AI Services
Computer Vision
We develop object detection, image classification, OCR, video analytics, quality inspection, and visual intelligence systems.
Why it matters
Systems that understand images, video, and visual operations.
Computer vision is useful when visual information needs to be counted, classified, extracted, inspected, or monitored consistently. The real challenge is usually data quality, labeling, lighting, camera conditions, and operational deployment.
Solutyics designs vision systems around the environment where they will run. We consider dataset preparation, model choice, accuracy requirements, edge or cloud deployment, human review, and how results flow into the business process.
Primary intent
Build image and video intelligence systems for operational use.
Fit
Where this work creates leverage.
Ideal for
- Companies automating visual inspection or counting
- Teams extracting information from images or documents
- Operations that need video analytics
- Products adding image understanding features
Typical use cases
- Quality inspection
- Object detection and counting
- OCR from documents or images
- Video analytics for operational monitoring
Scope
What Solutyics delivers.
Deliverables
- Vision use-case and dataset plan
- Labeling guidance and model development
- Object detection, OCR, or classification pipeline
- Deployment architecture for edge or cloud
- Accuracy report and monitoring recommendations
Considerations
- Lighting and camera placement affect accuracy
- Labeled data quality is critical
- False positives and false negatives have different business costs
- Edge deployment may require model optimization
Outcomes
What should improve after the work.
Automated visual recognition workflow
Clear accuracy and error profile
Practical deployment design
A path for improving the dataset over time
Process
How the work moves
Assess the environment
We review image sources, camera conditions, labels, accuracy needs, and operational constraints.
Prepare the dataset
We define labeling rules, collect samples, clean data, and establish evaluation criteria.
Build the model pipeline
We train or integrate detection, OCR, classification, or video analytics models.
Deploy and improve
We integrate outputs into workflows and plan monitoring, review, and dataset expansion.
FAQ
Questions that shape the work.
Can you build object detection systems? +
Yes. We can build object detection pipelines using models such as YOLO and related tooling, depending on the dataset and deployment requirements.
Do we need labeled images? +
Usually yes for custom vision models. We can help define labeling guidelines and assess whether existing models are enough before custom training.
Can computer vision run on edge devices? +
Yes, but model size, hardware, latency, camera feed, and network reliability must be considered early.
How do you measure accuracy? +
We use metrics suited to the task, such as precision, recall, IoU, character accuracy, or task-specific error rates, and interpret them against business consequences.
Can you combine OCR with AI workflows? +
Yes. OCR can feed document intelligence, validation, extraction, search, review queues, or LLM-based workflows where appropriate.
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Next
Turn images and video into operational signals.
Bring sample data, camera context, and the decision the system must support. We will help shape the vision pipeline.