency software
Manufacturing
Specialty chemicals, semiconductors, pharmaceuticals, advanced materials. Capital projects tied to demand forecasts with material uncertainty. Typical value: real-options valuation of stage-gated FEEDs that are often under-valued by deterministic NPV.
Engines used: Optimize, Forecast
Related: project portfolio optimization with AI, capital project management software
Data Centers
Hyperscale build programs with concurrent campus construction, grid interconnection, and long-lead cooling/power equipment. Capex programs measured in $B per year per operator. Typical value: schedule realism against vendor delivery, which is the dominant variance driver in the current environment.
Engines used: Forecast, Deliver
Related: AI project scheduling, capital project management software
By persona
Capital committees & CFOs
The primary users for Optimize and the output consumers for Forecast and De-risk. Decision cadence is quarterly (capital committee) and annual (planning cycle). What they get: priority lists, trade-off tables, sensitivity walk-throughs in the language of the committee — not the scheduler. Typical workflow: pre-meeting prep with the model, live stress-testing during the meeting, post-meeting ratification. Most relevant: capital allocation software, project portfolio optimization with AI, capital efficiency software.
Capital planners & project controls leads
The day-to-day users of the platform. Run probabilistic schedule and cost analysis at sanction and at every stage gate, track execution against the probabilistic plan, and brief the capital committee on changes. What they get: the analytic layer they've been doing by hand (or not at all) in Excel, running 100x faster. Most relevant: probabilistic project planning, Monte Carlo project simulation, AI project scheduling.
EPC contractors & engineering firms
Bid evaluation against active backlog, schedule realism against historical productivity, contingency sizing that's defensible in the bid review. What they get: a quantified bid recommendation and a clear view of where the proposed schedule will actually slip. Most relevant: AI for EPC contractors, AI project scheduling, Monte Carlo project simulation.
PE and infrastructure investors
Portfolio construction with correlation-aware position sizing, project-level diligence using probabilistic models calibrated against reference-class data, and ongoing portfolio monitoring. What they get: the quantitative rigor of liquid-markets portfolio math applied to physical capital. Most relevant: capital allocation software, capital efficiency software, project portfolio optimization with AI.
Where to start
Most customers enter through one of three patterns: a scoped pilot on a specific portfolio segment (most common for Enterprise), a single stage-gate decision where the probabilistic model needs to surface something the deterministic plan isn't (common for first-time buyers), or an annual capital planning cycle where the existing Excel model has visibly broken (common for teams who have outgrown their legacy tooling).
See Pricing for commercial structure, Platform for a deeper look at the four engines, or contact us to scope a pilot.
Match your use case
Tell us your industry, portfolio size, and the decision you're trying to improve. We'll come back with a scoped engagement.
Talk to Sales →