From Visual Data
to Working Models.

Describe your hosting setup, CPU or GPU, expected workload, and application. The delivery brings the model, inference runtime, and configuration together in a package for the agreed environment. Your team deploys it on that infrastructure and connects its application to the prediction API. Hardware compatibility and resource requirements are confirmed as part of the project.

There are two parts: fine-tuning the model on your examples to improve task-specific predictions, and optimizing how inference runs on your target hardware. Accuracy, response time, memory, and throughput are evaluated together on representative inputs. The aim is a practical fit for your workflow, not a generic performance promise.

Sivela is designed around a focused project, not a large AI department. Start with one task, such as sorting product photos or checking whether an item is present. The visual workflow connects data preparation, annotation, training, and prediction review, so your team can follow the whole process in one place.

Existing product photos or inspection images can be a useful starting point. First check that they show what you want the model to recognize and cover the conditions it will encounter. You may need to collect more examples or clarify the labels before training. Our dataset checklist explains how to assess your starting point.

The studio brings together the computer vision workflow: preparing and generating image data, annotating it, selecting a model, training, and testing predictions before deployment. It is designed to help teams move between these steps without losing the context of their project.

The visual workflow is designed for domain experts and product teams to contribute without writing training code. For deployment, the deployment package brings the model and its inference runtime together. Someone on your team or your technical partner still needs to deploy the package and connect your application, but should not need to assemble an AI serving stack.

Yes. The studio brings annotation, review, model selection, and evaluation into a shared workflow. Business teams contribute the context behind the data, while technical teams guide model and deployment decisions.

We walk through your use case, the data you work with, and the stages of the workflow you need. The session focuses on how your team would prepare a dataset, review labels, evaluate a model, and plan its next steps.

Pricing would depend on your project. Team needs, data volume, training workloads, and deployment requirements help define the scope. Discuss your use case with us as we shape the product.

Data handling, hosting, retention, and access requirements should be agreed as part of your project evaluation. Ask for the relevant technical details during your demo. Confirm the proposed setup with your security team before sharing production data.