AI Services
AI Services service track
We develop object detection, image classification, OCR, video analytics, quality inspection, and visual intelligence systems.
Approach
The work is structured around explicit decisions and usable outputs rather than a generic delivery template.
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.
Fit
The strongest engagements have a clear operating constraint, decision, workflow, or delivery risk to improve.
Best fit
Typical use cases
Deliverables
Each workstream is labelled for the outcome or artifact it is responsible for, not its position in a template.
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
Process
A visible sequence of decisions, working outputs, review points, and handover, rather than a black-box delivery cycle.
We review image sources, camera conditions, labels, accuracy needs, and operational constraints.
We define labeling rules, collect samples, clean data, and establish evaluation criteria.
We train or integrate detection, OCR, classification, or video analytics models.
We integrate outputs into workflows and plan monitoring, review, and dataset expansion.
Outcomes
Automated visual recognition workflow
Clear accuracy and error profile
Practical deployment design
A path for improving the dataset over time
FAQ
The answers below clarify scope, collaboration, ownership, and the conditions that usually affect delivery.
Yes. We can build object detection pipelines using models such as YOLO and related tooling, depending on the dataset and deployment requirements.
Usually yes for custom vision models. We can help define labeling guidelines and assess whether existing models are enough before custom training.
Yes, but model size, hardware, latency, camera feed, and network reliability must be considered early.
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.
Yes. OCR can feed document intelligence, validation, extraction, search, review queues, or LLM-based workflows where appropriate.
Related Services
Next step
Bring sample data, camera context, and the decision the system must support. We will help shape the vision pipeline.
