COMPUTER VISION SOLUTIONS
Computer vision for inspection and analysis
The line is moving. The inspector can't look at every unit, but a camera can.
- Consistent inspection at line speed, on every unit and every shift
- Confidence gating that routes only ambiguous cases to a person
- Measured on mAP, precision, and recall, not a marketing number
For a visual inspection use case, the flow is concrete.
Detect, score, route. The same logic extends beyond the line.
Recognition, counting, presence-checking and monitoring all follow the same detect-score-route pattern. Computer vision is part of our applied AI solutions portfolio, running on a governed AI platform with production monitoring from MLOps.
Capture
A camera captures each unit at the inspection point. Every unit seen, not a sample.
Detect and classify
A model locates and classifies defects, a scratch here, a missing component there, with a confidence score for each. A number behind every call.
Act on the clear cases
High-confidence passes and clear defects are actioned automatically: accept, reject, or divert. Straight through where it is safe.
Route the borderline
Ambiguous cases route to a human reviewer, who confirms or overrides. A person on the hard cases only.
Learn from review
Reviewer decisions feed back to sharpen the model on your hardest cases. The model improves where it struggles.
Same pattern, new tasks
Recognition, counting, presence-checking, and monitoring all follow the detect-score-route logic. One pattern, many use cases.
Put a tireless inspector on every unit, and a person only where the model is unsure.
The models, gating and monitoring that make inspection dependable.
- Data labelling and model training on your parts and defect types.
- Detection, segmentation, OCR, or VLM models matched to the task.
- Confidence gating with a human-review path for borderline cases.
- Edge or cloud deployment tuned to your latency and connectivity.
- An mAP, precision, and recall baseline, with monitoring for drift as conditions change.
Computer vision has honest metrics, and we hold ourselves to them.
mAP
Mean Average Precision: overall detection quality across defect classes and confidence levels.
Precision
Of the defects the model flags, how many are real. This controls false alarms and needless scrap.
Recall
Of the real defects, how many the model catches. This controls escapes to the customer.
Set the operating point
In safety-critical inspection we tune for recall and accept more review; where scrap is costly we protect precision.
Measure and monitor
High accuracy on clear cases, human review on the ambiguous ones, and drift monitoring through MLOps.
We match the model to the task, not one architecture to every problem.
A representative stack by capability. Edge deployment keeps inference fast and local where a cloud round trip is too slow or connectivity is unreliable.
Where decisions carry real consequence, we apply Responsible AI and Governance. Figures are placeholders; Softobiz to verify against your environment.
From missed defects under time pressure to consistent catch rates.
Challenge: Pickles relied on manual visual inspection that missed defects under time pressure.
Approach: YOLO-based detection with confidence gating and human review on borderline units.
Result: Higher, more consistent catch rates with inspectors focused on ambiguous cases. (Softobiz to verify.)
Applied AI solutions, built around measurable decisions.
Applied AI Solutions
The broader portfolio of generative, predictive and vision systems this solution belongs to.
Intelligent Document Processing
OCR, layout, and LLM extraction with validation and straight-through processing.
Structured Data Extraction
LLM and VLM extraction from forms, PDFs, and email across the long tail.
Claim Handling
Document AI, policy checks, and routing with human control on adjudication.
MLOps
Versioned pipelines and drift monitoring that keep the vision model accurate in production.
Responsible AI and Governance
The controls that apply where a detection decision carries real consequence.
What quality and operations leaders ask us first.
High on clear cases; ambiguous cases route to human review. We report mAP, precision, and recall at an operating point we set with you, then measure it in production, not a single headline figure.
Yes. We deploy at the edge for line-speed latency where cloud round trips are too slow or connectivity is unreliable.
We monitor for drift and retrain on new examples, and reviewer decisions on borderline cases continuously sharpen the model.
Tell us the defect that's costing you most in scrap or escapes, and we'll show what detection can catch at your line speed.
A model trained on your parts, gated by confidence, measured on mAP, precision, and recall.