<h2><strong>AI Scaling with TwinKnowledge and AWS Collaboration </strong></h2>
<p><img loading="lazy" decoding="async" class="alignnone size-medium wp-image-14373" src="https://apiblog.cloudastra.co/wp-content/uploads/2025/02/Scaling-AI-in-AEC-Industry-with-TwinKnowledge-and-AWS-PACE-Collaboration-300x300.png" alt="" width="300" height="300" srcset="https://apiblog.cloudastra.co/wp-content/uploads/2025/02/Scaling-AI-in-AEC-Industry-with-TwinKnowledge-and-AWS-PACE-Collaboration-300x300.png 300w, https://apiblog.cloudastra.co/wp-content/uploads/2025/02/Scaling-AI-in-AEC-Industry-with-TwinKnowledge-and-AWS-PACE-Collaboration-150x150.png 150w, https://apiblog.cloudastra.co/wp-content/uploads/2025/02/Scaling-AI-in-AEC-Industry-with-TwinKnowledge-and-AWS-PACE-Collaboration-768x768.png 768w, https://apiblog.cloudastra.co/wp-content/uploads/2025/02/Scaling-AI-in-AEC-Industry-with-TwinKnowledge-and-AWS-PACE-Collaboration-96x96.png 96w, https://apiblog.cloudastra.co/wp-content/uploads/2025/02/Scaling-AI-in-AEC-Industry-with-TwinKnowledge-and-AWS-PACE-Collaboration.png 1024w" sizes="auto, (max-width: 300px) 100vw, 300px" /></p>
<h2 data-start="428" data-end="449"><strong data-start="431" data-end="447">Introduction</strong></h2>
<p data-start="451" data-end="751">The architectural, engineering, and construction (AEC) industry is undergoing a digital revolution, with artificial intelligence (AI) and machine learning (ML) at the forefront. TwinKnowledge, an innovative AEC AI company, is redefining how construction drawings are designed and reviewed at scale.</p>
<p data-start="753" data-end="1177">In collaboration with Amazon Web Services (AWS) Prototyping and Cloud Engineering (PACE) team, TwinKnowledge addressed a crucial challenge: scaling their AI-driven computer vision (CV) platform to process thousands of architectural drawings efficiently. This solution leveraged model fine-tuning with AWS services, enhancing large language models (LLMs) and CV models to improve construction document accuracy.</p>
<p data-start="1179" data-end="1557">TwinKnowledge’s mission is to minimize errors in construction drawings and boost project efficiency. However, they faced a scalability limitation that restricted their ability to implement a broader AI strategy. With Amazon SageMaker’s MLOps capabilities, AWS helped TwinKnowledge scale model fine-tuning across clients while maintaining high performance and accuracy.</p>
<p data-start="1559" data-end="1739">This blog explores how this solution not only solved scalability constraints but also laid the foundation for enhanced AI-driven insights in construction document processing.</p>
<h3 data-start="1741" data-end="1787"><strong data-start="1744" data-end="1785">1. The Industry’s Information Problem</strong></h3>
<h4 data-start="1789" data-end="1834"><strong data-start="1793" data-end="1832">Challenges in Construction Drawings</strong></h4>
<p data-start="1836" data-end="2195">Two-dimensional (2D) drawing sets remain the primary reference in construction projects. Over 70% of AEC professionals still use printed blueprints, leading to inefficiencies and errors. To mitigate these risks, companies invest in quality assurance (QA) and quality control (QC) teams, yet mistakes persist due to the complexity of these documents.</p>
<p data-start="2197" data-end="2404">A PlanGrid-FMI industry report highlights that nearly 25% of construction rework stems from incorrect project information, costing the U.S. industry $14.3 billion annually. Professionals spend:</p>
<p><strong data-start="2408" data-end="2428">5.5 hours weekly</strong> searching for project data</p>
<p><strong data-start="2460" data-end="2478">5 hours weekly</strong> resolving information-related conflicts</p>
<p><strong data-start="2523" data-end="2541">4 hours weekly</strong> fixing errors and rework</p>
<p data-start="2570" data-end="2693">These inefficiencies demand an AI-driven model fine-tuning approach to automate document review and enhance accuracy.</p>
<h4 data-start="2695" data-end="2732"><strong data-start="2699" data-end="2730">TwinKnowledge’s AI Solution</strong></h4>
<p data-start="2734" data-end="3002">TwinKnowledge integrates LLMs and CV models to process textual and graphical data in construction drawings. However, off-the-shelf AI models failed to accurately interpret drawing set structures, requiring a customized model fine-tuning approach.</p>
<p data-start="3004" data-end="3345">Their proprietary AI pipeline first extracts textual and graphical information separately. Then, a fine-tuned CV model maps design details to lessons learned, requirements, and standards. However, scalability remained a challenge, requiring an automated AI scaling infrastructure to support their expanding client base.</p>
<div>
<h3 data-start="3347" data-end="3401"><strong data-start="3350" data-end="3399">2. Scaling Model Fine-Tuning for AI Workflows</strong></h3>
<h4 data-start="3403" data-end="3453"><strong data-start="3407" data-end="3451">AI Challenges in Construction Compliance</strong></h4>
<p data-start="3455" data-end="3524">The AEC industry struggles with compliance verification due to:</p>
<p><strong data-start="3529" data-end="3564">1. Massive amounts of project data</strong> that must be reviewed</p>
<p><strong data-start="3592" data-end="3613">2. Limited AI models</strong> capable of mapping design compliance effectively</p>
<h3 data-start="3666" data-end="3712"><strong data-start="3670" data-end="3710">Model Fine-Tuning for AI Scalability</strong></h3>
