The setup of a 2D or 3D annotation project for Deep Learning applications requires precise organization, strong responsiveness, and an immediate understanding of the client’s objectives. At Infoscribe, we have structured our processes to ensure an extremely fast project kickoff while maintaining a level of quality compatible with the requirements of modern Deep Learning pipelines.

Concretely, we can launch a pilot within 48 hours after scope approval. This rapid turnaround is made possible by our ability to quickly mobilize a trained team capable of understanding the specific constraints of your Deep Learning models: architecture, data formats, annotation granularity requirements, and potential dataset biases.

During these 48 hours, we handle all critical steps: annotator onboarding, detailed training on guidelines, annotation platform setup, workflow integration, and preparation of quality control procedures. This accelerated preparation is essential to deliver data that is immediately usable in your Deep Learning training cycles, whether for 2D convolutional models, 3D networks such as PointNet, or multi-task pipelines.

For larger projects beyond the scope of a pilot, the average setup time typically ranges from 48 hours to 5 business days. This period allows us to refine ontologies, adjust annotation examples to the nature of the Deep Learning tasks, and ensure optimal inter-annotator consistency before scaling up. We know that annotation quality directly impacts the performance, robustness, and generalization capacity of Deep Learning models, which is why we pay special attention to the initial precision of the workflow.

Once the project is underway, our teams remain available to continuously adjust processes based on feedback from your first Deep Learning training runs. This ongoing dialogue allows us to optimize data structure, reduce label ambiguities, and progressively enhance dataset relevance. The goal is clear: to ensure that every batch of data provided contributes directly to improving the efficiency and stability of your Deep Learning models in production.

Thanks to this combination of speed, methodological rigor, and Deep Learning expertise, we guarantee a smooth, secure, and fully adapted project kickoff for your AI initiatives.

The pricing models we offer are based on a simple, flexible structure tailored to the nature of your annotation project.

The first approach is time-based billing, using a scalable rate depending on the number of hours ordered: the more hours purchased, the lower the hourly rate. This structure applies to various types of services, including classification, 2D bounding, segmentation, or point cloud annotation.

The second approach is unit-based billing. In this model, Infoscribe sets a cost per annotated image or object. The calculation is based on an analysis of the time required, an estimation of the annotation pace, and conversion into a unit price. Projects involving millions of images can be evaluated based on the total time needed.

Finally, pricing can also be adjusted according to task complexity, quality control requirements, or the desired level of support. Each project is preceded by a detailed assessment to precisely tailor the chosen model.

Infoscribe offers a flexible pricing structure combining hourly billing, unit-based billing, and customized adjustments, effectively meeting the diverse needs of annotation projects.

Infoscribe is structured to efficiently meet the needs of large-scale Deep Learning projects. With a team of over 300 annotators, the company can quickly mobilize human resources to handle increased workloads.

When data volumes rise significantly—whether images, videos, or 3D point clouds—Infoscribe adapts by combining flexibility, workforce scaling, and a robust annotation workflow.

In practice, our internal processes ensure that data is prepared, validated, and annotated according to standards that meet Deep Learning model requirements.

The human-in-the-loop approach combines human precision with the efficiency of AI/Deep Learning systems: it allows automatic predictions to be validated or corrected, reducing errors while accelerating production—a key advantage when volumes surge.

Additionally, Infoscribe implements systematic quality control, including internal reviews, compliance reporting, and iterative corrections where necessary, ensuring consistent annotations—essential for training robust Deep Learning models.

For large volumes, Infoscribe’s production architecture—which combines multiple subsidiaries, task distribution, and dedicated project managers—enables scaling without compromising quality. This agile setup ensures your AI/Deep Learning projects remain performant even in demanding contexts.

In summary, Infoscribe provides operational and human scalability while delivering reliable, reproducible annotations perfectly suited to the constraints of Deep Learning development.

Yes, Infoscribe offers a trial in the form of a POC (Proof of Concept) that allows you to test our annotation workflows before committing to a long-term engagement. This approach is especially valuable for companies seeking to validate data processing quality, methodological rigor, and pipeline compatibility with their own Deep Learning models.

The POC reproduces the full data processing cycle on a small scale: receiving the dataset, defining guidelines, configuring tools, annotation by our expert teams, multi-layer quality control, and delivery of outputs. This pilot phase provides a clear view of processing times, achieved accuracy, and expected consistency on larger projects. It is also an ideal opportunity to measure the added value of our methods for your Deep Learning initiatives.

During the POC, Infoscribe teams analyze the specific requirements of your use case: label granularity, ontology structure, export formats, and any constraints related to your Deep Learning pipeline. This phase allows data processing to be tailored to your objectives, whether for 2D or 3D annotation, video, object tracking, or complex segmentation.

The POC also validates our scalability. You can evaluate how our teams handle large-scale data processing, maintain annotation consistency, and adjust workflows to ensure a fully usable dataset in a Deep Learning environment.

At the end of the POC, you receive complete deliverables along with a performance report, a reliable cost estimate, and a realistic timeline projection for broader deployment. This allows you to objectively validate the effectiveness of our data processing before considering a long-term engagement.

In summary, the Infoscribe trial offer provides a reassuring and transparent step to confirm that our workflows are perfectly aligned with your Deep Learning needs.