ConeLabs
ConeLabs is an AI inspection platform that turns drone and camera imagery into 3D models and automatically detects building and infrastructure defects.
Best for: Property owners, managers, and engineering firms automating building envelope and structural condition inspections
Last reviewed 6/2/26
- Integrations
- Data-source agnostic; accepts imagery from smartphone, camera, or drone
- Property types
- Commercial buildings, Infrastructure, Bridges and transport assets, Utilities
- Geography served
- Canada, United States
How ConeLabs uses AI[1][3][5]
ConeLabs converts field imagery from smartphones, cameras, or drones into inspection-grade 3D photogrammetric models, then runs AI defect detection to find and categorize issues like cracks, water infiltration, poor insulation, and weather damage. It quantifies the location and severity of each defect and generates structured inspection reports in one workflow.
- • Converts uploaded field imagery into high-resolution 3D photogrammetric models, regardless of capture device
- • AI semantic segmentation detects and categorizes building envelope and structural defects with location and severity quantification
- • Generates structured, engineering-grade inspection reports from a single field-to-report workflow
AI type: Computer vision and 3D photogrammetry defect detection
Key numbers[3][6]
- • Raised $1.5M pre-seed to modernize infrastructure inspection
- • Won first place and the $25,000 best pitch and Audience Choice awards at Communitech Fast Track Cities (Nov 2024)
- • Secured $100,000 in pilot funding for bridge and roadway inspections via Pitch Kitchener
- • Grand prize winner at the AWS AI pitch competition
Credibility[2][3][7]
- Founders
- Albert Mansour (CEO, P.Eng., MBA, PMP) and Ahmed Mahmoud (CTO, PhD, AI and sensor fusion)
- Customers
- Acuren, Kajima, RDH (named partners using or piloting the product)
- Investors
- Spatial Capital (lead), Techstars, Ontario Centre of Innovation, The Ricketts Family Trust, Team Ignite Ventures, Navis Capital, Exitfund
Founded
2023
Headquarters
Waterloo, Ontario, Canada
Stage
Pre-seed
Employees
1-10
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