How ONESTRUCTION built the Ishigaki-IDS foundation model with AWS GenAIIC

The ability to custom-tailor large language models for specialized, data-scarce domains just became significantly more accessible, offering a path to unlock highly targeted AI solutions for challenging industries. This AWS Machine Learning case study details how a construction technology company, working with the AWS Generative AI Innovation Center, developed a specialized foundation model named Ishigaki-IDS. They achieved this by strategically leveraging synthetic data generation, a sophisticated three-stage training pipeline, and verifiable rewards on Amazon EC2, demonstrating a practical methodology for building domain-specific AI where traditional data reservoirs are sparse. This development directly impacts founders and operators in fields long considered too niche or data-poor for effective AI application. Consider an independent SaaS founder in, say, Toledo, Ohio, building tools for specialized manufacturing. They can now explore building a truly bespoke AI assistant for quality control documentation by generating synthetic examples of defects and specifications, rather than waiting years to accumulate real-world data. Or imagine a mid-sized urban planning firm in Dallas, Texas, needing to automate the initial drafting of environmental impact statements. Instead of a general-purpose LLM offering vague summaries, they could train a model on public domain environmental regulations and synthetic scenarios, generating precise, context-aware drafts in minutes. Even a small architectural practice in Portland, Oregon, could use this approach to create a BIM (Building Information Modeling) assistant that understands specific local zoning codes and material constraints, greatly streamlining early-stage design validation. The core insight here is that domain expertise, not just massive data volume, can now drive powerful AI model development. This shifts the focus from passively collecting vast datasets to actively engineering relevant training data and refining models with structured feedback. To begin exploring this, consider a specific, data-limited problem within your own operations or product. Identify the type of data most needed and spend an hour this week brainstorming 5-10 distinct categories of synthetic data that could represent this. Then, using a readily available general-purpose LLM, experiment with generating a few dozen synthetic examples based on those categories, evaluating their quality and relevance to your specific domain challenge.