Automate repetitive tasks
Hand routine processes to AI to improve accuracy, reduce errors and keep quality consistent, so your team can focus on strategy.
AI development · Models to production
AI Development Services: models designed, trained and deployed
We design, train and deploy AI models that automate work, predict outcomes and support decisions. Our engineers and data scientists, many with PhDs, take a model from data preparation to production and keep refining it as your business changes.
- Models built for your specific business problem
- Trained on quality data and tested in real scenarios
- Deployed without disrupting your operations
- 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 AI development delivers
We build models where they change results: lower costs, fewer surprises and faster decisions.
Anticipate risks
Models that find patterns and anomalies in unstructured data flag potential threats early, so you can act before issues escalate.
Decide in real time
AI evaluates incoming data continuously and produces insights, trends and predictions your managers can act on straight away.
What we deliver
Our AI development capabilities
From a single model to a complete AI application, including the work that keeps models accurate after launch.
- 01
Custom AI model development
Models tailored to your business challenge, from predictive analytics to computer vision and natural language processing.
- 02
AI software development
Complete AI-powered software, including analytics tools, virtual assistants and intelligent automation systems.
- 03
Generative AI solutions
Generative AI development, including custom large language models and content generation tools.
- 04
AI integration and deployment
We integrate models into your existing systems and support them after deployment, without disrupting operations.
- 05
AI training and optimisation
Models need continuous learning. We train them on high-quality data, refine the algorithms and adapt them as your needs change.
- 06
Data engineering and preparation
We collect, clean and structure your data so the models receive high-quality inputs.
- 07
Edge AI development
Models optimised to make real-time decisions on devices with limited computing resources.
- 08
AI ethics, governance and security
Frameworks for responsible AI use, compliance and bias mitigation, and security measures that protect your models and data.
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
Custom LLM chatbot with document references for a consulting firm
In-house LLM no reliance on third-party LLM services
A private, hosted chatbot built on a custom large language model, giving a consulting firm answers with source references while keeping full control over its data.
Credit scoring model for an SME lending company
800+ features from 14 data sources
A machine learning model that predicts borrower default and fully automates underwriting, improving loan portfolio quality and cutting decision time from hours to a fraction of a minute.
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.
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.
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. Ivan Tankoyeu CEO 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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Dr. Sergey Sukhanov Advisor PhD in Machine Learning, data scientist since 2013. Publications, patents and 30+ implemented AI use cases; several IEEE awards.
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 delivered an extra module that we were not expecting but turned out to be very useful.”
Research and custom software development applying generative adversarial networks for a pharmaceutical company with 5,000+ employees.
“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 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
How we apply AI development across industries
Examples of the models and applications we develop for each sector.
Insurance
Models that optimise claims processing, fraud detection and risk assessment.
- Fraud detection and risk mitigation
- Automated claims processing
- Customer behaviour analysis
- Personalised policy recommendations
Finance
Better security, customer service and regulatory compliance for financial institutions.
- Fraud prevention
- Credit risk assessment
- Personalised investment strategies
- Automated financial reporting
Pharmaceuticals
Support for drug discovery, patient care and medical research.
- Drug development
- Personalised treatment plans
- AI-assisted clinical trials
- Predictive analytics for healthcare trends
Construction
AI for planning, safety and project management.
- Smart project scheduling
- Risk assessment
- Construction site monitoring
- Automated material inspection
Real Estate
Streamlined property management, client engagement and pricing.
- Property valuation
- Smart lead generation
- Automated real estate transactions
- Personalised property recommendations
Oil and Gas
Optimised exploration, production and maintenance.
- Predictive maintenance
- Drone-based pipeline inspection
- Intelligent resource allocation
- Geological data analysis
Media
Content generation, personalisation and analytics.
- Content curation
- Sentiment analysis for media insights
- Automated video and image tagging
- Personalised audience engagement
Government
Efficiency, transparency and better public services.
- Policy analysis
- Intelligent document processing
- Chatbots for public inquiries
- Predictive analytics for governance
Tech Start-ups
AI that helps growing companies scale and compete.
- Product recommendations
- Customer support automation
- Scalable AI architectures
- User behaviour analysis
Space
AI for space exploration, satellite monitoring and mission planning.
- Satellite imaging analysis
- Spacecraft safety and diagnostics
- Predictive analytics for missions
- Automated anomaly detection
NGOs and Non-profits
Tools for social impact, operational efficiency and compliance.
- Grant application screening
- Automated impact assessment
- Public engagement strategies
- Data-driven decision frameworks
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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Top Artificial Intelligence Company Germany 2026 · Clutch -
Top Machine Learning Company Germany 2026 · Clutch -
Top Computer Vision Company Germany 2026 · Clutch -
Top Generative AI Company Germany 2026 · Clutch -
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
What does the AI development process look like?
Discovery defines goals and checks feasibility. We then collect and pre-process data and select models and frameworks. An MVP tests the model in real-world scenarios and is refined on feedback and performance metrics. Once it proves effective, we scale and integrate it, then monitor and fine-tune it continuously.
Do we need a large dataset?
Models need quality data, but the amount depends on the application. Some models work with limited data through transfer learning; others need extensive data to reach the required accuracy. We assess your data during discovery.
What are the main risks in AI development, and how do you handle them?
The typical challenges are data quality, model bias, ethical concerns and integration complexity. We address them with data governance, thorough testing and ongoing monitoring of models in production.
Can the models be tailored to our business?
Yes. We tailor every model to your industry, processes and goals, and integrate it with your existing systems.
How long does AI development take?
It depends on project complexity, data availability and integration requirements. Projects take from weeks to several months, depending on scope and objectives.
How is an AI development project priced?
Cost depends on complexity, the degree of customisation and the scale of deployment. We offer flexible pricing models and scope each stage before it starts, so you know what the next step involves.
How do we get started?
Start with an assessment of your business needs. Our experts then guide you through strategy, model selection and implementation.
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.