ONESTRUCTION has built Ishigaki-IDS, a foundation model for construction and building information modeling workflows, with technical advisory support from the AWS Generative AI Innovation Center. The AWS case study focuses on how the team handled a domain where high-quality training data can be scarce.
The approach combined synthetic data, a three-stage training pipeline, and verifiable rewards on Amazon EC2. In plain terms, synthetic data can expand examples when real project data is limited, while verifiable rewards give the model clearer signals about whether an output meets defined task requirements.
The project shows why industry-specific models are becoming a separate track from general chatbots. Construction workflows involve drawings, structured building data, domain vocabulary, and strict accuracy needs. A specialized model can be tuned for those constraints, but it also depends on careful evaluation because mistakes in design or project documentation can be costly. The useful takeaway is that enterprises may need domain models built around their workflows, not just larger general-purpose systems.