AI for Feasibility Studies
AI for conducting construction project feasibility analysis.
Definition
AI for feasibility studies helps analyze project viability before commitment. AI can analyze site constraints, estimate preliminary costs, assess regulatory requirements, and evaluate project risks. This improves feasibility study accuracy and efficiency.
In Depth
Feasibility studies evaluate whether a proposed construction project is viable — analyzing the site's development potential, the probable construction cost, the permitting timeline, and the financial return. AI accelerates each of these analyses, enabling teams to evaluate opportunities faster and with more data than manual analysis allows.
The site development analysis combines zoning constraints, building code limitations, and site physical characteristics to determine what can be built. AI calculates the maximum allowable density (FAR × site area), the maximum building height, the parking requirements, and the setback-constrained building footprint — producing a development envelope that defines the range of feasible options.
Cost benchmarking against comparable completed projects provides a more reliable early estimate than parametric cost-per-square-foot factors. AI identifies comparable projects from the firm's database and public records, adjusts for location, escalation, and project-specific factors, and produces a probable cost range with confidence intervals that help owners make informed go/no-go decisions.
Examples
Analyzing site constraints
Estimating preliminary costs
Assessing regulatory requirements
Nomic Use Cases
See how Nomic applies this in production AEC workflows:
Frequently Asked Questions
AI for feasibility studies helps analyze project viability before commitment. AI can analyze site constraints, estimate preliminary costs, assess regulatory requirements, and evaluate project risks. This improves feasibility study accuracy and efficiency.
Analyzing site constraints. Estimating preliminary costs. Assessing regulatory requirements.
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