VAPAR

Learning Objectives

• Describe what VAPAR does and why automated pipe inspection matters for utilities
• Explain how deep learning is applied to CCTV footage of sewer and stormwater pipes
• Identify who uses VAPAR and how expert-in-the-loop review fits the workflow

What Is VAPAR?

VAPAR is a software platform that uses deep learning to review the video footage utilities capture when inspecting underground sewer and stormwater pipes. Founded in 2018 and based in Sydney, VAPAR tackles a slow, labor-intensive bottleneck: traditionally, a trained operator watches hours of closed-circuit television (CCTV) footage frame by frame to spot and record every crack, root intrusion, or point of infiltration. VAPAR automates that first pass, detecting and coding defects and generating repair recommendations far faster than manual review.

The platform is designed to keep human expertise in the loop rather than remove it. VAPAR's AI does the tedious detection and classification work, and engineers or asset teams review and confirm the results. It integrates with utility asset-management systems, including Autodesk Info360, so findings flow directly into the tools utilities already use to plan maintenance and capital work.

What VAPAR Does

• Automated defect detection — analyzes CCTV footage to find cracks, root intrusion, infiltration, and other defects
• Standardized coding — assigns defect codes automatically so results are consistent across inspections and operators
• Repair recommendations — generates suggested remediation actions from the coded conditions
• Faster turnaround — clears inspection review dramatically faster than watching footage manually
• Asset-system integration — connects with utility asset-management platforms such as Autodesk Info360

How AI Is Applied

VAPAR's core is genuine computer vision. The deep-learning models are trained to recognize the visual signatures of pipe defects in inspection video — the shapes of fractures, the tangle of intruding roots, the telltale flow of water infiltrating through a joint. As footage plays, the models detect these features, locate them within the pipe run, and classify each into a standardized defect category with a severity indication.

This is real machine learning applied to a visual problem, not a rules-based script. Because a human expert reviews and validates the AI's output, the workflow combines the speed and consistency of automated analysis with the judgment of an experienced engineer. The result is a faster, more consistent inspection process that turns raw video into structured, decision-ready condition data.

Who Uses VAPAR

VAPAR is used by water and wastewater utilities, municipal councils, and the inspection contractors that survey pipe networks on their behalf. Its users are asset managers, network and maintenance engineers, and inspection teams who need to process large volumes of CCTV footage and turn it into prioritized, well-documented repair plans.

Pricing

VAPAR is enterprise software with quote-based pricing. Costs depend on the volume of inspection footage processed, the number of users, and the integrations required. Utilities and contractors contact VAPAR directly for a tailored quote.

Company Details

Strengths

• Genuine computer vision — deep-learning models trained specifically to recognize pipe defects in video
• Major time savings — automates the slow, manual review of hours of CCTV footage
• Consistent results — standardized defect coding reduces operator-to-operator variability
• Expert-in-the-loop — engineers validate AI findings, blending speed with professional judgment
• Fits existing tools — integrates with asset-management systems utilities already run

Limitations and Considerations

• Depends on footage quality — poor lighting, debris, or low-quality video can limit detection accuracy
• Human review still required — the AI accelerates but does not fully replace expert confirmation
• Standards alignment — coding must match the defect-classification standard the utility uses
• Enterprise scope — quote-based and aimed at utilities and inspection contractors, not individual users

Key Takeaways

• VAPAR uses deep learning to detect and code defects in sewer and stormwater CCTV footage and generate repair recommendations
• Its computer vision automates a slow manual review process while keeping an expert-in-the-loop for validation
• Standardized coding and integration with tools like Autodesk Info360 turn raw video into decision-ready asset data
• Best for water utilities, councils, and inspection contractors that need to process large volumes of pipe footage consistently and quickly