Higher model accuracy
Precisely labelled data sets help models recognise patterns reliably in tasks such as image recognition, sentiment analysis and speech-to-text.
Data annotation · Training data for AI
AI Data Annotation Services for training data your models can trust
A model is only as accurate as the data it learns from. We label images, video, text, audio and 3D point clouds to the specification of your model, combining AI-assisted tools with human review and quality control, and deliver data sets that plug straight into your machine learning pipeline.
- Precisely labelled image, text, audio and 3D data
- AI-assisted labelling with human review
- Annotation designed around your model
- 57% of our team hold a PhD
- German company, working under the GDPR
Discuss your project
5.0 18 reviews on Clutch Go Global Awards winner
Trusted by enterprises, scale-ups and non-profits
What you gain
Why annotation quality decides model quality
Poor labels lead to inaccurate outputs. We treat annotation as part of model development, not a separate chore.
Consistency at scale
AI-assisted tools, human expertise and quality control keep labels precise and consistent, even across large data sets.
Labels built for your use case
From image classification to named entity recognition and video frame annotation, the labelling scheme follows your model's requirements.
What we deliver
Our data annotation capabilities
Annotation for every major data type, with the quality control to back it.
- 01
Image annotation for computer vision
Bounding boxes, polygons, keypoints and pixel-precise semantic segmentation for object detection and image recognition.
- 02
Video annotation for object tracking
Frame-level annotation that teaches models to track objects, actions and behavioural patterns.
- 03
Text annotation for NLP
Named entity recognition, sentiment analysis, intent classification and text categorisation for search, chatbots and analytics.
- 04
Audio and speech annotation
Phonetic and language-specific transcription, speaker identification, emotion detection and noise classification for voice recognition.
- 05
3D point cloud and LiDAR labelling
Structured annotation of LiDAR and 3D sensor data for autonomous systems, drones and geospatial models.
- 06
Multi-class and multi-label annotation
Complex labelling schemes for models that need several categories per item.
- 07
Quality control and data verification
Multi-layered checks: AI-powered validation, human review and automated consistency checks.
- 08
Custom annotation pipelines
Annotation workflows and guidelines designed around your model, your industry and your development pipeline.
- 09
Bias-aware annotation
Annotation processes designed to reduce bias and support fair model training.
- 10
Scalable annotation infrastructure
Cloud-based annotation platforms and automation tools for high-volume training projects.
Technologies we work with
- PyTorch
- TensorFlow
- PySpark
- Hadoop
Not sure which of these you need? Describe the problem and we will recommend the approach.
Discuss your projectCase studies
All case studiesResults we delivered for our clients
Healthcare · Computer Vision, Deep Learning, Object Detection
Pill detection and counting for a healthcare technology provider
99.9%precision, recall and F1 across all predictions
A computer vision system that identifies, classifies and counts pharmaceutical pills by National Drug Code (NDC) from photos, replacing error-prone manual counting.
Read the case study
Drone-based litter detection and collection planning
25x faster litter detection, with overall costs halved
A computer vision system that detects litter in drone images across roughly one thousand square kilometres of coastline, with a GIS application for planning and tracking collection.
Real-time graffiti detection and mapping for a municipality
Real-time graffiti detection with GPS location
A deep learning system that detects and locates graffiti across a city in real time, so cleaning teams can find, prioritise and remove it faster.
Eye fat and muscle volume estimation from MRI scans
Automated fat and muscle segmentation in every MRI slice
A deep learning model for an ophthalmology centre that segments fat and muscle tissue in MRI orbit scans, estimates their volume and supports before-and-after comparisons.
Automated information extraction from invoices
25x faster invoice processing
A web service that extracts data from different types of invoices, automating data entry for a multinational equipment distributor and saving 850 man-hours per month.
Why AI Superior
Research-grade AI, delivered like a product company
Many vendors can build a demo. Making AI work reliably on real data, inside real business processes, takes a different kind of team. This is what you get with us.
