Project Cost Estimation

Automate construction project cost estimation by AI-driven semantic alignment of quantity take-offs

Business impact

• Cost Estimation Accuracy — AI improves precision by aligning quantity take-offs with standardized cost indexes
• Estimation Time — Automation significantly reduces the time required for generating cost estimates
• Financial Risk — Early discrepancy detection lowers risk of costly estimation errors
• Operational Efficiency — Streamlines workflows by reducing manual data processing and review

Data requirements

• Quantity Take-Off (QTO) spreadsheets (Structured) — Provide detailed work item descriptions and quantities for cost alignment
• Construction Cost Index (CCI) databases (Structured) — Contain standardized cost items and unit rates for matching QTO entries
• Textual descriptions from QTO and cost indexes (Text) — Used for semantic analysis and matching via NLP techniques

AI methods and techniques

• Predictive AI — Predicts cost matches by analyzing semantic similarity between QTO and cost items
• Generative AI — Generates adjusted similarity scores and refines matching through ensemble learning
• Symbolic AI — Applies rule-based text preprocessing and normalization to improve matching accuracy

AI models and model families

BERT, spaCy, Word2Vec, GloVe

Industries

Real-world evidence

8 documented case studies on record.

Companies using this: Andmar Software, Australian Institute Quantity Surveyors AIQS, Carroll Estimating, Cast Consultancy, DPR Construction, Duck Creek Technologies, Trimble, Windover Construction.