McCalister and Grp

Professional Engineering Services

Company Type

Professional Engineering Services



The Project:

To streamline project delivery and enhance the accuracy and efficiency of engineering assessments for diverse infrastructure projects.

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How We Approached It:

We undertook a detailed examination of McCalister and Grp’s past project data, client feedback, and regulatory compliance requirements. By integrating insights from various stakeholders and leveraging advanced simulation models, we identified key performance drivers and potential bottlenecks. Our approach was to use a blend of traditional engineering expertise and modern data-driven methodologies to optimize project outcomes.

The Solution from Business Analytics, Data Analytics, and AI:

Business Analytics:

Utilized risk management analytics to proactively identify and address potential project risks, enhancing the predictability and success rate of engineering endeavors.

Developed a resource optimization model that allocates human and material resources based on project demands, historical performance, and predictive insights, ensuring maximum efficiency and cost-effectiveness.

Crafted a client satisfaction tracking system that collects and analyzes feedback throughout the project lifecycle, enabling continuous improvement and stronger client relationships.

Data Analytics:

Applied complex statistical models to analyze project timelines and identify patterns in delays and cost overruns. This analysis provided the foundation for developing more accurate project estimates and schedules.

Used structural health monitoring data to predict potential issues in infrastructure projects, allowing for preemptive interventions that save time and resources.

Analyzed subcontractor performance data to create a rating system that guides future collaborations and ensures the engagement of high-performing partners.


Developed an AI-assisted tool that provides real-time decision support to engineers, integrating data from various sources to offer predictive analytics and actionable insights.

Implemented machine learning models to automate routine analysis tasks, freeing up engineers to focus on more complex and creative problem-solving.

Introduced natural language processing (NLP) capabilities to automatically extract and summarize key information from project documentation, enhancing knowledge sharing and collaboration among team members.