---
title: "How ONESTRUCTION Built the Ishigaki-IDS Foundation Model with AWS GenAIIC"
slug: "how-onestruction-built-the-ishigaki-ids-foundation-model-with-aws-genaiic"
locale: "en"
canonical: "https://ireadcustomer.com/en/blog/how-onestruction-built-the-ishigaki-ids-foundation-model-with-aws-genaiic"
markdown_url: "https://ireadcustomer.com/en/blog/how-onestruction-built-the-ishigaki-ids-foundation-model-with-aws-genaiic.md"
published: "2026-08-29"
updated: "2026-08-29"
author: "iReadCustomer Team"
description: "Discover how ONESTRUCTION collaborated with the AWS Generative AI Innovation Center to build Ishigaki-IDS, a groundbreaking foundation model for BIM and construction."
quick_answer: "ONESTRUCTION built the Ishigaki-IDS foundation model for BIM and construction workflows with AWS GenAIIC by combining synthetic data, a three-stage training pipeline, and verifiable rewards on Amazon EC2, reducing manual layout generation times by up to 80%."
categories: []
tags: 
  - "aws genaiic"
  - "onestruction ishigaki-ids"
  - "bim foundation model"
  - "synthetic data construction"
  - "amazon ec2 ai training"
source_urls: 
  - "https://aws.amazon.com/blogs/machine-learning/how-onestruction-built-the-ishigaki-ids-foundation-model-with-aws-genaiic"
faq:
  - question: "What is the Ishigaki-IDS foundation model?"
    answer: "Ishigaki-IDS is a domain-specific foundation model built by ONESTRUCTION in collaboration with the AWS Generative AI Innovation Center. It is specifically trained and optimized to understand construction workflows, architectural physics, and Building Information Modeling (BIM) structural designs."
  - question: "Why do standard LLMs fail at construction tasks?"
    answer: "Standard language models treat blueprints as simple text or flat images. They fail to understand physical limitations, spatial 3D relationships, and materials load-bearing constraints, which causes them to hallucinate impossible structural layouts and fail local regulatory and safety standards."
  - question: "How did synthetic data help build this foundation model?"
    answer: "Because real-world construction blueprints are proprietary and highly confidential, ONESTRUCTION used parametric programming rules to generate high-fidelity synthetic layouts. This programmatically created vast training datasets containing accurate architectural components without violating client privacy."
  - question: "What is the purpose of the three-stage training pipeline?"
    answer: "The three-stage training pipeline ensures structured learning. It starts with Continued Pre-training to absorb general technical and engineering vocabularies, followed by Supervised Fine-Tuning for task-based design instruction, and ends with Reinforcement Learning with verifiable rewards to optimize output safety."
  - question: "How does this custom AWS solution compare to generic SaaS software?"
    answer: "Generic SaaS tools present rigid features and recurring licensing costs while risking data leakage. In contrast, custom model integration on AWS grants the enterprise full model ownership, protects proprietary workflows, allows continuous updates, and creates unique, defensible IP assets."
robots: "noindex, follow"
---

# How ONESTRUCTION Built the Ishigaki-IDS Foundation Model with AWS GenAIIC

Discover how ONESTRUCTION collaborated with the AWS Generative AI Innovation Center to build Ishigaki-IDS, a groundbreaking foundation model for BIM and construction.

Building custom artificial intelligence models with high precision in data-scarce industries is no longer an impossibility. The collaboration between ONESTRUCTION, a Japanese construction technology pioneer, and the [AWS Generative AI Innovation Center](https://aws.amazon.com/blogs/machine-learning/how-onestruction-built-the-ishigaki-ids-foundation-model-with-aws-genaiic) (GenAIIC) has proved this by developing the **onestruction ishigaki ids foundation model**. This domain-specific large language model is tailored specifically for Building Information Modeling (BIM) workflows, enabling organizations to process complex structural blueprints efficiently using scalable, cost-effective cloud technologies.

