Data-HubAnnotation & Training Data
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Training data, labelled by people

Ground-truth data your models can trust.

Data-Hub turns raw imagery, video and text into precise, production-ready training data — for computer vision, satellite & geospatial systems, and LLM & language models. Domain experts across medicine, biology, engineering, finance, software development and more — covering 10+ languages in-house. A track record with AI labs. Quality measured at every batch.

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98%+
Batch accuracy target
Days
To first pilot batch
3
Continents served
Where German precision meets AI training data.
High-quality annotation, evaluation, and data operations at scale.
What we label

Annotation built around your domain, not a generic queue.

Every project gets a dedicated lead, a domain-matched team, and a label taxonomy agreed before the first box is drawn. Four areas where we go deep.

CV

Computer vision

Bounding boxes, polygons, semantic & instance segmentation and keypoints for detection, tracking and pose — across street scenes, retail, manufacturing and inspection.

  • bbox
  • segmentation
  • keypoints
  • polylines
  • OCR
GEO

Satellite & geospatial

Overhead and aerial imagery labelled for GEOINT and EO pipelines: building footprints, land cover, roads, vessels and change detection — geometry that holds up at scale.

  • footprints
  • land cover
  • rotated bbox
  • change detection
LLM

LLM & language

Instruction & preference data (RLHF/DPO), prompt–response evaluation, red-teaming, dynamic pricing validation, HTML parsing corrections and multilingual text work — reviewed by domain experts across medicine, biology, engineering, finance and software development — covering 10+ languages in-house.

  • SFT & preference
  • evaluation
  • red-teaming
  • NER & classification
  • RAG sets
VID

Video & sequences

Frame-by-frame object tracking, action recognition and event boundaries with temporal consistency — for autonomous systems, security and behaviour analysis.

  • object tracking
  • action recognition
  • interpolation
  • events
Annotation types

The full toolkit, one delivery standard.

01 / bbox

Bounding boxes

Tight 2D boxes for object detection and localisation across images and video frames.

02 / poly

Polygons

Precise outlines for irregular shapes where a box wastes context.

03 / seg

Segmentation

Pixel-level semantic and instance masks for dense scene understanding.

04 / kp

Keypoints

Landmarks and joints for pose, gesture and structural estimation.

05 / line

Polylines

Lanes, paths and boundaries for routing and lane-detection models.

06 / cuboid

3D cuboids

Depth-aware boxes capturing orientation and volume for spatial models.

07 / track

Object tracking

Persistent IDs across frames with motion and trajectory continuity.

08 / ocr

Text & OCR

Scene text regions and transcription for signage, documents and overlays.

Beyond annotation

Data analytics & data science, when the labels aren't the whole job.

Some projects need more than ground truth. We also help teams make sense of the data around it — and stand up the models that use it.

ANALYTICS

Data analytics

Cleaning and structuring messy datasets, exploratory analysis, metrics and reporting — turning raw operational data into something you can actually read and act on.

  • data cleaning
  • EDA
  • dashboards
  • reporting
SCIENCE

Data science

Feature engineering, model prototyping, evaluation and error analysis — pragmatic support to get a model from promising to production, with the domain context to ask the right questions.

  • feature eng.
  • prototyping
  • evaluation
  • error analysis
Why Data-Hub

Annotation is a quality problem. We treat it like one.

A 5% error in a static image is noise. A 5% error in a driving scene is a crash. Our model is built to keep error out of your pipeline — not push volume through it.

Domain experts, not a generic crowd

Reviewers with real backgrounds — doctors, biologists, engineers, financial experts, software developers and more — supporting AI labs on both LLM and computer-vision projects in 10+ languages. The right eyes on your data, briefed on your taxonomy and edge cases.

Multi-stage QA with IAA tracking

Every batch passes human review. We track inter-annotator agreement and IoU, and resolve inconsistencies before data reaches training.

EU lead, dedicated delivery

A Vienna-based project lead owns scope, communication and acceptance; a managed delivery team handles throughput. One point of contact, one standard.

GDPR & EU AI Act aware

Clear data handling, signed confidentiality, and access controls scoped to your project — built for European compliance expectations from day one.

How it works

From brief to production-ready data in six steps.

A clear, controlled workflow. You stay in the loop at every milestone; nothing moves forward without passing review.

STEP 01

NDA & first contact

We sign a mutual NDA upfront — your data and guidelines stay confidential from day one.

STEP 02

Kickoff & scope

You share raw data and quality standards. We define methodology, taxonomy and assign a dedicated lead.

STEP 03

Pilot & estimate

We annotate a representative sample and return a clear estimate by complexity, hours and review rounds.

STEP 04

Agreement & setup

Scope, quality thresholds and deadlines fixed in writing. We configure the right platform — CVAT, Labelbox, SuperAnnotate or your own.

STEP 05

Annotation & QA

Trained teams label; every batch passes human review with agreement tracked throughout.

STEP 06

Delivery

Clean, validated data in your format — COCO, Pascal VOC, JSON, PCD or custom — with a full quality report.

Selected work

Where the labels earned their keep.

All case studies →
Drone-based aircraft surface inspection
Computer vision · Aviation

Drone-based aircraft surface inspection

Close-range drone imagery of aircraft fuselages annotated for surface cracks, corrosion, dents and paint damage — enabling predictive maintenance models that reduce manual inspection time by 80%.

30k+
Images labelled
5
Damage classes
22
Sub-classes
Building footprints from satellite tiles
Geospatial · Earth observation

Building footprints from satellite tiles

Polygon and land-cover annotation across multi-resolution overhead imagery — geometry consistent enough to train a footprint-extraction model for change detection.

90k+
Images labelled
3
Sensor sources
8
Land-cover classes
Expert LLM evaluation interface
LLM & language · Medical AI

Expert evaluation & instruction data for an LLM

Reviewers with backgrounds across medicine, biology, engineering, finance and software development built preference data and graded model responses against a clinical rubric — instruction and evaluation sets an AI lab could trust on high-stakes medical prompts.

20k+
Responses graded
3
Expert domains
98%+
Batch accuracy target
7
Export formats supported
3
Continents served
24h
Reply to a new brief
In their words

What clients say after the first batch.

We are very pleased with the service provided by Data-Hub Sholudchenko (DHS), and wish to recommend it further. DHS became a very important pillar for our company when it comes to training data preparation for our machine learning algorithms. We have done already several projects in image labelling and manual information extraction from documents in order to prepare ground truths for our models. The team from DHS works very efficiently and the quality has always been outstanding. I can definitely recommend to work with DHS and we are looking forward to continue our collaboration!
Dr. Wolfgang A. Brunauer Dr. Wolfgang A. BrunauerCEO · DataScience Service GmbH (Austria)
As we all know, dataset quality is critical for AI. As we developed various AI models, we tried labeling in-house and worked with various outsourcers, but the quality was very poor and the labelers were not easy to manage in-house. So when we were building a new dataset, we were afraid to use an outsourcer. But after meeting Issac from DataHub, we didn’t hesitate to work with DataHub for any of our labeling needs. Open communication, accountability, understanding of the dataset and domain, and detailed and meticulous data labeling. After working with Datahub, I wouldn’t hesitate to recommend them to anyone thinking about outsourcing their data labeling. Datahub is playing a huge role in advancing AI around the world.
Seowoo Han Seowoo HanCTO · AI company (Republic of Korea)
Start small, scale on proof

Run a pilot batch before you commit.

Send a sample of your data and your quality bar. We'll label a representative set, share the results and a transparent estimate — no commitment.

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