AI-Driven BIM Classification and Metadata Structuring for SketchUp Components

Functional Requirements

• Train and fine-tune an agentic AI workflow capable of automatically classifying and enriching SketchUp-native 3D components with IFC4.x-compliant metadata with ≥95% accuracy
• Develop an approach that has flexibility in loading and adapting to different IFC schema for local compliance with BCA CorenetX (i.e. IFC-SG)
• Enable real-time classification and metadata injection directly within design workflows via modelling software plug-ins, starting with SketchUp Ruby/C API
• Web-based development for customer in-house digital content management platform, with these features:
  
  • Support batch processing of component libraries to streamline digital asset onboarding, with informed progress from the platform on estimation of remaining time, and aborting and resuming tasks
  • Able to deploy to an in-premise cloud platform via API (e.g. Microsoft Azure, AWS, Alibaba Cloud, etc.)
  • Support both project-specific, and client-specific modes of content management
  • Support storing of metadata mapped to individual components

Technical Requirements

Core Requirements:

• Full compatibility with SketchUp (.skp files), with future extensibility to Revit (.rfa, .rvt), ArchiCAD, and Rhino
• RESTful API access to enable secure communication between design tools and the backend AI engine
• Modular agentic AI architecture encompassing classification, reasoning, validation, and formatting workflows
• Compliance with IFC 4.x AND IFC-SG schemas, producing export-ready metadata
• Support enterprise deployment features, including Bring-Your-Own-Key (BYOK) capability for OpenAI API-based processing, or in-premise infrastructure processing
‍

Additional Preferences:

• Real-time classification and metadata injection during component creation or import
• Native integration with Trimble Connect for model federation and cross-disciplinary coordination
• Plug-ins for additional BIM model authoring tools (in order of priority: Rhino, Blender, ArchiCAD, and Revit) to enable in-app access to AI functionality
• Transform disorganised or unstructured geometry into structured, federated digital assets ready for OpenBIM workflows‍

‍Expected Outcomes

• Reduction in BIM preparation time by 75-85%
• Savings in manpower cost of at least 60%
• Improvement in consistency, efficiency and drastically reduce time to organising data in digital deliverables across teams‍

Deployment Environment and Constraints

Even for a modest architectural BIM model, such as a small residential or low-rise building, the number of components often exceeds 2,000 objects. In the case of M Moser’s typical workplace interior projects, this number routinely exceeds 5,000 components. The solution must be able to handle those large numbers.

Also, the environment includes both in-house and external content with inconsistent standards. The solution must integrate with desktop modelling tools and support cloud-based collaboration workflows. It should work within project teams spread across regional offices with varying levels of BIM maturity and network connectivity.

M Moser will conduct the pilot within several ongoing workplace interior fit-out projects in Singapore. It should be able to run batch classification across >1,000 in-house and external components, validate all outputs through manual cross-check, and be benchmarked on classification accuracy, layer conformity, and time per component.

M Moser will provide:

• Curated Test Library: A verified dataset of 4000 SketchUp components sourced from M Moser’s in-house library, 3D Warehouse, and project-specific content, spanning furniture, lighting, equipment, doors, partitions, and more. Each file includes benchmarked classification references for validation and training.
• AI-Ready Infrastructure: A fully operational agentic pipeline, structured into four modular AI roles (Visual, Classification, Validation, and Formatting), is already integrated with the SketchUp Ruby API. This enables plug-and-play testing and seamless I/O for external teams
• Real-World Projects: Multiple workplace projects in Singapore, Kuala Lumpur, Manila, Hong Kong, Shenzhen, and Guangzhou are available for pilot testing. These sites reflect realistic BIM conditions and workflows, allowing for robust use-case validation in both live and sandboxed environments
• Domain Expertise and Advisory: M Moser’s internal GAIA (Generative AI Agency) global team and Singapore office consisting over 100 professionals from various disciplines will provide constant feedback loops throughout the development stage. Additionally, the M Moser team includes advisors and practitioners familiar with IFC schema, ensuring the solution aligns with local and international IFC schema requirements
• Integration Support: We will provide a sandbox enabled for the selected Solution Provider with unlimited storage and full access with Trimble Connect Workspace API Access for testing federated workflows and various real-life coordination and collaboration scenarios

Throughout the pilot, M Moser’s BIM specialists, MEPF Engineers, Workplace Designers, Project Managers will participate in structured testing and review sessions, offering detailed insights into tagging reliability, efficiency gains, and the tool’s fit within existing coordination workflows. Key performance indicators will include classification precision, average processing time per object, and interoperability with other BIM platforms such as Revit and IFC-compliant environments.‍

Commercialisation and Scaling

Upon validation, M Moser will adopt the platform across their global project teams. The solution will be positioned as a SaaS product offering tiered pricing: free starter tools for smaller teams, pay-per-use API plans for growing firms, and full enterprise licensing with on-premises deployment options. A budget of S$70,000 is estimated for the first 6-9 months, focused on AI models fine-tuning and training, SketchUp plugin development, UI/UX design and implementation, SaaS deployment tests, and strategic partnerships with existing CDE platform providers like Trimble.

With an estimated 500,000 BIM projects globally each year, M Moser envisions scaling the platform to serve an addressable market of S$250 million per year, with potential to become the industry’s preferred solution for AI-assisted BIM classification.