Generative AI

Multi-Agent System Empowered by RAG for Next-Level Construction

We leverage Retrieval-Augmented Generation (RAG) to provide on-demand, context-rich connections from multiple data sources.

With this approach, engineers, project managers, and subcontractors all benefit from AI-driven processes that minimize rework, streamline communication, and ensure sustainable project delivery.

Pain Points:

Pain Points:

• Rising Complexity: Construction projects now juggle multiple subcontractors and ever-stricter sustainability requirements.

• Siloed Software: Traditional tools often lack integration, causing data mismatches, manual re-entry, and limited visibility.

• Intricate Designs and Regulations: As projects grow more complex, teams struggle with quick design iterations and reliable compliance checks.

• Missed AI Potential: Conventional systems rarely use advanced analytics, overlooking key safety alerts and cost-optimization insights.

• Need for Real-Time Insights: Without connected data and predictive capabilities, projects face higher rework risks and slower decision-making.


How Lepei Solve It:


Documents
Documents
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1-Unified Data and
Document Ingestion

We integrate site logs, design files, and operational data into a RAG pipeline.

This pipeline references both structured (e.g., IFC, scheduling databases) and unstructured content (meeting notes, contracts).

Our Large Language Model queries this repository for immediate, context-aware insights—reducing the guesswork that often stalls project decisions.

2 - Multi-Agent Coordination

Specialized MAS “agents” handle tasks like scheduling, budgeting, and quality checks.

Each agent continuously updates its knowledge base via Retrieval-Augmented Generation, ensuring design specs and field data remain aligned.

Whether verifying labor availability or adjusting a cost forecast, these agents interact seamlessly, exchanging data in near real time.

3-Generative Design & Predictive Analytics

ConstrAI’s GenAI modules propose new design variations, factoring in cost constraints, environmental guidelines, and workforce capacity.

Machine learning models forecast potential overruns, safety hazards, or equipment failures.

By pairing generative design with RAG-driven retrieval of historical best practices, we offer data-backed proposals that reflect both innovation and practical wisdom.

4-RAG Enhanced Lifecycle Management

Beyond build completion, we harness RAG to consolidate maintenance logs, IoT sensor outputs, and historical wear data.

The system’s multi-agent structure flags upcoming repairs and schedules them efficiently—often before breakdowns occur.

With each update, ConstrAI’s LLM re-checks relevant documents and historical notes, keeping maintenance planning precise and well-informed.

5-Automated Defect
Detection & Compliance

We use advanced computer vision (OpenCV, YOLO-based frameworks) and 3D point cloud processing (Open3D, IFCOpenShell) to spot defects or noncompliant elements.

RAG retrieves any related regulations or contract clauses for immediate reference, alerting site managers to the relevant guidelines.

This fusion of scanning technology with text-based retrieval averts guesswork and speeds up resolution.

Ongoing Case: Ontario Expansion


1-Data Fusion

Historical budgets, design drawings, and real-time drone footage feed into our ingestion pipeline

2-RAG QA

Site managers ask queries like “Are there any safety clauses about elevator installations?”

The system retrieves relevant local codes, merges them with on-site notes, and generates precise recommendations.

3-Multi-Agent Scheduling

Agents dynamically coordinate deliveries, trades, and inspection milestones, adjusting tasks based on real-time availability data

4-Defect Spotlight

Our CV algorithms spot micro-cracks in load-bearing walls.

RAG then surfaces relevant building standards, automating compliance checks and repair directives

5-Predictive Maintenance

Post-construction, sensors track structural health, feeding data to ConstrAI’s analytics.

The system flags anomalies early, ensuring minimal downtime and optimal lifecycle management.

Used Technologies:

Used Technologies:

Foundation 3D Models: Automate clash detection with next-gen 3D geometry engines, integrated into ConstrAI’s pipeline.

RAG for Contextual QA: Retrieval-Augmented Generation ensures the LLM references current, project-specific documents (e.g., safety protocols, local building codes).

Neural Radiance Fields (NeRF): Enhance site mapping for accurate as-built models and design comparisons.

Hybrid Cloud Deployment: Kubernetes-based orchestration and containerization for scalable, secure on-prem or cloud deployment.

Domain-Specific LLMs: Fine-tuned GPT or Llama 2 variants, adapted specifically for construction documentation and regulation references.


Conclusion:

Conclusion:

By unifying Multi-Agent System with Retrieval-Augmented Generation, ConstrAI transcends the limitations of conventional construction software.

It unifies everything—cost modeling, scheduling, compliance checks, and predictive maintenance—into a single intelligence-driven platform.

This holistic approach transforms day-to-day processes, slashing rework and fostering agility in an industry known for tight margins and high stakes.

If you’re looking for the 2025-ready solution to manage complex builds while driving down costs and risks, ConstrAI stands ready to lead the way.


For more information on the technologies used, please follow the link

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