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For ML teams

Delayed dataset, costly relabel or inconsistent quality? Labeling with clear criteria, ready for training.

Classification, detection, segmentation, video and text — with human validation and delivery shaped for your pipeline, so the model isn't waiting (or relearning) for nothing.

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Share volume, annotation type and deadline. We'll return with the next step.

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Case study

Leibniz Universität Hannover Research Project

Institute of Mechatronic Systems - Leibniz Universität Hannover

Robotic Scrub Nurses (RSN) Research Project

Research Collaboration

Partner Perspective

In my research project at the Institute of Mechatronic Systems (IMES) at Leibniz Universität Hannover, we tackled key challenges in developing Robotic Scrub Nurses (RSNs). FexData's expertise and dedication were instrumental in testing and validating the system on real-world data with exceptional precision and efficiency. Their high-quality work delivered promising results, bringing us closer to functional RSNs that could assist in operating rooms and help address the global shortage of healthcare professionals.

Dr.-Ing. Jorge Badilla-Solórzano

Researcher, Institute of Mechatronic Systems

Why FexData

Why label with FexData

What cuts delay and relabel

  1. People

    • Domain context

      Specialists who understand business criteria, not just the annotation UI.

    • Teams aligned to the model

      Internal training on your ML requirements before scaling volume.

  2. Operations

    • On-demand pace

      Adapt to spikes and guideline changes without losing consistency.

    • Quality with human review

      Multi-stage human-in-the-loop validation before every delivery.

  3. Delivery

    • Secure infrastructure

      Controlled dataset access, encrypted in transit and at rest.

    • Visible progress

      Quality and pace tracking — so training isn't flying blind.

Labeling ready for your training run

Less waiting, less relabel, more consistency across batches.

Image after annotation
Image before annotation

  1. Scale with criteria

    Volume without giving up consistency across annotators.

  2. Verifiable quality

    Staged review before releasing a batch to the model.

  3. Domain focus

    Teams dedicated to your data type and project sector.

Capabilities

Annotation types

What your model needs: classification, boxes, masks, video and text — with a clear guideline and delivery in your pipeline format.

  • Image categorization

    Classes and hierarchies aligned to your model ontology — multi-class when training requires it.

Classification
Classification Image categorization

Security and compliance

Labeling means access to your dataset

That is trust we do not treat as a detail. From upload to delivery, access stays controlled, encrypted and verifiable.

  • Data protection compliance

    Infrastructure and processes aligned with GDPR and LGPD requirements.

  • End-to-end encryption

    Your dataset is encrypted in transit and at rest.

  • Human-in-the-loop

    Human review before every delivery — quality without shortcuts.

Let's unblock your dataset

Tell us the volume, annotation type and deadline. We'll come back with a clear execution plan.

Talk to a specialist

Or use the form — technical team, not an autoresponder.

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