Safety monitoring was too reactive
BuildVision managed large infrastructure projects with manual safety inspections, delayed progress reporting, and reactive incident management.
2024·18 weeks·6 engineers
Construction Safety & Progress Intelligence
85% reduction in safety incidents, real-time progress tracking across 200+ sites
Client:BuildVision
85% reduction in safety incidents, real-time progress tracking across 200+ sites
Overview
BuildVision partnered with Boolean & Beyond to deliver aI-powered construction monitoring platform combining computer vision, IoT sensors, and BIM integration for safety compliance, progress tracking, and predictive risk management. The engagement focused on improving how the business operated day to day while creating a platform that could scale with demand.
The Problem
BuildVision managed large infrastructure projects with manual safety inspections, delayed progress reporting, and reactive incident management. Safety violations went undetected, and project delays cost millions. BuildVision needed a delivery partner who could translate that pressure into a product and systems implementation that improved speed, visibility, and reliability without adding more manual overhead.
Challenges
What made this hard
Critical issues surfaced too late
Safety violations went undetected, and project delays cost millions.
The business needed a more scalable foundation
BuildVision needed an implementation path that solved the immediate bottlenecks while building a more scalable operating model for construction & infrastructure.
How we delivered
Our approach
Phase 01
Discovery & workflow mapping
We began by mapping the current workflow end to end, identifying the steps creating the most friction for BuildVision, and prioritizing the journeys where better UX, cleaner data flow, or deeper automation would create the fastest business lift.
Phase 02
Architecture & experience design
From there, we translated the business goals into a delivery plan and architecture centered on AI-powered construction monitoring platform combining computer vision, IoT sensors, and BIM integration for safety compliance, progress tracking, and predictive risk management, so the build could improve adoption, operator control, and room for scale at the same time.
Phase 03
Build, integrations & intelligence
The build was structured across the experience layer (React), the intelligence layer (PyTorch and Computer Vision), and production integrations and ops (BIM APIs, Azure, and IoT). We deployed an AI system that analyzes CCTV feeds for PPE compliance, hazard detection, and unsafe behaviors in real-time. BIM integration tracks as-built vs planned progress using drone imagery. Predictive models forecast delays and safety risks before they occur.
Phase 04
Launch hardening & optimization
Before and after launch, we tuned the workflows, operational guardrails, and instrumentation against live usage so the platform could reliably support 85% reduction in safety incidents, real-time progress tracking across 200+ sites.
What we built
Solution highlights
01
Core product experience
AI-powered construction monitoring platform combining computer vision, IoT sensors, and BIM integration for safety compliance, progress tracking, and predictive risk management.
02
Real-time operations and data flow
We deployed an AI system that analyzes CCTV feeds for PPE compliance, hazard detection, and unsafe behaviors in real-time.
03
Vision-driven monitoring
BIM integration tracks as-built vs planned progress using drone imagery.
04
Decision intelligence in the workflow
Predictive models forecast delays and safety risks before they occur.
The full story
Journey & Execution
Construction Safety & Progress Intelligence
BuildVision was operating in an environment where safety, schedule control, and site visibility all had direct commercial consequences. A late report or a missed compliance signal could push entire project milestones off track.
The business needed a system that could interpret what was happening on site continuously, not only when a manager or inspector happened to visit.
Business Context
Manual inspections and delayed reports meant site leaders were often reacting after hazards had already emerged. The same lag made progress tracking unreliable, especially across large numbers of active locations.
The team also needed a way to connect field reality with the planned project baseline, so progress conversations could move from opinion to evidence.
BuildVision managed large infrastructure projects with manual safety inspections, delayed progress reporting, and reactive incident management. Safety violations went undetected, and project delays cost millions.
How We Approached the Build
Discovery and workflow mapping
We mapped the flow of CCTV footage, drone imagery, BIM milestones, and site reporting to identify the moments where automated detection and faster escalation would have the highest operational value.
