PipeAid transforms raw CCTV sewer inspection footage into precise, ArcGIS-ready digital twins coded to NASSCO standards. The platform combines AI-powered first-pass analysis with human QA/QC review by NASSCO-certified technicians, ensuring consistent, accurate system records regardless of camera type or video age. This eliminates manual review bottlenecks and creates a durable data asset that survives personnel turnover.
Funding
Funding not disclosed
Founders
Product
Problem
Municipalities and utility contractors rely on manual review of CCTV sewer inspection footage, which is slow, labor-intensive, and prone to inconsistent coding. This creates bottlenecks in pipeline assessment, leads to inaccurate system records, and makes data unreliable when personnel change.
Solution
PipeAid provides a platform that converts CCTV inspection footage into precise, ArcGIS-ready digital twins of sewer systems. Users upload video from any contractor, camera, or software format, and PipeAid's AI performs the initial defect coding pass. NASSCO-certified technicians then review and verify every finding, ensuring accuracy and consistency. The final output is a clean, standardized dataset delivered directly into ArcGIS, which the customer fully owns. This approach eliminates the need for new hardware or software and accelerates the entire inspection-to-mapping workflow.
Target Audience
Primary customers are municipal sewer departments, utility companies, and pipeline inspection contractors who need to process large volumes of CCTV footage and maintain accurate GIS-based system records.
Features
- AI-powered first-pass defect coding that accelerates the review process
- Human QA/QC by NASSCO-certified technicians to ensure accuracy and consistency
- Accepts video from any contractor, camera, or software format, including older footage
- Outputs directly into ArcGIS as a clean, standardized digital twin
- Consistent coding methodology that prevents data degradation from personnel turnover
- No new hardware or software required for implementation