This architectural case study demonstrates that a shortage of proprietary real-world data does not have to stall enterprise innovation. By combining synthetic data generation with a highly structured three-stage training pipeline and verifiable rewards running on high-performance Amazon EC2 instances, they successfully trained a highly specialized model. This blueprint provides Thai enterprise operators and small-to-medium businesses (SMBs) with a practical roadmap to overcome data scarcity and build defensible intellectual property.

## How ONESTRUCTION Built the Ishigaki-IDS Foundation Model with AWS GenAIIC to Solve Construction Data Scarcity

Developing a specialized foundation model for structural engineering requires translating unstructured blueprints into mathematically precise, physically viable 3D spatial models. The primary hurdle in this sector is that most high-quality Building Information Modeling (BIM) datasets are privately held corporate assets protected by strict non-disclosure agreements. Relying on generic, off-the-shelf generative models frequently leads to catastrophic hallucinations, where the AI suggests structural components that violate fundamental physics laws.

To bypass these limitations, ONESTRUCTION collaborated with technical advisors from the AWS Generative AI Innovation Center (GenAIIC) to architect an end-to-end framework. Together, they engineered a novel process that extracts raw spatial logic, material dynamics, and local building codes into a structured training format, bypassing the need for public internet scrapes.

* **Zero Proprietary Data Leakage**: All training processes take place within a secure virtual private cloud environment, protecting client files.
* **Accelerated Layout Engineering**: The specialized model automates mechanical, electrical, and plumbing (MEP) routing, reducing design times by up to 80%.
* **Early Conflict Detection**: The model highlights steel column and concrete beam overlapping discrepancies before physical construction begins.
* **Industry Standard Interoperability**: The outputs seamlessly export into major professional BIM suites like Autodesk Revit and ArchiCAD.
* **Scalable Resource Allocation**: The underlying architecture adjusts server capacity based on the compute demands of the design pipeline.

### The Core Challenge of Traditional BIM Workflows

Traditional design validation processes are notorious for causing operational friction due to their high reliance on manual human oversight.

* **Protracted Communication Loops**: Revisions bounce between architectural designers and structural engineers, often adding weeks to project schedules.
* **Data Format Loss**: Converting files between different proprietary CAD programs often strips critical metadata, causing expensive errors.
* **Human Oversight Pitfalls**: Exhausted draftsmen frequently miss minor alignment issues that translate into multi-million baht rework onsite.
* **Prohibitive Software Licensing**: Buying high-end design software seats for every single junior worker burdens growing firms with heavy fixed costs.

### Why Off-the-Shelf Models Fail in Construction

Publicly available foundational models lack an inherent understanding of spatial constraints, structural physics, and construction engineering standards.

* **Lack of Spatial Dimensions**: Generic AI views blueprinted drawings as flat pixels rather than interconnected 3D mechanical structures.
* **Inaccurate Scaling Calculations**: Common public LLMs regularly generate structural column thickness specs that fail local safety criteria.
* **Contextual Jargon Limitations**: Specialized engineering nomenclature and local regulatory slang are frequently misinterpreted by general models.
* **Static Regulatory Databases**: Generic models cannot dynamically reference evolving local zoning laws and urban construction guidelines.

![Accelerated Layout Engineering: The specialized model automates mechanical, electrical, and…](https://land-admin.ireadcustomer.com/api/images/6a9292d8a9aa037f432ed77b)

## The Three-Stage Training Pipeline Architecture That Powers Ishigaki-IDS

Structuring the learning progression of a neural network is crucial for training complex structural design capabilities. This approach, known as the **three stage training pipeline architecture**, systematically introduces domain knowledge, task execution, and safety alignment to achieve maximum reliability.

This pipeline begins with specialized domain-adaptation continued pre-training, introducing deep technical terms to the model base. The second phase refines the model with instruction-based fine-tuning to establish proper task-oriented execution patterns, followed by reinforcement learning to enforce logical and physical consistency.

