Enterprise AI Analysis
AI-Driven Automation of Construction Cost Estimation: Integrating BIM with Large Language Models
Authors: Mohamed Abdelsalam, Amr Ashmawi and Phuong H. D. Nguyen
The construction industry faces challenges in estimating costs because the processes are time-consuming and involve a high likelihood of making errors. For instance, quantity take-offs are often inaccurate, and there is not a simple way to integrate data from Building Information Modeling (BIM) platforms and cost databases. This study introduces a framework that utilizes the Model Context Protocol (MCP) to ensure seamless integration between large language models (LLMs) and BIM models through Autodesk Revit in order to enable fully automated cost estimation workflows. The developed system combines an AI-powered MCP server with cost databases that are standard in the industry, such as the 2025 Craftsman National Building Cost Manual and the ZIP code-based location modifiers. This system enables LLMs to automatically obtain quantities from BIM models, match components to cost items, make regional changes, and make professional cost estimates. A case study of estimating the cost of an electrical system shows that the framework can reduce estimation time from 2.5–3.5 h (manual baseline) to 42.3 ± 3.7 s (n = 5 runs, warm start), representing a 98.6% efficiency gain, while being more accurate with respect to industry standards. The system processed 187 BIM elements in three component groups (receptacles, conduits, and panels). It automatically matched them to the right cost database items, used location-specific modifiers for ZIP code 01003, and made a full cost estimate of USD 13,945.81 with detailed breakdowns and a percent difference of %5.1 of the manual estimation. This research enhances automation in construction by developing a methodology for AI-BIM integration using standardized protocols, shows the practical application of AI in construction workflows, and provides empirical evidence of the advantages of automation in cost estimation processes. The results indicate that MCP-based AI integration presents a novel approach for construction automation, delivering improvements while applying professional standards of accuracy and availability.
Executive Impact
Leveraging AI and BIM, our framework delivers unprecedented efficiency and accuracy in construction cost estimation.
0%
Efficiency Gain
0%
Cost Variance (Automated vs. Manual)
0
BIM Elements Processed
0s
Average Estimation Time
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Integration Protocol
Performance & Efficiency
Accuracy & Reliability
Enterprise Application
Enterprise Process Flow: MCP-Based Estimation
Phase 1: Model Interrogation & Quantity Extraction
→
Phase 2: DB Integration & User Parameterization
→
Phase 3: Intelligent Component-Cost Matching (AI Reasoning)
→
Phase 4: Cost Calculation & Synthesis
→
Phase 5: Professional Reporting & Visualization
M × N to M + N
Simplified Integration Complexity via MCP
The Model Context Protocol (MCP) transforms complex M × N integration problems (many tools to many data sources) into a manageable M + N problem, significantly reducing integration complexity in construction workflows. This enables AI systems to dynamically access data, find capabilities, and invoke functions across various platforms.
| Aspect | Current Literature/Practice | Advancement in This Study |
|---|---|---|
| Integration Method |
|
|
| Automation Scope |
|
|
98.6%
Reduction in Estimation Time
The framework reduced estimation time from a manual baseline of 2.5–3.5 hours to an average of 42.3 seconds with AI-driven automation.
Measuring Performance: Automated vs. Manual
The automated system achieved a mean execution time of 42.3 s (SD = 3.7 s, n = 5) from user command initiation to complete HTML report generation. This included MCP server initialization, Revit model interrogation, cost database parsing, component matching, calculations, and report generation.
In contrast, manual estimates by a professional estimator with 8+ years of experience using standard industry practice (Revit Schedules for QTO, manual database lookup, spreadsheet calculations) took between 2.5, 3.0, and 3.5 hours (mean: 3.0 h).
This stark difference quantifies a 98.6% efficiency gain, making real-time cost feedback possible during iterative design phases.
| Study | Automation Level | Time Efficiency |
|---|---|---|
| This Study (MCP + LLM) | Full end-to-end |
|
| Alazawy et al. (2024) [34] (SVM-BIM) | QTO only |
|
| Attia (2025) [35] (AI-BIM integration) | Project workflows |
|
| Traditional BIM QTO | QTO only |
|
100%
Cost Database Matching Accuracy
The AI system demonstrated perfect accuracy in matching BIM components to the appropriate cost items in the Craftsman National Building Cost Manual 2025 and applying location modifiers for ZIP code 01003.
Accuracy Assessment: Quantity Extraction & Overall Estimate
Quantity Extraction Accuracy:
- Receptacles: Automated = 30, Manual = 29. Discrepancy: 3.4% overcount (AI misclassified a junction box).
- Conduit: Automated = 2450 LF, Manual = 2526.5 LF. Discrepancy: 3.1% undercount (AI aggregated connected conduit runs differently).
- Panels: Automated = 2, Manual = 2. Discrepancy: 0% (exact match).
Overall Estimate Accuracy: The AI-generated total cost was USD 13,945.81. The manually verified estimate (using corrected quantities) was USD 14,006.28. This resulted in an initial variance of -5.1%, which falls within acceptable ranges for preliminary estimates (±10–15%) but exceeds the tighter tolerance for detailed bid-level estimates (±5–8%). Professional validation is still crucial for production use.
| Study | Automation Level | Accuracy Metric |
|---|---|---|
| This Study (MCP + LLM) | Full end-to-end |
|
| Alazawy et al. (2024) [34] (SVM-BIM) | QTO only |
|
| Elmousalami (2020) [27] (Various ML) | Prediction only |
|
| Traditional BIM QTO | QTO only |
|
Transforming Construction Workflows with AI
This research confirms that the convergence of LLMs, standardized protocols (MCP), and BIM data represents a transformative shift for construction cost management. The developed framework acts as a powerful, accurate, and immensely efficient assistant capable of reshaping estimating workflows.
The key finding is the profound efficiency gain: 2.5–3.5 hours of manual effort reduced to under 45 seconds. This accelerates real-time cost feedback during design iterations.
The most effective application is a hybrid human–AI collaborative workflow, where AI generates rapid preliminary estimates, freeing skilled professionals to focus on validation, value engineering, and complex judgment. This synergy leverages AI's speed and consistency with human contextual understanding and expertise.
| Aspect | Current Literature/Practice | Advancement in This Study |
|---|---|---|
| AI Application |
|
|
| Solution Replicability |
|
|
| Performance Validation |
|
|
Calculate Your Potential ROI
Estimate the efficiency gains and cost savings your enterprise could achieve with AI-driven automation.
ROI Estimator
Your AI Implementation Roadmap
A structured approach to integrating AI into your enterprise workflows for maximum impact.
Phase 1: Discovery & Strategy
Assess current workflows, identify AI opportunities, and define clear objectives and KPIs. Establish core team and pilot scope.
Phase 2: Pilot Program Development
Develop and integrate initial AI solutions (e.g., MCP-based BIM integration) on a small scale. Test, gather feedback, and iterate.
Phase 3: Scaled Deployment & Training
Expand AI solutions across relevant departments. Provide comprehensive training for your teams to ensure smooth adoption and maximize utilization.
Phase 4: Optimization & Continuous Improvement
Monitor performance, fine-tune AI models, and integrate new data sources. Establish governance for ongoing AI evolution and innovation.
Ready to Transform Your Enterprise?
Connect with our AI specialists to explore how these advancements can revolutionize your operations and drive growth.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat