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AI & Agentic AI Development

Insights Table

Client
Insights Table
Project Nature
AI-Assisted Workforce Scheduling System
Services
AI Software Development
Overview

Project Overview

Insights Table centralises surveyor availability, work schedules and survey-session requirements within one operational platform. Its AI-assisted scheduling engine recommends practical workforce allocations based on availability, shift rules, locations, mentorship requirements and operational priorities, while keeping planners in full control of the final schedule.
Challenges

Challenges We Solved

Scheduling a large field workforce manually requires planners to consider multiple operational constraints at the same time. Availability, shift patterns, terminal movements, survey priorities and mentorship requirements can make roster preparation time-consuming and prone to inconsistencies.
  • Complex Scheduling Rules

    Converted multiple operational requirements and workforce constraints into a structured scheduling workflow.

  • Manual Planning Effort

    Reduced the time required to assign surveyors across shifts, terminals and survey sessions.

  • Limited Schedule Visibility

    Centralised availability, assignments and scheduling conflicts for easier review and coordination.

How It Works

Our Approach

OTG Lab developed a centralised scheduling platform that consolidates workforce data and uses AI to recommend compliant survey assignments. Planners can review, adjust and approve the proposed schedule before implementation.

Project Highlights

  • Human-Controlled AI: AI supports schedule preparation while planners retain full authority over final assignments.
  • Rule-Aware Scheduling: Recommendations are generated according to real operational constraints rather than simple availability matching.
  • Centralised Workforce Planning: Combines surveyor data, availability and session requirements within one platform.
  • Operational Flexibility: Planners can manually revise schedules when practical circumstances require exceptions.
  • Improved Resource Allocation: Helps distribute survey sessions more evenly across available team members.
  • Conflict Visibility: Makes unassigned sessions, duplicate bookings and scheduling gaps easier to identify.
Business and User Impact

Impact Area

  • Reduced Planning Time: Accelerates the preparation of complex surveyor schedules through AI-assisted recommendations.
  • Fewer Scheduling Errors: Validates assignments against availability and operational rules before approval.
  • Improved Workforce Utilisation: Helps planners allocate available surveyors more effectively across shifts and locations.
  • Better Operational Coverage: Highlights uncovered sessions and available personnel before the roster is finalised.
  • Greater Planning Consistency: Applies the same scheduling rules across different dates, teams and operational scenarios.
  • Stronger Decision Support: Gives planners a structured recommendation that can be reviewed instead of building every roster manually.
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