* **Continued Pre-training Execution**: Ingesting massive corpora of engineering manuals, structural building codes, and material specifications.
* **Automated Data Sanitization**: Implementing data cleaning algorithms to filter out corrupted blueprints, incomplete labels, and redundant scripts.
* **Instruction Dataset Engineering**: Drafting over 50,000 highly targeted design prompts and ideal response pairs to guide the training process.
* **Professional Human Evaluation**: Utilizing seasoned structural engineers to grade the AI designs, providing raw reinforcement inputs.
* **Fine-Grained Weight Adjustment**: Tweaking deep neural layers to curb random text generation while prioritizing consistent structural precision.

### Phase 1: Domain-Specific Continued Pre-training

Establishing a solid cognitive baseline ensures that the AI model understands basic technical relationships before attempting specialized design tasks.

* **Reference Material Integration**: Merging international building guidelines and materials properties databases into a unified vector space.
* **Technical Shorthand Comprehension**: Teaching the network to parse complex abbreviations and engineering notes commonly used in professional construction drawings.
* **System Co-dependence Logic**: Training the model to understand how mechanical pipes must bypass primary load-bearing structural elements.

### Phase 2: Supervised Fine-Tuning with Construction Data

This phase acts as an intensive engineering course that trains the base model to act as an automated architectural assistant.

* **Simulating Contractor Dialogues**: Training the AI to answer field-level design queries with professional, actionable terminology.
* **Discrepancy Resolution Training**: Teaching the model to identify conflicting material specifications across procurement documents.
* **Three-Dimensional Vector Translation**: Training the AI to accurately translate textual building descriptions into coordinate system models.

## Generating Massive Datasets with Synthetic Data for Foundation Models

When real-world industrial data is highly sensitive and scarce, developers can generate high-fidelity synthetic variations to bypass acquisition bottlenecks. Utilizing **synthetic data for foundation models** allows developers to bypass the complex copyright, privacy, and licensing problems associated with proprietary files.

ONESTRUCTION\'s synthetic generator employs strict parametric rules to programmatically build millions of structurally sound virtual layouts. These simulated environments contain detailed metadata on ductwork, concrete structures, and electrical pathways, providing an endless stream of training data.

* **Parametric Rule Constraints**: Defining floor heights and structural spans based on standard physical guidelines to ensure realistic outputs.
* **Automated 3D Generation**: Programmatically creating varied architectural forms ranging from small residences to large commercial centers.
* **Semantic Text Transformation**: Converting three-dimensional structures into rich textual descriptions suitable for language model training.
* **Logical Structural Filtering**: Automatically deleting generated models that feature impossible physics or structural instability.
* **Unlocking Scalable Training**: Eliminating the massive capital requirements usually required to purchase or license real proprietary BIM documents.

### Overcoming the Shortage of Proprietary BIM Files

Intellectual property and security concerns restrict the availability of design documents, particularly within regional markets like Thailand.

* **National Security Protocols**: Government facilities, financial centers, and critical infrastructure assets ban the sharing of interior design files.
* **Analogue Legacy Data**: Older construction archives are stored in flat, printed paper files that cannot easily train modern 3D engines.
* **Incompatible Software Standards**: Fragmented file systems across competing CAD platforms make unified data aggregation extremely tedious.

### Validating Synthetic Output Quality

Before synthetic datasets are fed into the neural network, they must undergo strict automated validation to avoid training the AI on flawed concepts.

* **Physics-Engine Validation**: Running physical simulations to ensure that simulated columns carry load properly without floating.
* **Geometric Intersection Audits**: Automatically checking that utility runs do not physically clip through reinforced concrete structures.
* **Data Density Optimization**: Monitoring the synthetic output to ensure a highly diverse mix of building layouts and material types.

## How Verifiable Rewards on Amazon EC2 Eliminate AI Errors

To prevent generative models from producing inaccurate or unsafe structural designs, companies must pair validation engines with scalable compute power. Deploying these systems using an **amazon ec2 construction foundation model** infrastructure ensures rapid verification loops and highly stable training sessions.

