Major Sports Broadcasting Network
500k+
Frames Labeled
99.4%
Annotation Accuracy
60 FPS
Inference Speed
100%
Regulatory Compliance
AI & Automation · Computer Vision
See what humans miss. Predict what data reveals.
Computer vision development company delivering custom machine learning solutions — image recognition, defect detection, real-time video analytics services, and deep learning engineering services for healthcare, manufacturing, and security.
Custom machine learning solutions
Computer vision development company
Real-time video analytics services
Edge AI implementation services
Deep learning engineering services
MLOps & model monitoring
98.2%
Detection accuracy
Precision validated on real-world production data.
<60ms
Inference latency
Real-time processing for live video and image feeds.
15+
Industry verticals
Healthcare, manufacturing, security, retail, agriculture.
92%
Defect escape reduction
Automated QC catching what manual inspection misses.
Why MetaDesign
From pixel to prediction — hire computer vision developers.
Industrial AI & vision company delivering models that run in prod, not just notebooks. Trained, validated, and monitored.
Teams shipping with MetaDesign
Selected work
Case studies.
ForceHQ
1M+
Facial Keypoints Annotated
85%
False Positives Reduced
100%
Demographic Balancing
<50ms
Gaze Detection Latency
DETECT.
CLASSIFY.
PREDICT.
OPTIMISE.
Manufacturing QC
Hire computer vision developers who build automated visual inspection detecting defects at 98%+ accuracy — an industrial AI & vision company replacing manual quality checks on production lines.
Medical Imaging
Deep learning engineering services for AI-assisted diagnosis from X-rays, MRIs, and pathology slides — augmenting radiologist workflow.
Security & Surveillance
Real-time video analytics services powering object detection and tracking systems, face recognition, and anomaly detection for physical security.
Predictive Maintenance
Custom machine learning solutions for sensor data analysis that predicts equipment failures before they happen — reducing downtime by 35%.
Document Intelligence
OCR, table extraction, and document classification — turning unstructured documents into structured data.
Agricultural Analysis
Edge AI implementation services for crop health monitoring, yield prediction, and pest detection from drone and satellite imagery.
Our Approach
Computer Vision: Our Proven Process
01
Data audit
Assess your data quality, quantity, and labelling — define the ML pipeline architecture and success metrics.
02
Train
Model selection, hyperparameter tuning, and iterative training with your domain-specific datasets.
03
Validate
Cross-validation, bias testing, edge-case analysis, and performance benchmarking against baselines.
04
Deploy
Production deployment with model serving, A/B testing, and monitoring infrastructure.
05
Monitor
Drift detection, automated retraining triggers, and performance dashboards for continuous improvement.
01
Data audit
Assess your data quality, quantity, and labelling — define the ML pipeline architecture and success metrics.
02
Train
Model selection, hyperparameter tuning, and iterative training with your domain-specific datasets.
03
Validate
Cross-validation, bias testing, edge-case analysis, and performance benchmarking against baselines.
04
Deploy
Production deployment with model serving, A/B testing, and monitoring infrastructure.
05
Monitor
Drift detection, automated retraining triggers, and performance dashboards for continuous improvement.
Customer value
Why enterprises trust us with their AI.
Real outcomes our clients report within the first engagement cycle.
Higher automation rates
Eliminate repetitive tasks and free your team to focus on strategic work.
Measurable accuracy
AI models with tracked precision, recall, and F1 scores — not guesswork.
Faster decision-making
Real-time insights and predictions that accelerate business decisions.
Reduced operational cost
Automation that pays for itself within the first quarter.
Production-grade reliability
Guardrails, monitoring, and fallback logic built into every AI system.
Knowledge transfer
Your team learns to maintain and extend AI systems independently.
By the numbers
400+
Engineers worldwide
200+
Active clients
20yr
Pure-play software
94%
Client retention
ENGAGEMENT MODELS
Three ways to deploy Computer Vision with our engineers.
From automated defect detection on factory lines to medical imaging and real-time video analytics, choose the model that fits your operational needs.
MOST POPULAR
Computer Vision Pod
End-to-end computer vision team handling dataset labeling, custom model architecture (YOLO, OpenCV, PyTorch), edge optimization, and cloud inference.
- Custom model training & tuning
- Edge & TensorRT acceleration
- Labeling pipeline & active learning
- Milestone-based delivery
EMBED
Hire Computer Vision Engineers
Senior CV engineers with hands-on experience in OpenCV, segmentation, object tracking, and edge deployment on NVIDIA Jetson and iOS/Android hardware.
- Fast 2-week placement
- PyTorch, TensorRT & CUDA experts
- Direct engineer communication
- Flexible scale-up / scale-down
LAUNCH
Vision MVP & Pipeline Build
Build and validate a working computer vision pipeline in 6–8 weeks with verified precision/recall benchmarks on your proprietary camera or sensor feeds.
- Proof-of-concept to production
- Fixed scope & clear deliverables
- Hardware & edge compatibility
- Full model weights & IP ownership
FAQ
Asked first, every time.
Don't see yours here? Send us the question — a principal engineer will reply within 24 hours.
It depends on complexity. For basic classification: 500–1,000 labelled samples. For complex vision tasks like defect detection, our deep learning engineering services use transfer learning and data augmentation to work effectively with smaller datasets — sometimes as few as 200 images.
Yes. Our edge AI implementation services optimise models for edge deployment using ONNX, TensorRT, and quantisation — running inference on NVIDIA Jetson, FPGAs, Raspberry Pi, or even mobile devices with minimal latency.
We test for demographic and dataset biases using fairness metrics, stratified evaluation, and adversarial testing. As a computer vision development company, we provide bias audit reports and implement mitigation strategies before production deployment.
Automated drift detection monitors prediction distributions in real time. When accuracy drops, our MLOps pipeline triggers retraining with new data, validates the updated model, and deploys it — hire computer vision developers who build self-healing ML systems.
Yes. We set up labelling workflows using Label Studio or custom tools, with multi-annotator review, quality metrics (inter-annotator agreement), and active learning to prioritise the most valuable samples for labelling.
Absolutely. Our custom machine learning solutions build ML pipelines on top of your existing data stack — Snowflake, BigQuery, S3, Databricks, or custom data lakes — with feature stores and model registries for reproducibility.
Traditional image processing uses hand-coded rules (thresholds, filters). Computer vision uses deep learning to automatically learn patterns from data, handling variations in lighting, angle, and defect types that rule-based systems cannot. Our real-time video analytics services generalise to new scenarios without manual tuning.
We build explainable AI with interpretability layers (SHAP, LIME, attention maps), maintain full model lineage and audit trails, and conduct bias testing. For FDA-regulated use cases, we follow IEC 62304 software lifecycle standards.
Yes. We deploy models on streaming platforms like Kafka and Kinesis for sub-second predictions on live data — fraud detection, anomaly alerts, and predictive maintenance triggers in real time.
We typically recommend AWS SageMaker, Google Cloud AI Platform, or Azure Machine Learning for scalable, enterprise-grade custom machine learning solutions, depending on your existing infrastructure.
Build production ML models with a team that ships.
Tell us about your project. We'll come back with a plan, a timeline, and the right team — no obligations.