<p data-start="3714" data-end="3963">TwinKnowledge leverages model fine-tuning to customize AI models for different clients and projects. Their solution transforms graphical data into structured knowledge and embeds it within LLMs to provide real-time, AI-driven insights.</p>
<p data-start="3965" data-end="4023">The architecture includes two distinct AI pipelines:</p>
<p><strong data-start="4028" data-end="4056">1. Text Processing Pipeline</strong> – Extracts and processes project data from contract documents</p>
<p><strong data-start="4124" data-end="4152">2. Computer Vision Pipeline</strong> – Processes drawing sets to map design structures</p>
<p data-start="4210" data-end="4351">The result is a scalable AI infrastructure, enabling model fine-tuning to achieve near-human accuracy in drawing compliance checks.</p>
<h3 data-start="4353" data-end="4385"><strong data-start="4357" data-end="4383">AWS-Powered AI Scaling</strong></h3>
<p data-start="4387" data-end="4540">To support this model fine-tuning approach, TwinKnowledge collaborated with AWS PACE to design an MLOps pipeline with three key components:</p>
<p><strong data-start="4545" data-end="4576">1. Data ingestion and labeling</strong> – Automates document classification for AI training</p>
<p><strong data-start="4634" data-end="4655">2. Training pipeline</strong> – Fine-tunes AI models for high-precision document review</p>
<p><strong data-start="4719" data-end="4741">3. Inference workflow</strong> – Deploys trained models for real-time construction analysis</p>
<p data-start="4806" data-end="4950">By implementing this MLOps strategy, TwinKnowledge achieved automated model fine-tuning, enhancing AI-driven workflows across clients.</p>
<h3 data-start="4952" data-end="5012"><strong data-start="4955" data-end="5010">3. AWS-Powered Infrastructure for Model Fine-Tuning</strong></h3>
<h4 data-start="5014" data-end="5060"><strong data-start="5018" data-end="5058">Data Ingestion and Labeling with AWS</strong></h4>
<p data-start="5062" data-end="5286">For AI models to deliver accurate results, high-quality, well-organized data is crucial. TwinKnowledge built an AI-driven data pipeline using AWS services to automate document processing. The workflow includes:</p>
<p>1. Construction document upload via AWS API Gateway</p>
<p>2. Authentication with Amazon Cognito</p>
<p>3. Data storage and metadata processing using Amazon S3 and DynamoDB</p>
<p>4. Inference execution with AWS Lambda and Amazon SageMaker</p>
<p>5. Model monitoring using AWS CloudWatch and X-Ray</p>
<p data-start="5617" data-end="5736">By automating data ingestion, TwinKnowledge accelerated model fine-tuning while maintaining high AI accuracy.</p>
<h3 data-start="5738" data-end="5782"><strong data-start="5742" data-end="5780">Fine-Tuning Computer Vision Models</strong></h3>
<p data-start="5784" data-end="5850">AWS’s scalable MLOps infrastructure allows TwinKnowledge to:</p>
<ol data-start="5852" data-end="6067">
<li data-start="5852" data-end="5926">Label key drawing set elements (title blocks, details, schedules)</li>
<li data-start="5927" data-end="5997">Fine-tune CV models to adapt to project-specific drawing sets</li>
<li data-start="5998" data-end="6067">Deploy inference pipelines for real-time compliance checking</li>
</ol>
<p data-start="6069" data-end="6214">By leveraging AWS Inferentia2-powered SageMaker instances, TwinKnowledge reduced AI model inference costs while improving processing speed.</p>
<h3 data-start="6216" data-end="6235"><strong data-start="6219" data-end="6233">Conclusion</strong></h3>
<p data-start="6237" data-end="6528">Through this AWS collaboration, TwinKnowledge successfully scaled model fine-tuning for AI-driven document review in the AEC industry. The solution not only improved accuracy and efficiency but also laid the foundation for future AI advancements in construction compliance.</p>
<h4 data-start="6530" data-end="6553">Key Takeaways</h4>
<ul data-start="6555" data-end="6790">
<li data-start="6555" data-end="6636">Model fine-tuning enables scalable AI solutions for complex AEC workflows</li>
<li data-start="6637" data-end="6712">AI-augmented document reviews enhance, not replace, human expertise</li>
<li data-start="6713" data-end="6790">A flexible AI scaling infrastructure supports rapid industry adoption</li>
</ul>
<p data-start="6792" data-end="7075">As the AEC industry continues to digitize and embrace AI, TwinKnowledge’s AI platform is set to drive efficiency and innovation. By integrating MLOps best practices with Amazon SageMaker, companies can scale AI workflows seamlessly and enhance project accuracy.</p>
<p data-start="7077" data-end="7280" data-is-last-node="" data-is-only-node="">Cloudastra supports AI initiatives by providing relevant cloud services, ensuring organizations leverage cutting-edge AI scaling infrastructure to boost productivity and streamline processes.</p>
</div>
<p data-start="6296" data-end="6466" data-is-last-node="" data-is-only-node="">Do you like to read more educational content? Read our blogs at<a href="https://www.cloudastra.co/blogs"> Cloudastra Technologies</a> or contact us for business enquiry at <a href="https://www.cloudastra.co/contact">Cloudastra Contact Us</a>.</p>
<h3> </h3>


AI-driven document review workflow for AEC industry

Blog Details

Scaling AI in AEC Industry with TwinKnowledge and AWS PACE Collaboration

Scaling AI in AEC Industry with TwinKnowledge and AWS PACE Collaboration

Share This Article

Follow Us