Generative AI in Construction /

A mid‑size construction firm operating across remote and urban sites in Alberta and Western Canada sought a way to gain real‑time visibility into equipment and personnel locations, reduce costly idle time, and enable faster, data‑driven dispatch decisions.

Company Size:

[ 150+ Employees ]

Location:

[ Alberta - Canada ]

Business Challenge /

The company struggled with fragmented, manual tracking of heavy equipment and crews. Radio logs and paper timesheets provided only delayed, error‑prone snapshots of resource utilization. As a result, excavators and loaders frequently sat idle for hours, crews were mis‑allocated, and project timelines slipped. The lack of a privacy‑compliant, automated location awareness system made proactive dispatch impossible and placed additional workload on field crews.

Realizing The Need for Change /

Mounting costs from idle machinery and delayed deployments pushed the firm to seek a solution that could deliver live location data without increasing field‑staff administrative burden. Leadership recognized that only a privacy‑first, deterministic AI platform could provide reliable, actionable insights while respecting on‑site crew data rights.

What We Did /

We designed and piloted a modular, privacy-centric platform that integrates anonymized mobile check-ins, project schedules, and telematics feeds to generate real-time location intelligence and AI-assisted dispatch recommendations.

SiteAware™ Location Engine /

A real‑time location awareness engine that aggregates anonymous crew check‑ins and project schedule data, producing a live map of equipment and personnel across all sites. The system automatically aligns timestamps, filters noise, and applies deterministic context tags (e.g., “excavator_42 at Site 7”) without storing personal identifiers.

Dispatch AI Agent /

An AI‑driven dispatch assistant that analyzes location, schedule, and utilization data to recommend optimal crew assignments and flag idle equipment. All recommendations are presented to supervisors for mandatory one‑tap confirmation before any action is taken, ensuring human oversight remains the final decision maker.

Dandy™ Context Engine /

A dynamic contextual‑tagging layer that enriches raw location and telematics data with rule‑based context (e.g., “idle loader,” “crew en‑route”). The engine supplies the Dispatch AI Agent with structured, high‑confidence inputs, and logs every tag for auditability, enabling rapid troubleshooting and model refinement.

Requirements /

The specific technology solutions that were selected and implemented to address the challenges they faced

AI Agents

AI Workflows

Private Compute

Data Integration

Reporting Dashboard

Mobile Check‑In

Our Value /

The technological advantages the client gained from our assistive and scalable interventions.

Reduced Idle Time

Equipment idle time fell by 25% across the three pilot sites over a three‑month measurement window, directly translating into lower operating costs and improved project schedules.

Faster Dispatch Decisions

Time‑to‑deployment for crews improved by 30%, as measured through dispatch‑log timestamps, allowing supervisors to respond to changing site conditions in near‑real time.

Scalable Visibility

The platform's private‑compute architecture and deterministic context rules provide a replicable foundation that can expand to 15+ active project sites while maintaining privacy compliance and audit trails.

The Finished Product/

The pilot delivered a working prototype that integrates anonymous mobile check‑ins, project schedules, and limited live telematics into a unified location‑awareness dashboard. AI‑generated dispatch recommendations are routed through a human‑approval workflow, eliminating blind automation while preserving speed. All contextual tags and model inputs are logged in Django Spire™, enabling full auditability and continuous refinement as the firm scales the solution across Western Canada.

Technologies Used /

The technologies used in this project

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