List of Chooch AI for Facility Safety Monitoring Customers
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Since 2010, our global team of researchers has been studying Chooch AI for Facility Safety Monitoring customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Chooch AI for Facility Safety Monitoring for Computer Vision from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Chooch AI for Facility Safety Monitoring for Computer Vision include: Hyundai India, a India based Automotive organisation with 5672 employees and revenues of $17.10 billion, Galliford Try, a United Kingdom based Construction and Real Estate organisation with 4198 employees and revenues of $2.17 billion, Comprehensive Healthcare At Home Canada, a Canada based Healthcare organisation with 25 employees and revenues of $2.0 million and many others.
Contact us if you need a completed and verified list of companies using Chooch AI for Facility Safety Monitoring, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight | Insight Source |
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Comprehensive Healthcare At Home Canada | Healthcare | 25 | $2M | Canada | Chooch AI | Chooch AI for Facility Safety Monitoring | Computer Vision | 2024 | n/a | In 2024, Comprehensive Healthcare At Home Canada implemented Chooch AI for Facility Safety Monitoring to introduce camera based at home patient monitoring. The deployment uses Computer Vision to continuously analyze residential camera streams in Ontario for falls, walker usage, distress signals and other observable safety risks, enabling remote observation to support aging in place. Chooch AI for Facility Safety Monitoring was configured with discrete event detection modules for fall detection, assistive device usage recognition and distress signal classification, paired with event scoring and automated alert generation for CHAH clinical staff. Implementation work emphasized camera placement standards, sensitivity tuning and notification rules to manage signal fidelity and reduce false positives, while vendor provided analytics deliver ongoing model updates and classification improvements. Rollout focused on facility and home safety operations across Ontario and was embedded into CHAH remote monitoring and care coordination workflows so alerts route to care coordinators and predefined response protocols. The program targets patient safety, care coordination and emergency response functions with measurable aims to lower hospital admissions and reduce ER visits, and CHAH operates the monitoring service with Chooch AI technical support for maintenance and model management. | |
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Galliford Try | Construction and Real Estate | 4198 | $2.2B | United Kingdom | Chooch AI | Chooch AI for Facility Safety Monitoring | Computer Vision | 2024 | n/a | In 2024, Galliford Try implemented Chooch AI for Facility Safety Monitoring. The Chooch AI for Facility Safety Monitoring application is a Computer Vision deployment delivered in partnership with Chooch and Galliford Try Asset Intelligence to provide real-time facility and site safety monitoring across construction and infrastructure projects in the United Kingdom. The implementation centers on three functional modules described by the partners, facility safety, fire detection, and perimeter monitoring, each instrumented to analyze continuous video streams and surface hazardous events. Chooch AI for Facility Safety Monitoring performs automated detection and classification of fire and perimeter-breach scenarios, and it generates real-time alerts that feed into site security and incident response workflows. The configuration emphasizes configurable detection models and event-level alerting to support operational use by site teams. Deployment is focused on UK operations and is managed through Galliford Try Asset Intelligence, covering construction and infrastructure project sites where asset protection and early hazard detection are priorities. The rollout aligns system outputs with existing site security and incident response procedures to speed incident response and enhance site security, and partners reported improved asset protection and earlier fire and hazard detection as primary outcomes. | |
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Hyundai India | Automotive | 5672 | $17.1B | India | Chooch AI | Chooch AI for Facility Safety Monitoring | Computer Vision | 2023 | n/a | In 2023, Hyundai India implemented Chooch AI for Facility Safety Monitoring to augment manufacturing floor visual inspection in its engine production operations. The deployment applied Computer Vision to automate visual quality checks and to provide continuous monitoring across production lines, targeting manufacturing and quality control business functions at Hyundai India facilities. Chooch AI for Facility Safety Monitoring was configured to perform automated defect detection and accelerated visual inspections, using trained vision models and inference pipelines to flag anomalies and reduce manual inspection cycles. Functional capabilities implemented included image capture and classification, rule based inspection thresholds, and alerting workflows to surface potential defects to quality technicians. The operational scope focused on manufacturing floor monitoring in India, with the system instrumenting production line workstations and feeding visual findings into frontline quality processes. The implementation was positioned as a facility level visual inspection layer rather than an ERP module, and it was used directly by production and quality assurance teams to inform shift level inspection decisions. Governance for the program centered on configuring inspection rules, model validation checkpoints, and operational handoffs to QA teams to ensure consistent defect triage. Reported outcomes included reduced defect escape and accelerated visual quality checks on production lines, reflecting improvements in inspection throughput and QA accuracy as described in the vendor case study. |
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