Architecture and product design
The implementation combined a vision pipeline for site feeds, integrations for project and BIM data, and site-level dashboards that could turn raw detections into clear action for safety and delivery teams.
Delivery and integration
Rather than treating safety monitoring and project intelligence as separate tools, we designed one operational layer that could surface both immediate incidents and emerging delivery risks.
What We Implemented
The shipped product centered on the operating workflows project teams actually needed:
- Real-time PPE and hazard detection across site footage so supervisors could intervene faster.
- Progress monitoring that compared observed site conditions with planned construction milestones.
- Early risk views that surfaced safety and delivery patterns before they became formal incidents or delays.
- Alerting and escalation paths that pushed the right signals to the right operating teams without burying them in noise.
AI and automation layer
Computer vision was used to identify safety signals and progress deviations at a pace manual review could not match. The important design choice was not the model alone, but the thresholding, escalation, and human review logic around it.
That let BuildVision use automation for speed while keeping site leadership in control of the operational response.
Stack and engineering decisions
The implementation used PyTorch, Computer Vision, BIM APIs, React, Azure, and IoT across the experience, service, data, intelligence, and integration layers.
- Experience & channels: React
- AI/ML & automation: PyTorch and Computer Vision
- Cloud, integrations & ops: BIM APIs, Azure, and IoT
Rollout and measurable outcomes
The rollout emphasized calibration by site conditions, camera placement, and workflow rules so the system could become dependable in real construction environments rather than only in ideal test footage.
- Safety Incidents: -85%
- Project Visibility: 200+ sites
- Early Risk Detection: 14 days
- Inspection Time: -75%
Why This Delivered Business Value
The platform improved both safety response and project visibility because site intelligence moved from scattered reports to a continuous operating signal.
Taken together, the engagement delivered 85% reduction in safety incidents, real-time progress tracking across 200+ sites by aligning product experience, workflow design, and implementation detail around the same business objective.
Under the hood
Technical Deep Dive
We treated the implementation as a product system rather than a single feature build, with React on the delivery surface and a resilient service and data foundation behind it. We deployed an AI system that analyzes CCTV feeds for PPE compliance, hazard detection, and unsafe behaviors in real-time. BIM integration tracks as-built vs planned progress using drone imagery. Predictive models forecast delays and safety risks before they occur. Intelligence was embedded through PyTorch and Computer Vision, giving BuildVision automation and decision support inside the workflow instead of forcing teams into separate tools. Deployment, integrations, and production operations were supported by BIM APIs, Azure, and IoT, which helped the team roll out reliably and keep improving after launch.
Intelligence layer
AI Capabilities
Computer vision and image analysis
Visual intelligence was used to detect patterns, hazards, or clinical signals faster than manual review alone and to surface the findings inside the operational workflow.
Risk scoring and decision support
The system combined structured business rules with model-driven scoring so teams could move faster on low-risk cases and focus human attention where judgment still mattered most.
Predictive operational intelligence
Forecasting and prediction models turned historical and live signals into recommendations teams could use for planning, pricing, staffing, or resource allocation.
Tech Stack
Technologies used
Experience & channels
React
AI/ML & automation
PyTorchComputer Vision
Cloud, integrations & ops
BIM APIsAzureIoT
Impact at a glance
Results that speak for themselves
Every metric shown is measured in production — not projected, not estimated. These are the real numbers from the deployed system.
Measurable outcomes
-85%
Safety Incidents
Reduction in recordable safety incidents through real-time CCTV analysis for PPE compliance and hazard detection
200+ sites
Project Visibility
Real-time progress tracking across all active construction sites using drone imagery and BIM comparison
14 days
Early Risk Detection
Predictive models flag potential delays and safety risks 14 days before they would typically be noticed by site managers
-75%
Inspection Time
Time spent on manual safety inspections reduced by 75% as AI handles continuous monitoring with exception-based alerts
“
"Safety incidents dropped 85% in the first quarter after deployment. The AI catches PPE violations and hazard conditions in real-time across all our sites — something manual inspections could never achieve at this scale."
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