Consulting       process

Our consulting process

Empowering businesses with AI Solutions in few simple steps

Step one — Data Collection

The magic starts with data. We work closely with our clients to create large and diverse datasets. The time it takes for data collection depends on the client's data availability, but it usually ranges from 1 week to 1 month.

Step two — Data Annotation

Once we have some data, we start the annotation process. This involves labeling images and videos with relevant information, which helps the neural network learn to recognize patterns and make sense of the data. Data annotation typically takes between 2 weeks and 1 month.

Step three — Model Training and Iterative Improvement

After some data has been annotated, we train a neural network on it. This phase usually takes around 2 months. We continuously improve the model through rigorous testing and fine-tuning, ensuring that it meets the desired accuracy and performance benchmarks. We keep iterating until the model is production-ready.

Step four — Porting and Integration

The final step is porting the neural network to the target inference platform and integrating it into the production environment. This process takes about 1 month, after which you'll have a fully functional model in your target production environment.In addition to delivering the model, we also provide comprehensive documentation, full code, and any necessary training for your team. This enables you to work with the model and even retrain it in the future if needed.
The OpenCV connection

We are the very team behind OpenCV – which means that we have the know-how and experience to build production-grade solutions.  in few simple steps

Want to know what we are using? Here’s the list.

Tools and hardware we use to work wonders.

Core ML
Deepstream SDK
Raspberry Pi
Intel / AMD CPU
Intel Myriad X
Deploy everywhere

Cloud, local, or edge device deployment.

On edge devices

The data can be processed on the device used to capture it, thus enhancing user privacy and saving big on server costs.
AI on Edge Devices


Cloud solutions are sure to provide a flexible and cost-effective way to leverage computer vision models for a variety of applications. Easily scale up or down depending on the size and complexity of the case.
AI in the server

On-premise servers

With on-premise inference the information stays at the source, which keeps your data secured and protected.
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