Vision Engineering & Validation
RobuTrace is an engineering prototype under active development for industrial machine vision. It guides teams from the inspection task through system modelling and robustness tests to measurements, evidence and technical release decisions, while keeping simulation clearly separate from real-world proof.
The problem
Good individual results are not enough when context, data versions, parameters, variants and changes remain disconnected. Without a structured engineering process, teams cannot reliably explain why a vision system is stable or where it begins to fail.
Requirements, image data, variants, risks and decisions often live in separate tools, spreadsheets and people's heads.
Scripts, parameter sets and results change continuously. Without a baseline, it is hard to determine which change actually improved the system.
Sensitivity to illumination, position, blur, noise or product variation often becomes visible only once the prototype has reached the production line.
The current prototype
RobuTrace complements existing HALCON, OpenCV, AI and industrial-vision workflows instead of replacing them. The current version combines system design, robustness assessment, real measurement data and technical decision logic in one guided workflow.
Capture requirements, the inspection task and sample images in a structured way. Missing information remains visible instead of being treated as confirmed fact.
Connect the image chain, pipeline, engineering rules, risks and evidence in a traceable engineering knowledge graph.
Compare the nominal state, line changes and worst cases under controlled disturbance factors. Simulation remains explicitly a projection.
Evaluate measurements, data quality, test coverage and target KPIs. Only evidence that matches the current pipeline counts as proof.
Bring release gates, the baseline, risks and reporting together. Missing or outdated evidence visibly blocks the release decision.
More than a concept sketch
Clear status: the prototype is not yet a production-ready SaaS product. User roles, audit-proof storage and integrations with production lines, MES or QMS are still missing. System proposals are preliminary technical designs and must be validated with real plant data.
The benefit
Not accuracy for its own sake, but a clear path from the vision idea through engineering iterations to a defensible technical decision.
Who RobuTrace is for
Structure use cases, image data and technical risks earlier so customer projects can move from prototype to defensible solution more efficiently.
Standardise recurring engineering patterns and make project knowledge usable across data, tests, parameters and variants.
Assess changes during line ramp-up, product changeovers and optimisation using clear metrics and traceable history.
About RobuTrace
RobuTrace is developed by Sara Hofmann. The starting point is simple: industrial machine vision should not only work under ideal conditions, but remain measurably robust under real production conditions.
Experience in software development, optical system design and systems engineering feeds into a tool that combines technical depth with a clear engineering structure.
More about Sara HofmannRobuTrace Insights
Five typical gaps between a demo, a pilot system and a defensible production release.
Why a single accuracy value is not enough for industrial decisions.
A pragmatic introduction to perturbation, sensitivity and worst-case analysis.
Pilot phase
RobuTrace is looking for machine builders, integrators and production teams with a specific inspection problem, real operating constraints and representative sample images. The aim is not a sales pitch, but a technical pilot with an open outcome.
Prefer to write directly? info@robutrace.de