How To Guides for AI Inspection
Practical, step-by-step guides for building, maintaining and scaling AI-powered inspection with ReliVision.
Training Data
Generate Synthetic Defect Data
Produce realistic synthetic defect images with the on-prem GenAI engine when real defect examples are too rare to train a reliable model on.
Read guide →Training Data
Deploy Inspection with Minimal Data
Use few-shot training and on-prem generative augmentation to stand up a working inspection model from only a handful of defect examples.
Read guide →Getting Started
Build AI Inspection with Our Intuitive UI
Go from raw images to a deployed inspection solution using ReliUI's no-code interface — annotation, training and deployment without writing a line of code.
Read guide →Strategy
Design a Scalable, Zero-Defect Inspection Strategy
Plan inspection coverage across stations, lines and sites so quality targets hold as volume grows, instead of solving one station at a time.
Read guide →New Product Introduction
Eliminate the Inspection Gap at NPI
Close the window between the first build of a new product and a stable inspection recipe, so escapes and rework do not concentrate in the ramp.
Read guide →Pipelines
Build a Pipeline with Custom Post-Processing
Assemble an inspection pipeline and add custom post-processing logic so raw model output becomes a pass/fail decision your line can act on.
Read guide →Model Maintenance
Maintain AI Models without a Data Science Team
Monitor, flag, fine-tune and deploy updated models in-house — the full maintenance loop run by the QA team in under three hours.
Read guide →Data Labeling
Accelerate Image Labeling with NLP & Image-Prompted Coding
Generate a classical image-processing node from a text prompt and a reference image, and pre-label defects like porosity before deep-learning training begins.
Read guide →Pipelines
Catch Multiple Defects with a Single AI Pipeline
Build one branching pipeline that checks label presence, label defects, printed characters and surface condition in a single pass.
Read guide →
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