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For ML teams

Specialized image and video labeling. For projects that demand precision and consistency.

Annotation by a team that understands how the data will be used in the model: explicit criteria, staged quality control and delivery in your pipeline's format. Classification, detection, segmentation, video and text.

Request a sample assessment

Send a sample or your guidelines. We'll come back with an effort estimate.

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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.

Computer vision and labeling

We annotate knowing what the model will do with the data

FexData also builds computer vision systems for manufacturing. That changes how we define classes, handle ambiguity and design review.

  • Criteria drawn from operations

    We know which class ambiguities break a model in production, because we keep models in production.

  • Review designed for training

    Validation is built around what the model needs to learn, not only around compliance with the guideline.

Explore computer vision solutions

Frequently asked questions about data labeling

What ML teams usually ask before sending the first batch.

Which annotation types do you support?

Classification, bounding boxes, polygons, semantic and instance segmentation, keypoints, video tracking and text annotation. The choice depends on what the model needs to learn.

Does FexData help create guidelines?

Yes. When a project has no written criteria yet, we build the guidelines with you from a first sample and validate them before scaling volume.

How is quality controlled?

Annotation, review and sampling audit, with explicit handling of disagreements. No batch is released without human review.

Can you review an existing dataset?

Yes. We audit the dataset, measure inter-annotator agreement and point out where criteria diverge, before proposing correction or re-annotation.

Which formats can you export?

We deliver in your pipeline's format — COCO, YOLO, Pascal VOC, masks or a custom schema — with versioning on every delivery.

How is confidential data handled?

Access controlled per project, data encrypted in transit and at rest, processes aligned with GDPR and LGPD, and an NDA when needed.

Can we run a pilot batch?

Yes, and it's the recommended start. A pilot batch validates guidelines, pace and quality criteria before any volume commitment.

Keep reading

Annotation formats and the full labeling offer.

Start with a sample

Send a small batch or your guidelines. We'll return an effort assessment and an execution plan — before any full engagement.

Request a sample assessment

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

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Let's understand your challenge

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