Image evidence
Reviewed examples across products, defects, lighting states and changeovers determine whether a test is meaningful.
Vision AI planning
Estimate a computer-vision project around the line, not the demo
Camera placement, lighting, line rate, defect evidence and the action path decide the project. The model comes after those constraints are written down.
What the estimate measures
Production conditions, not model fashion
The public result is driven by evidence that can be inspected before a project is signed.
Decision window
Offline review, supervisor alert and a reject gate impose different latency and availability requirements.
Physical integration
Optics, mounting, enclosure, network segmentation and controller interfaces are inside the estimate.
Field ownership
Commissioning, obstruction detection, drift sampling, retraining and spares are counted before go-live.
Worked examples
How the inputs move the starting point
These examples explain the rules. They are not invented client results.
| Situation | Likely first path | Why |
|---|---|---|
| Thirty sample images, no reviewed classes, one proposed station | Feasibility Study · from $9,500 | The first decision is whether the available signal and image conditions can support the inspection. |
| One cell, reviewed defect history, PLC tag map, line-rate target | Vision System · $60K–150K | The evidence and interface are sufficiently defined for a line-side build and commissioning scope. |
| Mixed cameras across sites, safety alerts, restricted footage movement | Enterprise AI · $180K–500K+ | Multi-site rollout, privacy boundaries and operational validation dominate the work. |
Methodology v1.0.0
A versioned rule, open to inspection
The score has five dimensions: problem definition 20, data readiness 25, integration readiness 20, acceptance readiness 20 and operational ownership 15.
- 01The readiness score does not predict accuracy. It measures whether the data, physical station, action interface and acceptance process are ready to produce a defensible accuracy result.
- 02The estimator selects the published single-line band only after representative images, a defined decision window and a production owner exist.
- 03Hardware and commissioning are reflected at the band level; final components require a site survey and an optics test.
Limitations
What this result cannot establish
- The estimate is a planning band derived from the answers supplied. It is not a quote, warranty, or substitute for reviewing real data and interfaces.
- Third-party licences, cloud or hardware consumption, travel, and unusual certification work are outside the build band unless a proposal explicitly includes them.
- No model, accuracy, savings, or return claim is inferred from a questionnaire. Those require a feasibility test or measured baseline.
Questions
Before you use the range
No. Any accuracy number given before testing your real images is marketing. The estimator identifies the evidence needed to measure it.
The published vision-system band assumes a normal single line or cell and includes typical hardware and commissioning. The final component list follows the survey.
The likely first path is feasibility. Anomaly detection may be appropriate, but it still needs real held-out parts, controlled capture and agreement on what constitutes an actionable deviation.