01 / Detection and Tracking
Follow every object. Understand every movement.
Original AI Analysis
Object class · Track ID · Count · Direction
Your visual data. Working intelligence.
Custom computer vision. Built for production.
We turn proprietary images, video and sensor data into computer vision systems ready to integrate into real products and operations.
01 / Detection and TrackingIllustrative demonstrations, not approved client cases.
Original AI Analysis
Object class · Track ID · Count · Direction
See what your cameras could understand.
Explore the original input and the analysis it can produce. Illustrations show the intended output; performance is validated on your data.
02 / Defect Detection
Locate faults before they leave the line.
Original AI Analysis
Defect location · Type · Review decision
03 / Image Segmentation
Go beyond boxes. See exact boundaries.
Original AI Analysis
Pixel masks · Coverage · Calibrated area
04 / Anomaly Detection
Highlight deviations from normal appearance.
Original AI Analysis
Anomaly heatmap · Score · Review regions
05 / Colour Analysis
Make visual consistency measurable.
Original AI Analysis
Reference comparison · Colour deviation
06 / OCR and Label Verification
Turn labels and codes into structured data.
Original AI Analysis
Extracted text · Format check · Match result
More ways to work with visual data.
Classification · Counting · Pose estimation · Thermal analysis · 3D vision · LiDAR + sensor fusion
Colour and physical measurements require appropriate calibration.
The process
From raw data to real operations.
One technical partner for the complete pipeline. Start with a focused question, then build against agreed acceptance criteria.
Discuss your use case →- 01
Define the outcome
Translate your process into measurable requirements and operating constraints.
- 02
Assess your data
Review quality, annotation needs and missing scenarios. Establish feasibility.
- 03
Build and validate
Train a baseline, evaluate on held-out data and document failure cases.
- 04
Optimize and integrate
Prepare the model and inference pipeline for your edge or cloud environment.
- 05
Improve over time
Use new examples to address drift, expand coverage and retrain as needed.
Ways to work together
Start with the level of commitment that fits your project.
01 / Decide
Feasibility Review
For teams that need to know whether a use case is technically and economically viable.
- Data quality assessment
- Technical feasibility analysis
- Recommended architecture
- Annotation estimate and pilot scope
02 / Prove
Model Development Pilot
A focused engagement that produces and validates an initial working model on representative data.
- Prepared evaluation dataset
- Baseline model
- Performance metrics and failure analysis
- Deployment recommendation
03 / Deploy
Production Computer Vision System
End-to-end development of a system prepared for integration and deployment.
- Dataset and annotation pipeline
- Trained and validated model
- Inference software, API or SDK
- Acceptance testing and deployment support
04 / Maintain
Continuous Improvement
Ongoing support when products, cameras or operating conditions change.
- Performance monitoring
- Dataset expansion and retraining
- Regression testing
- Deployment updates
Scope, deliverables and acceptance criteria are agreed before development begins.
Pilot engagements start from EUR 2,500, depending on data, scope and technical requirements.
What you receive
A deliverable your engineering team can actually use.
Depending on project scope, a delivery may include:
Deployment-aware development
Models are designed around the actual target environment: cloud, server, industrial computer or edge device.
Transparent evaluation
We document performance, limitations and failure cases instead of a single accuracy number.
Your data remains yours
Projects are structured around confidentiality, controlled access and clear ownership of client data and deliverables.
What you receive
- 01Trained model weights
- 02ONNX or TensorRT model
- 03Inference pipeline
- 04Docker container
- 05API or SDK
- 06Dataset and annotation specification
- 07Evaluation dataset
- 08Performance report and failure analysis
- 09Hardware benchmark
- 10Integration guide
- 11Source code defined in the agreement
- 12Deployment and validation support
Every project defines its deliverables and acceptance criteria before development begins.
Industries
Different industries. The same focus on delivery.
01
Manufacturing and Quality Control
Defect detection, component verification, assembly inspection, colour control and production monitoring.
02
Logistics and Warehousing
Counting, tracking, package identification, damage detection and movement analysis.
03
Robotics and Autonomous Systems
Detection, segmentation, localization, mapping, obstacle perception and sensor fusion.
04
Infrastructure and Energy
Asset inspection, change detection, thermal analysis and anomaly monitoring.
05
Agriculture and Environmental Monitoring
Crop analysis, object counting, land segmentation, condition monitoring and aerial imagery analysis.
06
Technology Products
Custom computer vision modules for software, hardware and connected products.
About
Visual intelligence, built with purpose.
Veyren Systems is a computer vision engineering company that builds production-ready visual AI systems from proprietary image, video and sensor data. We handle the complete pipeline, from data assessment and model development to validation, optimization and deployment.
Not sure whether your industry or use case fits? Send a short description and representative examples.
Before we start
Good questions. Clear answers.
Do we need annotated data?
No. We can assess existing annotations or define an annotation strategy for unlabelled data. The required volume and type depend on the task.
How much data do we need?
There is no universal minimum. Requirements depend on visual variability, the number of classes, operating conditions and the required performance. A representative sample is usually enough for an initial feasibility assessment.
Can you work with confidential data?
Yes. Confidential projects can begin under an NDA with agreed access, storage and deletion procedures.
Who owns the model and project deliverables?
Ownership and licensing are defined in the project agreement. Client-specific data remains the property of the client.
Can the model run on our own hardware?
Yes. Models can be prepared for cloud infrastructure, local servers, industrial computers, NVIDIA Jetson devices and other supported environments.
Can you improve an existing model?
Yes. We can evaluate an existing system, identify its main failure modes and determine whether improvements should come from data, training, model architecture or deployment configuration.
Can you guarantee a specific accuracy?
Performance targets can be defined, but they depend on data quality and the nature of the problem. We establish realistic acceptance criteria after reviewing representative data.
How long does a project take?
A focused feasibility or pilot engagement may take several weeks. Production timelines depend on data readiness, annotation volume, integration and target hardware.
What does a pilot cost?
Pilot engagements typically start from EUR 2,500. Production systems are scoped individually based on data, complexity, integration and deployment requirements.
Contact
What should your cameras understand?
Tell us about your data, current process and desired outcome. We will review the use case and determine the most appropriate next step.
No internal machine learning team required.