TRITVA - AI Visual Inspection & Defect Detection Platform

An AI visual inspection platform is software that uses computer vision and machine learning to automatically analyse images and video from cameras, drones, or mobile devices to detect defects, anomalies, and non-conformances in products, components, or physical infrastructure. Unlike traditional rule-based machine vision systems, AI visual inspection platforms learn from labelled examples, adapt to new defect patterns, and continuously improve accuracy making them suitable for complex, varied, and high-throughput industrial inspection environments.

TRITVA is an AI-powered visual inspection platform developed by Ombrulla. It uses computer vision and machine learning to automatically detect defects in manufacturing production lines, inspect oil and gas infrastructure, analyse drone survey imagery for civil infrastructure condition, and manage quality inspection workflows for construction projects. TRITVA comprises three integrated modules Tritva Vision (AI model building), Tritva Watch (real-time inference), and Tritva Sky (analytics and learning) deployed on cloud, on-premises, or edge infrastructure.

AI defect detection systems typically achieve 95–99% accuracy on trained defect classes, compared to 70–85% for human visual inspectors who are subject to fatigue, distraction, and subjective judgement variation. AI systems also operate at consistent accuracy across all shifts and at line speeds that exceed human capability. Tritva's adaptive models improve continuously as new inspection data is collected, further increasing accuracy over time for site-specific defect types.

Traditional machine vision uses fixed, rule-based algorithms (edge detection, colour thresholds, template matching) that must be manually reprogrammed for each new product variant or defect type. AI visual inspection uses deep learning models trained on real defect examples, enabling them to generalise to new variants, detect subtle or complex defects that rules cannot capture, and self-improve over time. AI inspection is significantly more flexible, accurate, and scalable than traditional machine vision.

TRITVA inspects oil and gas assets pipelines, storage tanks, pressure vessels, flare stacks, and offshore structures using drone-mounted cameras, crawler robots, and fixed AI cameras deployed in hazardous or hard-to-access locations. AI computer vision models trained on O&G defect types detect corrosion, cracks, coating degradation, weld anomalies, and structural deformation with sub-millimetre sensitivity. Every finding is geotagged, classified by severity, and stored in an auditable inspection record supporting API 570, API 510, and PSSR compliance.

TRITVA's Tritva Sky module processes aerial inspection imagery and video captured by survey drones. After a drone flight, imagery is uploaded to Tritva Sky (or streamed in real time via edge processing). AI models analyse every image to detect structural defects, classify them by type and severity, and plot their locations on a georeferenced asset map. Automated inspection reports are generated in minutes replacing days of manual imagery review with defect findings linked to the relevant asset in the inspection history database.

Yes. TRITVA has pre-built connectors for SAP QM, SAP PM/EAM, Oracle Quality Management, Siemens Opcenter, IBM Maximo, and other major ERP, MES, and quality management platforms. When TRITVA detects a defect or inspection non-conformance, it can automatically create a quality notification, non-conformance report, or work order in the connected system with the AI detection image, defect classification, and confidence score attached as evidence. Full REST API and webhook support enables custom integrations with any enterprise platform.

Yes. TRITVA's edge deployment option runs the full AI inference engine locally on edge hardware industrial PCs, NVIDIA Jetson modules, or ruggedised appliances without any cloud connectivity requirement. This makes TRITVA suitable for offshore platforms, remote pipeline sites, underground facilities, and secure manufacturing environments with strict network isolation policies. When connectivity is restored, edge inspection data is synchronised with the central Tritva Sky analytics hub.

Organisations that replace manual inspection and legacy machine vision with AI typically achieve: 20–50% reduction in defect escape rate, 15–35% improvement in First-Pass Yield, 30–60% reduction in manual inspection labour costs, and 40–70% reduction in post-flight inspection report time for drone surveys. The elimination of customer warranty claims from escaped defects and regulatory penalties from inspection compliance failures can deliver additional ROI multiples. Most TRITVA deployments achieve positive ROI within 6–12 months.

TRITVA provides a tamper-evident digital audit trail for every inspection event recording the image captured, the AI model that analysed it, the defect classification and confidence score, the operator review decision, and any linked corrective action. This creates the documented, objective inspection evidence required for ISO 9001, ISO 17020, IATF 16949, and API inspection standard compliance. Tritva Sky's reporting module generates compliance-ready inspection reports directly from the stored data, eliminating manual report compilation.

A basic TRITVA deployment connecting cameras, onboarding users, and training an initial defect detection model typically takes 2–4 weeks for a single production line or inspection point, depending on data availability and infrastructure readiness. Pre-built foundation models and transfer learning reduce the number of labelled training images required. More complex multi-site or multi-product deployments are typically completed in 6–12 weeks. Ombrulla provides implementation support, model training services, and training for customer teams.