Initial Challenge
A workforce management platform serving the construction and field services industry needed to enhance how it forecasted labor demand across active and future job sites. The platform’s users—project managers, field supervisors, and operations leaders—were relying on static data and manual estimations, resulting in labor mismatches, inefficient hiring cycles, and delayed project starts.
The company sought a solution that could deliver:
- Accurate, daily workforce forecasts by role, trade, and location.
- Predictive insights directly within the platform’s user interface.
- Explainable AI outputs to increase trust and adoption.
- Scalable performance across multiple concurrent projects.
Solution Delivered by DinoCloud Using AWS
To meet these needs, the company partnered with DinoCloud, an AWS Premier Services Partner, to design and deploy a production-grade workforce forecasting engine powered by Amazon Web Services (AWS).
Leveraging Amazon Bedrock, SageMaker, and other AWS-native services, DinoCloud built and integrated an AI solution that transformed the platform’s approach to staffing. The system generates daily headcount forecasts, automates alerts for workforce shortages, and enables proactive hiring strategies—delivered securely through the platform’s interface.
Results & Impact
The implementation was completed successfully, with all project success criteria met:
- ✅ Forecast Accuracy: Delivered labor forecasts with a Mean Absolute Percentage Error (MAPE) under 15%.
- ✅ Trade-Specific Precision: Achieved over 85% accuracy in matching predicted workforce needs to actual trade requirements.
- ✅ Embedded Explainability: Integrated SHAP-based explainability features that helped users understand the drivers behind predictions.
- ✅ Performance & Uptime: Maintained 99.9% service uptime and sub-5-second average response time for predictions.
- ✅ Export & Alerts: Enabled CSV/PDF forecast exports and automated hiring alerts via Amazon SNS.
- ✅ Automated Retraining: Implemented quarterly model retraining workflows using Amazon Step Functions.
Architecture Highlights
The solution leveraged the full power of AWS:
- Amazon Bedrock: Used as the core inference engine for real-time, generative AI-powered predictions.
- Amazon SageMaker: Supported fallback modeling and model versioning for regulated use cases.
- AWS Lambda & API Gateway: Handled secure, event-driven forecast requests and logic.
- Amazon S3 & DynamoDB: Managed training data, forecast results, and metadata storage.
- Amazon Cognito: Secured user authentication and access control.
- Amazon CloudWatch & X-Ray: Enabled full observability, SLA tracking, and auditability.
- Amazon SNS/SES: Powered alert notifications for workforce gaps.
Conclusion
With DinoCloud’s guidance and AWS’s generative AI capabilities, the platform successfully evolved from a workforce tracking tool into a predictive labor planning engine. This not only enhanced operational efficiency but also gave field teams a strategic advantage by enabling proactive staffing decisions across diverse construction environments.
The project demonstrates how GenAI, when applied with the right architectural rigor, can solve highly specific business problems—delivering measurable impact in real-world, high-variability industries like construction.
