Better predictive modelling
Machine learning on GIS data makes more accurate forecasts possible for urban and environmental planning.
GeoAI · Geospatial analysis
GeoAI Services from geospatial data to decisions
We combine machine learning and deep learning with GIS to analyse satellite imagery, aerial photography, LiDAR, GPS and sensor data. The result is spatial patterns, predictions and maps your teams can act on, for urban planning, environmental monitoring, logistics, insurance and more.
- Objects and areas detected in satellite and aerial imagery
- Forecasts built on historical geospatial data
- AI added to the GIS frameworks you already use
- 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
What GeoAI adds to GIS
GIS provides large spatial data sets; AI turns them into patterns, predictions and faster decisions.
Large-scale data processing
Satellite imagery, sensor data and other large geospatial data sets are processed and analysed far more efficiently.
Real-time insight
Geospatial data interpreted as it arrives supports time-critical decisions such as traffic management and disaster response.
What we deliver
Our GeoAI capabilities
From analysing complex geospatial data sets to deploying custom models, we help you find spatial patterns, connections and trends and act on them.
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Advanced spatial data analysis
Detection of clusters, patterns and trends in complex geospatial data sets to support resource planning and risk management.
- 02
Predictive modelling and forecasting
Models trained on historical geospatial data that predict changes and support strategic, proactive planning.
- 03
Real-time data processing
Interpretation of geospatial data on the fly to shorten response times in situations such as dynamic traffic control and disaster management.
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GIS and AI integration
AI integrated with your existing GIS frameworks, adding decision support and richer spatial data interpretation.
- 05
Remote sensing analysis
Interpretation of remotely sensed data for environmental monitoring and agriculture, from cloud monitoring for weather prediction to forest and crop growth.
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Data visualisation and mapping
Raw geospatial data turned into visualisations, maps and geographical models that make complex information usable for stakeholders.
- 07
Machine learning and deep learning integration
Pattern recognition, predictive analysis and anomaly detection applied to geospatial data.
- 08
Pre-trained detection models
Ready models that detect, localise and segment common objects such as trees, buildings and vehicles, so you do not start from scratch.
- 09
Custom GeoAI solutions
Applications built around industry-specific challenges, including models trained from scratch to detect the specific objects you need.
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
Space · Computer Vision
Object detection in hyperspectral satellite imagery
10 x 8 pxsmallest object size recognised
An environmental monitoring solution, developed as part of our social responsibility programme, that detects small objects in hyperspectral satellite imagery with very high accuracy.
Read the case study
Roof detection and segmentation for a solar energy company
Automated roof detection and roof plane segmentation
Deep learning models that detect residential roofs in satellite images, segment them into distinct planes and estimate the area and dimensions of each, so solar panel installations can be planned efficiently.
Map segmentation for urban zone pricing on a real estate platform
5 classes of land use mapped pixel by pixel
A deep learning system that segments city maps, compares them with historical maps and tracks open land over time to support price assessment of urban zones.
Land degradation and conflict prevalence research for an NGO
74% accuracy predicting conflict occurrence 6 months ahead
Data science research with the humanitarian NGO World Vision, analysing satellite vegetation data (NDVI) and historical conflict records to study how land degradation relates to conflict prevalence.
Pothole detection and road damage assessment for a public body
Automated pothole detection, severity scoring and mapping
A deep learning platform that detects and evaluates potholes and road damage on different types of roads with minimal human intervention, and maps them for maintenance teams.
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, is the hard part, and it needs a different kind of team.
01
A research-led team
AI Superior was founded in 2019 by AI researchers with published papers and patents, and the whole team works to that research standard.
- 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 only goes into approaches that have worked 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 by one team, so nothing gets lost in hand-overs between 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
“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.
“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.
“I appreciate their approach, expertise, and the quality of information they provide.”
AI and machine learning consulting to identify the right use cases for a web design and graphics company.
How we work
A staged AI project life cycle
Every stage ends with a result you can check, and you decide on the next one only after seeing the previous one work. You keep control of scope and budget.
- 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
We review the results with you and make sure they are read correctly.
You get: What the solution delivered and where to improve next
Use cases by industry
GeoAI applications by industry
From crop yields to response times, geospatial AI supports decisions in many sectors.
Urban Planning and Smart Cities
Data-driven insight into urban growth patterns for better resource allocation and infrastructure development.
- Land use optimisation
- Public services planning
- Transportation network optimisation
Environmental Monitoring
Tracking changes in ecosystems and environmental trends, and evaluating the potential impact of decisions.
- Natural resource management
- Conservation
- Pollution control
Agriculture and Land Management
Insight into soil health and crop condition to use land to its full potential.
- Crop disease monitoring
- Precision farming recommendations
- Planting and watering timing
Disaster Response and Management
Real-time analysis for fast, informed decisions in a crisis.
- Damage assessment
- Disaster impact prediction
- Automated mapping of affected areas
Transportation and Logistics
Better fleet management, demand prediction and delivery routes, with lower fuel consumption.
- Traffic detection
- Shipment tracking
- Route planning for heavy vehicles
- Supply chain mapping
Public Health and Epidemiology
Spatial analysis of how diseases spread, supporting more proactive healthcare measures.
- Disease tracking
- Predictive modelling for epidemiological studies
- Resource allocation
Energy and Resource Management
Insight into consumption patterns and resource distribution for more sustainable practices.
- Energy grid optimisation
- Renewable energy potential
- Efficient resource use
Retail and Business Analytics
Location-based insight into customer demographics and preferences.
- Consumer behaviour analysis
- Inventory and product placement
- Store location optimisation
Real Estate and Property Management
Data-driven insight for urban development, market analysis and property valuation.
- Property valuation
- Market analysis
- Zoning and infrastructure decisions
Insurance and Risk Assessment
Detailed insight into geographical risks to support policy and pricing decisions.
- Spatial risk assessment and mitigation
- Faster claims processing
- Policy optimisation
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
How does GeoAI differ from traditional GIS?
GIS provides the foundation for analysing spatial data. GeoAI adds machine learning and deep learning on top, bringing predictive modelling, better pattern recognition and automated decision support.
What types of data does GeoAI use?
Satellite data and imagery, aerial photography, LiDAR point clouds, GPS and GIS data, sensor data, text and video. Combining sources gives a more complete view of geographical patterns, relationships and trends.
Do we need to train a model from scratch?
Not always. We have pre-trained models for common requests such as trees, buildings and vehicles that detect, localise and segment objects in your data. If you need to detect something specific, we train a model for it.
Can GeoAI work with our existing GIS?
Yes. We integrate AI with your existing GIS frameworks, which extends your spatial analysis and decision support rather than replacing the tools your teams use.
Is GeoAI reliable enough for business decisions?
Real-time processing, predictive modelling and validated models make it a sound basis for decisions. Before deployment we test the solution thoroughly and train it on your data to ensure accurate results.
How does a GeoAI project run?
We start with a consultation and needs assessment, then plan milestones, scope and a roadmap. After development we test, validate and train the solution on your data, deploy it to your environment, and monitor and support it as your needs evolve.
What does implementing GeoAI cost?
It depends on the scope, complexity and level of customisation. Every project is different, so we give you an estimate once we understand your data and requirements.
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