Benefits:
- 95% reduction in image search time
- 98% decrease in manual tagging effort
- 97% accuracy in automated classification
AWS Services Used
- Amazon Rekognition
- Amazon SageMaker
- Amazon OpenSearch Service
- Amazon FSx for Windows
Architecture
General Overview
The Client is a leading engineering consulting firm. They offer solutions in transportation, environmental planning, geospatial services, bridges and structures, and infrastructure projects.
The Client partnered with dbSeer to deploy an intelligent image tagging and search platform using AWS AI/ML services. They faced challenges with manual image classification across thousands of project photos from multiple engineering disciplines. The Client needed an automated solution to improve asset discoverability and operational efficiency. dbSeer implemented a comprehensive solution using Amazon Rekognition, Amazon SageMaker, Amazon OpenSearch Service, and Amazon FSx for Windows. This created a scalable, AI-powered platform. It automatically analyzes, classifies, and indexes engineering imagery. Users can now conduct instant text-based searches across large image datasets.
The Opportunity: Inefficient Manual Processes Hindered Engineering Workflows
The Client managed an extensive library of engineering images across multiple disciplines. These included transportation infrastructure, bridge inspections, environmental assessments, geospatial surveys, and facilities documentation. Their manual classification system created significant operational bottlenecks. Project teams had to search through disorganized image libraries to locate specific visual assets across different specialties. The lack of consistent tagging standards meant similar images were categorized differently in transportation, environmental, and structural projects. This made it nearly impossible to leverage historical project data for current multi-disciplinary initiatives. This inefficiency affected project timelines. Engineers and consultants lost productivity to asset retrieval rather than focusing on core analysis and client deliverables.
The business impact extended beyond operational inefficiencies. Without proper image organization and search capabilities, the Client struggled to comply with regulatory documentation requirements. They also had difficulty maintaining quality control standards across multiple disciplines and extracting valuable insights from visual project data in transportation, environmental, and infrastructure domains. The growing volume of images from active geospatial, structural, and environmental projects threatened to overwhelm their systems. The lack of scalable search functionality prevented teams from accessing relevant reference materials that could improve decision-making and reduce risks. The Client realized their manual approach was unsustainable and sought an AI-powered solution to transform their image management.
The Solution: Automated AI Platform Delivers Intelligent Image Organization
dbSeer designed and implemented an AI-powered solution that automatically analyzes and classifies engineering images. This works across multiple disciplines upon upload. The architecture uses Amazon Rekognition for standard object and scene detection. It identifies engineering elements like infrastructure components, survey equipment, environmental conditions, and transportation assets. For domain-specific needs, the team developed custom machine learning models with Amazon SageMaker. These specialized algorithms recognize elements such as bridge components, roadway conditions, environmental features, geospatial landmarks, and structural details. This hybrid approach combines AWS’s pre-trained models with custom algorithms optimized for multi-disciplinary consulting work.
Amazon FSx for Windows is a centralized storage solution. It provides high-performance file access and maintains compatibility with Windows-based engineering workflows and CAD integrations. The system uses an event-driven architecture. It processes images and triggers AI analysis when new files are uploaded from field surveys, inspections, or project documentation. Generated tags and classifications are structured as metadata and indexed using Amazon OpenSearch Service. This enables fast text-based searches across the whole image repository, which covers transportation, environmental, structural, and geospatial projects. The search supports natural language queries. Users can search for specific project elements and receive relevant results. The serverless architecture allows automatic scaling for variable workloads across concurrent engineering projects. It also maintains cost efficiency through pay-per-use pricing models.
As an AWS Advanced Partner, dbSeer possesses critical expertise in engineering consulting workflows, which was crucial to the success of the solution.
The Outcome: Measurable Efficiency Gains Drive Business Value
The AI-powered image platform delivered immediate and measurable improvements for the Client’s multidisciplinary operations. Search efficiency increased by 95% allowing users to locate images much faster than before. The automated system achieved 97% accuracy and reduced manual tagging effort by 98%. Thus, valuable engineering resources were freed up for higher-value analysis and client consultation. The platform now handles the Client’s growing image volumes across their diverse engineering disciplines.
Beyond operational metrics, the platform created significant business value. Engineering workflows and decision-making improved across the Client’s service portfolio. Documentation and reporting processes became 95% faster. Project teams can quickly compile visual evidence for their projects. Enhanced asset discoverability lets engineers leverage historical data more effectively. The Client established a foundation for advanced analytics and future AI uses, making them fit for the digital age.
