AI-Driven Intelligent Worker Allocation for Field Services

The Challenge

A growing Australian service support company managing hundreds of field workers across multiple client sites was facing a persistent and costly operational bottleneck: matching the right worker to the right job. Allocation decisions were made manually by coordinators relying on memory, spreadsheets, and phone calls. This led to frequent skill mismatches, underutilised specialist workers, excessive travel times, and mounting client dissatisfaction when jobs required rework because the assigned worker lacked the required capabilities or site-specific experience. As the business scaled, the manual approach was becoming unsustainable — coordinator workload was growing linearly with job volume, and allocation quality was declining.

Our Approach

Corlence developed an AI-powered worker selection and allocation engine that transformed this manual process into a data-driven, intelligent system.

We began by building a comprehensive worker profiling system that ingested data from past job completions, trade certifications, client feedback scores, and integration history with specific building systems and equipment types. Unlike static skills databases, each worker's profile evolved dynamically based on their real-world performance — a worker who consistently delivered excellent results on a particular equipment type would see their matching score for similar jobs increase over time.

The core intelligent matching algorithm scored and ranked available workers for each incoming job based on multiple weighted factors: relevant trade skills and certifications, demonstrated experience with similar integrations and equipment, geographic proximity to the job site, real-time availability, historical client satisfaction ratings, and certification currency. The algorithm balanced these factors to optimise for both job quality and operational efficiency.

Rather than replacing human judgment entirely, the system was designed as a coordinator decision-support tool. It presented dispatchers with a ranked shortlist of the top recommended workers along with confidence scores and clear reasoning for each recommendation — enabling faster, better-informed allocation decisions while keeping experienced coordinators in the loop for edge cases.

A continuous learning feedback loop incorporated job completion reports, client satisfaction ratings, and coordinator overrides to continuously refine matching accuracy. The system got measurably smarter with every completed job, adapting to seasonal workforce changes, new certifications, and evolving client requirements.

Technology Stack

Python, Scikit-learn, FastAPI, PostgreSQL, React, Docker

Business Value Delivered

• First-time fix rates improved significantly, reducing costly return visits and improving client retention
• Average worker travel time decreased through proximity-aware matching, lowering fuel costs and increasing daily job capacity
• Coordinator allocation time per job reduced from approximately 15 minutes to under 2 minutes, freeing dispatchers to focus on exception handling and client relationships
• Client satisfaction scores improved measurably as the right expertise was consistently matched to each job requirement