Before any modelling, we establish what the project is actually trying to achieve: a regulatory approval, an investor requirement, an operating cost target, a reputational position, or all four. The technical scope follows from that, not from a standard service list.
A defensible starting point: the boundary, the assumptions, the emission factors, the climate file, the operating profile. Documented and agreed at the outset, because every subsequent result is measured against it.
Simulation across the disciplines that apply, covering energy, daylight, carbon and cost, run as a set of comparable options rather than a single scheme. Where the option space is large, parametric and AI-assisted workflows widen the search.
Findings are presented as a decision, not a data dump: what we recommend, what it costs, what it delivers, what it depends on, and what the alternative would have been. Assumptions are stated so the recommendation can be challenged.
Recommendations are tracked through specification, tender, submittal review and construction. A measure that is designed but not procured has delivered nothing, and this is where most performance is lost.
Once the asset is running, measured performance is compared against the model. The gap is investigated, reported, and fed back into the next project. This is what keeps the modelling honest.
Applied to a single building or an entire organization, the sequence is the same.
Establish the standard the asset will be held to.
Quantify the current and predicted performance.
Test options and select on evidence.
Deliver, verify and report the reduction.