This verifiable rewards mechanism evaluates structural outputs generated by the AI inside a separate simulation environment. If the design complies with strict physical laws, the model receives positive reinforcement, driving the network toward safer and more efficient design solutions over successive training runs.

* **High-Performance GPU Clusters**: Provisioning Amazon EC2 P4d and P5 instances to handle high-demand matrix multiplication operations.
* **Immediate Scoring Loops**: Calculating spatial alignment rewards dynamically to provide instantaneous feedback to the training model.
* **Decoupled Container Environments**: Isolating the evaluation engines from the primary model training loop to optimize overall computing efficiency.
* **Ultra-Low Network Latency**: Leveraging AWS\'s high-speed internal networking to transfer layout structures between systems in milliseconds.
* **Spot Instance Optimization**: Dynamically calling lower-cost compute instances to run non-time-critical validation and batch processing jobs.

### The Reinforcement Learning Setup for BIM Verification

Configuring the reward functions carefully prevents the generative network from cutting corners to exploit the reinforcement scoring system.

* **Optimal Separation Rewards**: Giving the highest points when the model places utility lines at safe, standard-compliant distances.
* **Severe Collision Penalties**: Deducting maximum points if the AI attempts to run pipes directly through primary structural columns.
* **Material Conservation Bonuses**: Providing minor point additions when the model designs shorter utility runs to minimize material waste.

### Computational Scaling on Amazon EC2 Instances

Matching compute capacity to the immediate training load allows enterprises to prevent expensive cloud resources from sitting idle.

* **Auto Scaling Configuration**: Automatically launching additional compute instances when training pipelines face heavy processing queues.
* **Spot Instance Integration**: Cutting compute budgets by up to 90% by running fault-tolerant data generation jobs on spare AWS capacity.
* **High-Throughput Object Storage**: Utilizing Amazon S3 buckets to store and distribute massive training files and model checkpoint weights.

![onestruction ishigaki ids foundation model](https://land-admin.ireadcustomer.com/api/images/6a9292d9a9aa037f432ed781)

## Why the Onestruction Ishigaki Ids Foundation Model Matters for Thai SMBs

ONESTRUCTION\'s success proves that deploying a high-value, domain-specific AI does not require the resources of a global technology giant. This real-world example shows that achieving a high-ROI [Why Custom AI Integration for SMEs Outperforms Off-the-Shelf Software](/en/blog/why-custom-ai-integration-for-smes-outperforms-off-the-shelf-software) is entirely feasible by focusing on targeted datasets and niche workflows.

For Thai construction firms and architectural studios, adopting domain-specific AI models helps level the playing field against conglomerate developers. Furthermore, catching engineering errors early in the design phase prevents costly on-site demolition and rebuild cycles.

* **Rapid Turnaround Times**: Delivering polished, structurally sound 3D initial concept designs to clients five times faster than competitors.
* **Mitigating Skilled Labor Shortages**: Enabling junior architects to draft complex designs that comply with local structural codes.
* **Precise Material Estimations**: Preventing material waste and reducing procurement budgets via highly accurate automated material takeoffs.
* **Minimizing Legal Compliance Risks**: Ensuring all proposed architectural models comply automatically with local building setback laws.
* **Strengthening Client Trust**: Pitching highly polished, technically validated structural designs that project absolute professional reliability.

### Lowering the Barrier to Custom AI Integration for SMEs

Developing custom foundational models was once restricted to tech giants, but modern cloud tools have opened the playing field.

* **Democratic Access to Cloud Compute**: SMBs can access the same high-performance cloud hardware used by global firms via their web browser.
* **Granular Pay-Per-Use [Pricing](/en/pricing)**: Avoiding capital expenditures on local servers by paying only for the exact minutes used during training.
* **Leveraging High-Quality Open-Source**: Building upon powerful, pre-existing open weights models instead of programming architectures from scratch.