01
Scientists, not generalists
AI Superior was founded in 2019 by AI researchers with published papers and patents. That depth runs through the whole team.
- PhD57%
- MSc28%
- BSc15%
02
Built to reach production
Every project is de-risked in stages, with a go/no-go decision at each milestone, so budget goes only into solutions proven on your data.
4× our proof-of-concept success rate compared with the market average
03
A complete team from day one
Strategy, models and software are designed together, not handed over between separate vendors.
- Product AI product owner, business analyst, project manager
- Data Data scientists, ML engineers, BI analyst
- Development Software engineers, QA, DevOps
04
Your data stays under control
When data must not leave your organisation, we build on private, self-hosted models instead of third-party AI services.
- GDPR, German company
- Self-hosted LLMs
- Transparent methods
Our team
The experts behind your project
Your project is led by senior data scientists and engineers with research backgrounds, supported by our product and development teams.
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Dr. Sergey Sukhanov Chief Data Scientist PhD in Machine Learning, data scientist since 2013. Publications, patents and 30+ implemented AI use cases; several IEEE awards.
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Dr. Ivan Tankoyeu Chief Data Officer PhD in Computer Science. Recognised by the IEEE GRSS Society, winner of the Global AI Hackathon 2017, Kaggle TOP500 data scientist (2014).
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Sergio Frayle, M.Sc. Lead Data Scientist Master’s in AI, Pattern Recognition and Digital Imaging; 7+ years in image processing and computer vision, with publications.
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Enrique Fernández, M.Sc. Senior ML Expert Master’s in Robotics and Mechatronics; builds and deploys complex AI solutions, particularly in computer vision and NLP.
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Peter Luck, M.Sc. AI Consultant Master’s in Business Engineering and Computer Science; product and project manager for machine-learning products.
Client reviews
5.0 Rated 5.0 by the people we build for
18 verified reviews on Clutch
“They successfully fulfilled every component of the project and exceeded our expectations.”
Built an AI model that analyzes the roofs of residential and commercial properties, including data collection and labeling. All key deliverables were completed on time.
“AI Superior responded to all of our needs very carefully and considered them in the project.”
Built AI models and a dashboard for a pilot program that detects waste in drone imagery.
“AI Superior showed a strong commitment to delivering high-quality solutions to us.”
Improves and maintains the data infrastructure of a credit solutions company: data pipelines, ML models and custom reports.
How we work
A proven AI project life cycle
Every stage ends with a result you can check. You never commit to the next stage before seeing the previous one work, so scope, budget and risk stay under your control.
- Estimate before you commitYou see scope and expected results before the build begins.
- Go/no-go after every stageEach stage ends with a result you can check and a decision on the next step.
- Risks reported openlyWe share risks and opportunities as soon as the analysis shows them.
- Go / no-go decision
Discovery
We work through the business problem with your team and define the direction of the solution.
You get: Scope, approach and a high-level estimate of effort and expected results
- Go / no-go decision
Data and feasibility
We get to know your team and data and check whether AI is the right tool for this problem.
You get: A data assessment and a clear feasibility verdict before any build starts
- Go / no-go decision
Proof of concept / MVP
We start small, using the data already available, to test the solution in practice.
You get: Measured results on your own data and a basis for the investment decision
- Go / no-go decision
Integration and scaling
We integrate the solution into your existing systems, fine-tune the models and adjust them where needed.
You get: A solution running inside your processes, compatible with your data and systems
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Evaluation
Together we evaluate the results of the implementation and make sure they are interpreted correctly.
You get: A clear picture of the value delivered and where to improve next
Use cases by industry
Data annotation across industries
Examples of the labelled data sets we prepare for models in each sector.
Insurance
Labelled data for fraud detection, claims automation and risk assessment.
