AI Visual Inspection for Defect Detection
Detect surface, assembly, and packaging defects in real time using computer vision, deploy on edge/on-prem/cloud and integrate with MES/QMS/ERP workflows.
Measurable Impacts
30–70%
Fewer defect escapes
Dramatically reduce the number of defects reaching customers by catching issues early.
10–30%
Lower scrap and rework
Minimize material waste and labor costs with real-time process visibility.
<15 ms
Defect detection latency
Experience near-instant detection for high-speed manufacturing environments.
6–12 mo
Typical ROI target
Achieve rapid payback and measurable business impact within the first year.
Why AI Visual Inspection Matters
Ombrulla's AI visual inspection system helps business bring accuracy, speed and consistency in every stage of your quality process. Instead of depending on manual checks where small defects are missed, our system uses advanced computer vision to find issue early and make production runs effectively.
We partner with our clients to design and implement inspection workflows which deliver measurable results and support continuous improvement.
Key Benefits
Unlocking next-level quality control through our AI Visual Inspection system a smarter way to detect defects with high accuracy and speed.
Stop defects before they ship
Fewer escapes with consistent AI detection that catches issues early and protects brand, warranty, and customer trust.
Cut scrap and rework at the source
Detect defects upstream to prevent error propagation and reduce waste, rework loops, and costly downstream fixes.
Inspect 100% at line speed
Move from sampling to full coverage without slowing production real-time edge decisions remove inspection bottlenecks.
Prove quality with traceable insights
Get audit-ready image evidence plus analytics by line, shift, SKU, lot, and supplier to drive root-cause and continuous improvement.
Scalable, customisable multi-site visual inspection platform
Tritva is a configurable inspection platform that scales across plants, lines, and locations, standardising execution while allowing site-level flexibility.
Learn more
Real-Time Edge + Unified Cloud
Run inference on the shop floor for instant decisions, while keeping analytics and reporting centralised across sites.
One Platform for Any Inspection Device
Integrate drones, rovers, mobiles, cobots, and fixed cameras into a single configurable system no silos.
Single Dashboard for Every Site
Monitor all plants, lines, and inspection points with consistent KPIs, alerts, and benchmarks.
Custom AI Models, High Accuracy at Scale
Build and deploy tailored AI/ML models per use case (and site) with continuous improvement through retraining and versioning.
How Tritva works
Vision - Build smarter models
We clean and standardise visual data, label defects, and train models that match your tolerances and operating conditions.
Watch - Inspect in real time
Images/videos are processed instantly to detect, classify, and localise defects. You get clear alerts with evidence.
Sky - Control from the cloud
All inspection data is centralised for dashboards, reporting, continuous improvement, and multi-site rollout.
AI Visual Inspection Use Cases Across Industries
Ombrulla's AI visual inspection platform is proven across major industrial sectors. Every deployment is configured to the specific defect taxonomy, camera environment, line speed, and compliance requirements of the customer.
Automotive (paint, body, assembly)
Detect: scratches, dents, paint defects, missing parts, clip/fastener issues, and weld seam anomalies.
Read usecase
Steel & Metals (surface)
Surface Defect Detection: Identify cracks, laps, seams, pits, and corrosion on hot-rolled and cold-rolled steel coil, sheet, and structural sections.
Read usecase
Textile & Apparel (fabric Inspection)
Fabric Roll Inspection: Detect weaving defects, holes, snags, colour streaks, and contamination across fabric rolls at production speed
Manufacturing (Production Line)
Assembly Line Inspection: Verify part presence, orientation, and alignment at every station using real-time AI classification
Technical Capabilities
Ombrulla builds enterprise-grade AI inspection systems engineered for reliability, security, and scale. Our full-stack technical capability spans the entire AI lifecycle, from raw data ingestion to production model serving and ongoing MLOps management.
Data Engineering & Image Preprocessing
Raw visual data is cleaned, labelled, and augmented. Security controls and compliance audits are built into the pipeline from day one.
Custom Model Training & Continuous Adaptation
AI models are trained on your specific defect types and retrained regularly to maintain accuracy as product variants and defect patterns evolve.
Retrieval-Augmented Inspection Agents
AI agents retrieve contextual defect history and apply the right detection tool automatically enabling faster decisions and smoother workflows.
Distributed Compute & Scalability
Inspection runs scale horizontally across multiple lines, plants, and geographies without degrading performance or inflating infrastructure costs.
Inference Optimisation & Model Serving
Optimised inference engines (TensorRT, ONNX, OpenVINO) ensure sub-millisecond detection latency, supporting the most demanding production environments.
MLOps & Model Lifecycle Management
A unified ML platform handles versioning, A/B testing, rollback, and monitoring keeping every inspection model production-ready at all times.
Evaluation, Safety & Reliability Engineering
Continuous red-teaming, adversarial testing, and reliability benchmarking ensure inspection models perform consistently in real production conditions.
Security & Privacy-Preserving ML
IP protection, data encryption at rest and in transit, role-based access control, and on-premises deployment options for air-gapped environments.
Supported Deployment Environments
Edge Deployment
NVIDIA Jetson Orin/AGX, Intel NUC, Raspberry Pi CM4, Coral Edge TPU
On-Premises Server
GPU servers (NVIDIA A100, RTX 4090), x86 Linux, Windows Server
Cloud Platforms
AWS SageMaker, Azure ML, Google Cloud Vertex AI, private cloud
Hybrid
Edge inference with cloud analytics optimal for air-gapped plants requiring data sovereignty