### Driving Real Value in Thai Enterprise Digital Transformation 2026

Modern [digital transformation](/en/services/digital-transformation) demands a strategic shift toward technologies that deliver clear financial returns on investment. This focus on practical, high-value AI deployment is a core component of [Why Thai Enterprise Digital Transformation 2026 Demands ROI, Not AI Experiments](/en/blog/why-thai-enterprise-digital-transformation-2026-demands-roi-not-ai-experiments) as businesses move past simple trial integrations.

* **Eliminating Non-Productive Pilot Projects**: Shifting technology budgets away from visual demos toward functional, business-critical systems.
* **Codifying Institutional Knowledge**: Capturing the hard-earned experience of senior engineers into specialized, queryable corporate databases.
* **Expanding Regional Market Access**: Upgrading local design outputs to meet international BIM standards, unlocking AEC sector opportunities.

## Traditional BIM Optimization vs AWS-Powered Generative AI Workflows

Contrasting classic CAD modeling methods with custom generative models on cloud systems highlights the massive gains in design efficiency. Reviewing these comparative metrics helps corporate decision-makers plan their technological roadmap with confidence.

| Assessment Metric | Traditional BIM Process | Ishigaki-IDS on AWS |
| :--- | :--- | :--- |
| Initial Design Layout Phase | 5 to 10 Business Days | Under 2 Hours |
| Structural Collision Incidents | Average 15 Points per Project | Reduced to Near 0 Incidents |
| Specialized Headcount Needed | 4 - 5 Structural Engineers | 1 Architect supervising the AI |
| Operational Scaling Potential | Limited by available human hours | Processes hundreds of layouts in parallel |
| Initial Capital Investment | Low (Annual per-seat software licenses) | Moderate to High (Custom R&D phase) |

Transitioning to automated generative workflows requires updating human skillsets alongside installing software packages to ensure smooth operation.

* **Managing Technological Change**: Addressing initial resistance from traditional drafting teams by showing them how the tool reduces tedious tasks.
* **Structured Prompt Engineering Training**: Equipping engineering teams with the technical skills to query and guide specialized models effectively.
* **Ensuring High-Speed Connectivity**: Maintaining stable, high-bandwidth internet connections to support large cloud file transfers.
* **Preserving Professional Verification**: Requiring all AI-generated designs to undergo final validation and sign-off by a licensed engineer.

## Practical Roadmap to Build a Domain-Specific Model in Data-Scarce Fields

If your enterprise operates in a niche industry where general-purpose public AI models fail, follow this systematic deployment framework.

1. **Audit and Digitize Internal Assets**: Locate and organize your proprietary blueprints, training manuals, and past project data into clean text formats.
2. **Develop Parametric Synthetic Data Generators**: Build programmatic rules to simulate varied industry scenarios, creating rich training datasets safely.
3. **Set Up Secure Cloud Training Infrastructure**: Configure scalable AWS compute instances and select an open-weights model to serve as your foundation.
4. **Implement Verifiable Reward Evaluation Systems**: Program automated validation rules to score the AI\'s outputs, refining performance over successive loops.

Adhering to this structured pathway prevents expensive development errors and ensures your custom model directly solves core operational bottlenecks.

* **Avoid Scope Creep Failures**: Keep the initial training target focused on one clear operational bottleneck rather than trying to automate everything.
* **Prioritize Training Data Quality**: Clean training datasets thoroughly, as feeding low-quality data to the model ruins downstream performance.
* **Validate System Performance On-site**: Test the AI-generated blueprints under actual, real-world field conditions, not just inside virtual environments.
* **Optimize Neural Network Scale**: Select a model size that matches your actual operational needs to avoid wasting your cloud budget.

## Managing the Costs and ROI of Custom AI Infrastructure

Building proprietary foundation models requires rigorous cost controls to ensure the project delivers a strong return on investment. Managing cloud resources on AWS allows companies to monitor and optimize their budgets down to the penny, preventing unexpected cost overruns.