- Annotated documents for claims processing
- Fraud and risk data sets
- Policy document classification and entity recognition
Finance
Precise annotation for fraud detection, investment analysis and customer profiling.
- Labelled transaction data for anomaly detection
- Annotated financial statements
- Compliance document processing
Pharmaceuticals
Labelled medical data sets for drug discovery and patient monitoring.
- Annotated medical records
- Labelled clinical trial data
- Diagnostic training data sets
Construction
Labelled data for site safety, predictive maintenance and planning.
- Labelled drone imagery for site monitoring
- Annotated safety and compliance documents
- Risk data for predictive maintenance
Real Estate
Annotated data for valuation, search and document processing.
- Annotated listings for pricing models
- Labelled legal documents for contract analysis
- Image labelling for property features
Oil and Gas
Annotated geospatial and sensor data for exploration and operations.
- Annotated seismic images
- Labelled maintenance logs
- Safety report annotation
Media
Annotation for content moderation, sentiment and recommendations.
- Labelled video and image data for moderation
- Sentiment data for trend tracking
- Recommendation training data
Government
Labelled data for document automation and citizen services.
- Labelled public records
- Policy research and sentiment data sets
- Citizen enquiry data for chatbots
Tech Start-ups
Training data for support automation, analytics and personalisation.
- Chatbot training data sets
- Annotated customer behaviour data
- Content categorisation data
Space
Annotation for satellite imaging and anomaly detection.
- Labelled satellite images for geospatial analysis
- Anomaly detection data sets
NGOs and Non-profits
Annotated data for impact assessment and donor engagement.
- Annotated survey data for impact measurement
- Labelled donation records
Awards and recognition
Ranked among the top AI companies
Recognised by international business awards and by independent B2B platforms that rank companies on verified client reviews.
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Go Global Awards Winner 2021 · International Trade Council -
Best Data Science & AI Service Provider, Europe 2021 · German Business Awards -
Top Artificial Intelligence Company 2023 · Clutch -
Top Machine Learning Company 2023 · Clutch -
Clutch Champion Fall 2023 · Clutch -
Clutch Global Fall 2023 · Clutch -
Top BI & Big Data Company Germany 2023 · Clutch -
Top IT Services Company Germany 2023 · Clutch -
Top Artificial Intelligence Companies 2023 · TrueFirms -
Top Machine Learning Companies 2021 · Techreviewer -
Most Reviewed IT Services Companies Germany · The Manifest
Why does annotation quality matter so much?
Models learn patterns from labelled examples. Inaccurate or inconsistent labels lead to inaccurate outputs, which reduces efficiency and increases risk once the model is in use.
What types of data can you annotate?
Images, video, text, audio and 3D point cloud data, including LiDAR. Typical tasks are object detection, segmentation, entity recognition, sentiment analysis, transcription and speaker identification.
How does AI-assisted annotation work?
AI-powered tools pre-label the data, and human annotators refine and validate the labels. This speeds up labelling while keeping accuracy high.
How do you ensure annotation accuracy?
Through multi-layered quality control: AI-powered validation, human review and automated consistency checks. We also annotate a sample data set first to validate quality and refine the workflow before scaling up.
Can annotation be tailored to our model?
Yes. We define annotation goals and guidelines from your model's requirements, so the labelled data matches its learning objectives and integrates with your machine learning pipeline.
Can you handle large data sets?
Yes. We use cloud-based annotation platforms and automation tools to process high volumes of data with consistent quality.
Do we have to collect and prepare the data ourselves?
No. We can help gather and organise raw data, clean it of errors and inconsistencies, and pre-process it before annotation and model development.
Start your project
Tell us what you want AI to do for your business
Share a few details and our AI team will take it from there. Here is what happens next:
- We review your request and reply by email.
- A call with an AI expert to understand your problem, data and goals.
- A clear recommendation: the approach we suggest and a high-level estimate.
Prefer to pick a time yourself?
Schedule a call