Discuss your project.

Tell us what you’re building, what data you have and where the current process is getting stuck. We’ll help define a practical pilot, quality approach and delivery plan.

What happens next

Our project team replies by email to arrange a first call.
On the first call we go through the task, the data, the outputs you need and how quality will be judged.
We test the approach on representative samples of your data before production starts.

Where we are

Offices in Tel Aviv and Haifa, Israel
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What AI teams need to know before starting.

Every program is different. These are the practical questions we resolve early so your team can move forward with clarity.

Our annotators are employed and trained in-house, never crowdsourced.

Through Point AI Studio, our data platform. Your datasets are organised into projects there, you follow each batch through annotation and review, and you export deliveries in the agreed format, through the API or by cloud delivery.

We support image, video, 3D and sensor, text and language, and audio data. Engagements can include new annotation, review of existing labels, correction of model-generated pre-labels and ongoing quality operations.

We start by understanding the model use case, data, required outputs, quality criteria and timeline. A representative pilot then helps refine the instructions and confirm the right production approach.

Quality is tied to project-specific acceptance criteria, not a generic promise. We use calibration, documented decisions, structured review and feedback to keep interpretation consistent as requirements evolve.

Yes. We can support a focused dataset or a recurring data program. The team structure, review process and delivery rhythm are matched to the scope and maturity of the project.