Feasibility study for machine vision and AI
Gain clarity early before major project costs arise
A feasibility study is the most sensible starting point when it is still unclear whether a task can be solved reliably with machine vision, AI or a camera-based measurement or inspection system. Instead of investing directly in full implementation, we assess early on which data is available, what quality can be achieved and which technical boundary conditions must be met for a robust solution.
This saves time, reduces risks and creates a reliable basis for decisions about the next step. Especially with new products, varying surfaces, difficult lighting conditions, borderline cases or high variant diversity, this phase is often crucial for later project success.
When a feasibility study is especially useful
Typical starting situations
- An inspection or measurement process is to be automated.
- It is unclear whether camera and lighting are sufficient.
- An AI approach sounds promising, but the data situation is uncertain.
- There is no experience yet regarding accuracy, tolerances or stability.
- The task includes many variants, disturbances or borderline cases.
Typical goals
- Make technical risks visible early
- Assess achievable quality and accuracy goals
- Select suitable methods and system architecture
- Evaluate capture conditions and the data basis
- Provide technical backing for the implementation decision
What is specifically examined in the feasibility study
Data and image capture
- Quality, quantity and representativeness of existing images or measurement data
- Variant diversity, borderline cases and typical defect patterns
- Suitability of camera, optics, triggering and lighting
- Influence of position, surface, motion and ambient light
Evaluation and target values
- Realistic requirements for accuracy, hit rate and tolerances
- Suitable approaches from classical machine vision or AI
- Initial prototype results, metrics and technical limits
- Open issues, risks and sensible next steps
Depending on the task, we look at both classical algorithms and AI methods. It often turns out that a good solution consists of a sensible combination of both approaches.
Typical results of a feasibility study
The outcome is not a vague estimate, but a well-founded assessment. You receive a traceable statement on whether and under which conditions a task can be solved, which accuracy appears realistic and which technical measures are required.
From this, it can be derived whether direct implementation makes sense, whether additional data or test recordings are needed first or whether the boundary conditions should be adjusted. For many customers, this is the decisive basis for budget, prioritization and project planning.
Typical outcomes
- Reliable statement on technical feasibility
- Assessment of opportunities, risks and critical influencing factors
- Recommendation for camera, lighting and system setup
- Suitable solution approach for machine vision, AI or measurement systems
- Concrete basis for the subsequent implementation
Practical examples of a feasibility study
In optical quality inspection, the question is often whether defect patterns can be detected reliably even though material, surface or lighting vary. A feasibility study shows early which defects can be detected reliably and where additional measures become necessary.
With camera-based measurement systems, the focus is often on achievable accuracy. We then examine whether resolution, perspective, calibration and mechanical conditions are sufficient to maintain required tolerances stably.
For color inspection, 3D data, anomaly detection or the modernization of existing systems as well, an early technical assessment is often much more economical than direct full implementation.
Your benefit for the project
A good feasibility study reduces project risks because assumptions are checked early and technical uncertainties are made visible. This is especially valuable when several solution paths are possible or when an internal decision needs solid backing.
You gain a realistic basis for effort, priority, technical concept and further investments. Instead of assumptions, you get a technically sound assessment with clear recommendations for action.
From assessment to implementation
If the feasibility assessment is positive, the next phase can follow directly: concept, prototype, integration and productive application. This makes the transition much more structured, because goals, boundary conditions and critical points are already known.
Suitable follow-up steps include, for example, the implementation of a machine vision solution, the selection of suitable camera systems or a retrofit of existing inspection and evaluation systems.
Frequently asked questions about the feasibility study
How much material is needed for a feasibility study?
That depends on the task. For an initial assessment, typical sample images, measurement data or test recordings are often enough. For complex AI tasks, a broader data basis usually makes sense.
Is a feasibility study only useful for AI projects?
No. It is just as useful for classical machine vision, quality inspection, measurement systems and 3D evaluation. Technical limits can often be identified very early in precisely these areas.
What is the difference between a feasibility study and implementation?
The feasibility study evaluates whether and how a task can be solved sensibly. Implementation builds on this and turns the chosen approach into a stable application.
Can this lead directly to a concrete project?
Yes. In many cases, the feasibility study serves as the starting point for a follow-up project with concept, prototype and productive rollout.