Corporate financial officers must track clear performance indicators, comparing the hours saved by automated drafting against the monthly compute charges.

* **Utilize AWS Budgets Alerts**: Configure weekly spending thresholds that send automated notifications when costs approach 80% of your target.
* **Measure Direct Production Gains**: Track the increase in project throughput achieved by the design team without adding new staff.
* **Clean Up Temporary Compute Resources**: Configure automated policies to delete synthetic data storage volumes after training phases complete.
* **Leverage AWS Savings Plans**: Commit to long-term usage agreements to save up to 72% on compute costs over a multi-year horizon.
* **Value Proprietary Intellectual Assets**: Treat your custom-trained model weights as valuable intellectual property that increases corporate valuation.

### Optimizing Compute Expenses on Amazon Web Services

Applying specific infrastructure optimization techniques helps reduce the computing power required during the model training process.

* **Implement Model Quantization**: Compress the precision of model weights to reduce the memory footprint on high-end GPUs.
* **Leverage Custom AWS Silicon**: Use specialized chips like AWS Trainium to train networks at a lower cost-per-instance.
* **Automate Off-Hours Shutdowns**: Program your non-essential development systems to shut down automatically during nights and weekends.

### Measuring Productivity Gains in Hard Dollars

Translating operational speedups into clear financial metrics helps justify technological investments to shareholders and board members.

* **Reductions in Overtime Expenses**: Cut engineering overtime budgets by up to 35% by automating the most tedious drafting tasks.
* **Higher Bid Win Rates**: Win more large-scale construction contracts by delivering highly accurate structural bids faster than competitors.
* **Avoid Project Delay Fees**: Prevent costly late-delivery penalties by keeping project schedules on track using automated design checks.

## Why Custom AI Integration Beats Generic SaaS for Complex Workflows

Developing a proprietary foundation model provides Thai enterprises with a lasting, highly defensible competitive advantage. Using [Building a Modern Content Automation AI Pipeline Architecture for Enterprises](/en/blog/building-a-modern-content-automation-ai-pipeline-architecture-for-enterprises) to structure your operations ensures your proprietary workflows remain protected behind secure firewalls.

Relying on generic SaaS alternatives exposes your business to vendor lock-in, price hikes, and data leakage risks. By building a custom model with AWS GenAIIC, you secure full ownership of your technical assets and build a highly scalable platform tailored to your exact industry needs.

* **Total Technical Asset Ownership**: Retain full control over your software assets without risking sudden license price increases.
* **Continuous Domain Adaptation**: Update your model with new engineering data whenever your business targets a new market sector.
* **Create New Revenue Streams**: Package and license your specialized model as a software-as-a-service to smaller industry players.
* **Absolute Proprietary Data Security**: Ensure your sensitive structural designs and client files never exit your virtual private cloud.
* **Prepare for Future Automation**: Build a solid digital foundation that easily integrates with next-generation automated robotics and IoT systems.

### The Strategic Advantage of Model Ownership

Custom software assets that are deeply integrated with your operational workflows are exceptionally difficult for competitors to copy.

* **Defensible Market Differentiation**: Outpace competitors who rely on generic tools by offering highly specialized, pixel-perfect designs.
* **Attracting Premier Professional Talent**: Draw top-tier young engineers to your firm by offering a modern, AI-powered work environment.
* **Boosting Corporate Valuation**: Present a proprietary, high-value software portfolio that significantly increases your company\'s valuation.

### Next Steps for Enterprise Leaders

Transforming your enterprise into an AI-powered market leader starts with taking immediate, highly targeted action steps.

* **Assemble a Dedicated Task Force**: Form a cross-functional team featuring lead architects, structural engineers, and IT managers.
* **Schedule an Architecture Review**: Contact certified AWS consultants to draft a secure, scalable model training blueprint.
* **Fund a Targeted Proof of Concept**: Set aside a limited budget to build a small-scale synthetic dataset and test your first model weights.