Discovery Call
We learn your platform, vision task types, deployment conditions and testing priorities in a focused 30-minute session.
Computer vision models deserve rigorous QA
Testiva delivers specialist QA for Computer Vision Applications detection accuracy, classification testing, bias testing, and regression after every retrain.
50K+
Vision Outputs Evaluated
3x
Faster Release Cycles
40%
Lower Rework Costs
Why it matters
What happens when Computer Vision Applications
What happens when Computer Vision Applications
aren't tested properly
In production computer vision, accuracy failures aren't just wrong predictions they drive incorrect decisions, create safety incidents, produce legal liability, and degrade silently across deployment conditions that controlled test environments never replicate.
Detection failures under real-world visual conditions
Vision models that perform well in controlled test environments fail under poor lighting, partial occlusion, motion blur and weather degradation conditions that only structured real-world testing suites surface before deployment.
Demographic bias in detection and classification systems
Vision models that produce systematically lower accuracy for specific demographic groups create legal exposure, safety risk, and regulatory scrutiny that aggregate mAP metrics never capture at the class or subgroup level.
Silent accuracy degradation after model retraining
A retrained model may improve overall mAP while degrading detection accuracy for specific object classes or scene types and the regression will not be visible until production incidents or user complaints surface it.
Real-time video inference failures under production load
Vision systems that meet accuracy benchmarks in offline evaluation fail under real-time video stream conditions frame processing latency, throughput bottlenecks and concurrent stream handling only surface under load testing.
How Testiva protects your platform
- Computer vision evaluation expertise — Our QA engineers specialise in vision model testing, annotation quality, and real-world condition evaluation not generic software or AI testing.
- Annotated ground truth dataset build — We build and validate ground truth annotation sets for your specific object classes, scene types and deployment conditions so every metric is measured against what actually matters.
- Structured real-world condition testing — We run evaluation suites across lighting conditions, occlusion scenarios, motion blur, weather degradation and camera angle variations that controlled environments miss.
- Demographic bias measurement across vision outputs — We measure detection and classification accuracy parity across age, gender, skin tone and ethnicity quantifying the disparity gap and tracing it to training data or model architecture.
- Automated regression on every retrain and dataset update — Accuracy baselines established and monitored continuously detecting class-level degradation, fairness regressions and precision-recall shifts after every model update.
What we test
Core components of a Computer Vision Application we cover.
Every layer that affects detection accuracy, fairness, real-world robustness and production reliability is validated, stress-tested and verified across models, datasets and deployment conditions.
Object detection accuracy
Precision, recall and mAP measured against annotated ground truth across object classes and scene types.
Image classification testing
Top-1 and top-5 accuracy, confusion matrix analysis and misclassification pattern testing across category sets.
Segmentation quality testing
IoU, boundary accuracy and pixel-level precision evaluated across segmentation masks and scene complexity levels.
Demographic bias & fairness testing
Detection accuracy parity measured across age, gender, skin tone and ethnicity disparity gaps quantified per subgroup.
Real-world condition robustness
Accuracy tested across poor lighting, occlusion, motion blur, weather degradation and camera angle variations.
Real-time video stream testing
Frame processing latency, detection consistency and accuracy verified under concurrent real-time video stream loads.
Annotation quality & data set validation
Ground truth annotation accuracy, label consistency and dataset coverage gaps audited before model training begins.
Regression & retrain testing
Class-level accuracy and demographic parity re-evaluated automatically after every model retrain and dataset update.
Performance & scalability testing
Inference latency, throughput and accuracy consistency verified under concurrent high-volume production workloads.
HOW IT WORKS
Up and running in 4 simple steps
From first contact to your first test report a process designed to be fast, transparent and low-friction.
QA Audit & Plan
We audit your annotation quality, model coverage gaps and build an evaluation strategy with ground truth datasets for your specific classes.
Test Execution
Our team runs detection accuracy evaluation, condition robustness testing, bias audits and regression tests logging every finding with full evidence.
Report & Iterate
You receive a detailed report with class-level accuracy metrics, bias findings, condition degradation analysis and remediation recommendations.
What People Say
Testiva is a great team to work with. I’ve hired them multiple times and recommended them to others, all impressed by their thorough work. Highly recommended for QA.
Testiva team is highly skilled and extremely thorough. I trust them for accurate and timely delivery. They are a reliable resource for any project.
Testiva team delivered outstanding quality with great professionalism. Communication was excellent and delivery met expectations. Highly recommended.
Excellent team worked well with minimal supervision and did a great job. Their work helped us improve the robustness of the platform.
Computer Vision Applications
Testing Packages
| Feature | Starter | Professional | Enterprise | Custom AI |
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| CORE FUNCTIONAL TESTING | ||||
| End-to-end vision pipeline testing |  |
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| Object detection accuracy evaluation | |
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| Image classification testing | |
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| Concurrent inference / load testing |  |
5K images | 10K images | Unlimited |
| Automated regression test suite | |
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Setup only | Full build |
| CI/CD pipeline integration | |
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| COMPUTER VISION-SPECIFIC TESTING | ||||
| Ground truth annotation dataset build | |
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| Precision, recall & mAP measurement | |
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| Segmentation quality & IoU testing | |
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| Real-world condition robustness testing | |
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| Real-time video stream testing | |
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| Annotation quality & dataset audit | |
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| Class-level performance breakdown | |
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| A/B model version comparison | |
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Setup only | Full build |
| AI FAIRNESS & SAFETY | ||||
| Demographic bias & fairness evaluation | |
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| Subgroup accuracy disparity analysis | |
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| Adversarial robustness & attack testing | |
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| Edge case & out-of-distribution testing | |
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| Model version & rollback testing | |
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| SECURITY, PRIVACY & COMPLIANCE | ||||
| PII & biometric data handling audit | |
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| Data access & permission boundary QA | |
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| GDPR / CCPA compliance testing | |
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| Enterprise SSO & access control testing | |
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| SUPPORT & REPORTING | ||||
| Dedicated QA lead | |
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| AI quality scorecard & weekly report | |
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| 24/7 critical defect SLA | |
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Common questions
Model accuracy on edge cases, demographic bias in detection, real-world condition robustness, and decision explainability at scale none of which appear in standard functional or unit test suites.
We evaluate precision, recall and mAP against annotated ground truth datasets covering edge cases, low-confidence scenarios, class imbalance effects and performance across scene complexity levels.
We measure accuracy parity across age, gender, skin tone and ethnicity reporting disparity gaps and their downstream decision impact, and tracing bias to training data composition or model architecture.
We run evaluation suites across degraded visual conditions measuring accuracy drop, failure mode patterns and confidence threshold behaviour per condition type and object class combination.
We measure detection accuracy, frame processing latency and decision consistency under real-time video feed conditions benchmarked against your latency SLA and accuracy requirements at production stream volumes.
We re-run full evaluation suites against validated baselines measuring accuracy changes by class, condition type and demographic subgroup to detect both improvements and regressions introduced by the retrain.
Get in touch
Start with a discovery call.
Tell us about your computer vision application and we'll map out exactly what testing you need no obligation.
Email us
Book a call
30-minute discovery sessions available Mon–Fri
Fast response
We reply to all enquiries within 1 business day