Your AI Buyer’s Guide for Smarter Infrastructure Decisions
Artificial Intelligence is becoming more common in infrastructure tools—but not all AI is useful, and not every solution delivers real results. This guide was created to help organizations cut through the noise and evaluate AI solutions with confidence. Inside, you’ll find a framework to assess vendors, technology claims, and practical use cases that align with field needs.
What You’ll Learn in the AI Buyer’s Guide
Why AI is Being Added to Sewer Inspection Workflows Explore how AI-assisted tools reduce turnaround time, boost accuracy, and support proactive rehab planning—without replacing your team.
Key Questions to Ask AI Vendors Learn what to ask about model training, human oversight, QA/QC, turnaround speed, and data security to make a confident decision.
The Risks to Watch For Understand the red flags—like overcoding, video tampering, or misleading confidence scores—that can compromise accuracy and inflate rehab costs.
Must-Have AI Capabilities Get a breakdown of practical features that matter: NASSCO compliance, rehabilitation suggestions, prioritization tools, and hybrid automation workflows.
Evaluation Criteria That Matter Go beyond marketing claims with a criteria checklist that covers scope of output, access to reports, and transparency on model performance.
Implementation Insights
Get best practices for integrating AI into your current workflows—while keeping human experts in the loop and maintaining inspection integrity.
FAQs About AI-Assisted Pipe Inspection Defect Coding
AI-assisted sewer inspection uses computer vision and machine learning to automatically detect, classify, and code defects in CCTV pipe inspection video. Instead of inspectors manually reviewing hours of footage and applying NASSCO PACP, MACP, or LACP codes by hand, AI processes the video in a fraction of the time, flags potential defects, and routes the results to a certified coder for final review. The combination of AI speed and human oversight produces consistent, defensible, NASSCO-compliant condition data faster than manual coding alone.
AI defect coding works in a five-step workflow: the inspector uploads CCTV video to the AI platform, the model processes the footage by converting frames, clustering pixels, recognizing defects, and flagging areas of interest; a NASSCO-certified coder reviews and finalizes the coding; the system generates a report; and the results are integrated with GIS and asset management systems. This human-in-the-loop process pairs the speed and consistency of AI with the judgment of a certified expert, which is essential for producing audit-ready inspection records.
The benefits of using AI for sewer condition assessment include faster turnaround (hours instead of days), consistent application of NASSCO PACP, MACP, and LACP coding standards across inspections, proactive maintenance planning through risk scoring and rehabilitation recommendations, resource optimization that frees staff for higher-value QA, planning, and rehab work, and scalable insights that let municipalities and contractors assess significantly more footage per year. Utilities using AI-assisted coding have expanded annual network coverage from 10 to 15 percent up to 20 to 30 percent and reduced manual coding time from 16 hours per mile to roughly 4 hours per mile.
Human-in-the-loop AI for pipe inspection is a workflow in which the AI model processes the CCTV video and flags potential defects, and a qualified NASSCO-certified coder reviews, verifies, and finalizes every result before the report is delivered. This hybrid approach captures the efficiency of AI while preserving the accuracy and judgment of a certified inspector. Look for AI inspection vendors that perform QA/QC on 100 percent of inspections rather than a sample, since partial review can miss critical defects and inflate or deflate rehabilitation budgets.
The accuracy of AI defect coding depends on the size and quality of the training dataset, how often the model is retrained, whether it is tuned for different pipe materials, diameters, and video conditions, and whether outputs are verified by certified coders. ITpipes AiDetect, for example, has demonstrated accuracy in the 97 percent range with human-in-the-loop verification, compared to typical manual coding accuracy in the 60 to 70 percent range. When evaluating AI tools, ask for documented model performance KPIs rather than relying on vague "confidence scores."
When evaluating an AI sewer inspection vendor, ask questions in nine categories: turnaround time and inspection volume capacity, scope of output (defect coding only, or risk scoring and rehab recommendations as well), human oversight and QA/QC coverage, NASSCO PACP, MACP, and LACP coding standards compliance and supported versions, accessibility (proprietary viewer required or standard format), model training data volume and retraining frequency, model structure (tailored by pipe material and diameter or one-size-fits-all), video integrity (whether the original video is altered), and data security and hosting location. Asking these questions surfaces transparency issues before contract signing.
The main risks of AI sewer inspection tools include overcoding (which inflates rehabilitation budgets), undercoding (which misses critical defects), misleading confidence scores that lack context, vendor lack of transparency about QA/QC and model training, video tampering such as altering overlays, distances, or aspect ratios that compromises inspection integrity, and incomplete QA/QC where only a sample of inspections is reviewed. Mitigating these risks requires choosing a vendor with 100 percent human review, documented model performance metrics, transparent workflows, and a commitment to preserving the original video record.
Overcoding is when AI flags more defects than are actually present, often because the model confuses look-alike features such as cobwebs, light cracks, and roots, which inflates rehabilitation cost estimates and unnecessary repair scopes. Undercoding is when AI misses real defects, which leaves critical structural and operational issues unaddressed and exposes utilities to risk. The most effective way to prevent both is a human-in-the-loop workflow with NASSCO-certified review on 100 percent of inspections and a model trained on human-validated examples that distinguish between visually similar conditions.
AI sewer inspection results should be QA/QC verified by NASSCO-certified coders on 100 percent of inspections, not just a sample. The verification process should preserve the original video without altering overlays, distances, or aspect ratios, document changes made during review for audit purposes, and feed flagged inspections back into the model retraining loop for continuous improvement. Vendors should be willing to explain their full QA/QC workflow, share model performance KPIs, and demonstrate how look-alike defects are distinguished to prevent overcoding and undercoding.
AiDetect is the AI-assisted defect coding solution from ITpipes that automatically identifies and codes defects in CCTV sewer pipe inspections according to NASSCO PACP standards. AiDetect uses a human-in-the-loop workflow, in which the AI processes inspection video, flags defects, and a NASSCO-certified coder reviews and finalizes every result before delivery. AiDetect supports faster turnaround (hours instead of days), consistent NASSCO coding, scalable inspection volumes, and seamless integration with GIS platforms like Esri ArcGIS and asset management systems such as OpenGov EAM, Trimble Cityworks, and Lucity.
