Automate and decide on data
Automate processes, analyse large data sets and turn them into insights, from recommendation engines to fraud detection and NLP applications.
AI & machine learning · Models in production
AI & ML Development Services: models that turn your data into decisions
We build machine learning and deep learning models on your data: predictive analytics, NLP, computer vision and recommendation systems. We handle the full model life cycle, from data engineering and training to deployment, monitoring and retraining, so the models stay accurate after they go live.
- Predictions and insights from your own data
- Secure data pipelines and responsible AI governance
- Models monitored and optimised after deployment
- 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 and ML change in your operations
Machine learning pays off when it is accurate, secure and fast enough for daily use.
Accurate, private and compliant
Model reliability, data privacy and ethical AI built in, with encryption, secure data pipelines and responsible governance for sensitive information.
Performance at scale
Optimised algorithms, cloud AI platforms and real-time data processing keep models accurate and responsive as your business grows.
What we deliver
Our AI & ML development capabilities
The model types we develop, and the engineering that keeps them working in production.
- 01
Machine learning model development
We create, train and deploy models for real-time data analysis, automation and optimisation.
- 02
Deep learning solutions
Neural networks for advanced pattern recognition, image processing and predictive analytics.
- 03
Natural language processing
Chatbots, sentiment analysis, text summarisation and voice recognition.
- 04
Computer vision applications
Image and video recognition for object detection and tracking, automated classification and monitoring.
- 05
Predictive analytics and forecasting
Models that anticipate market trends and support better decisions and strategy.
- 06
Model training and optimisation
We fine-tune models for accuracy, efficiency and scalability.
- 07
Data engineering and data architecture
We design and build data architectures and integrate structured and unstructured data, so models get reliable inputs.
- 08
Deployment and maintenance
We deploy models into real-world applications with continuous monitoring and updates.
- 09
Edge AI development
Models optimised for real-time processing on edge devices, for speed and efficiency.
- 10
Security, compliance and governance
Security frameworks that protect data and support regulatory compliance, and governance focused on transparency, fairness and accountability.
Technologies we work with
- TensorFlow
- PyTorch
- OpenAI
- AWS
- Google Cloud
- Microsoft Azure
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
Finance · Machine Learning
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.
Read the case study
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.
Player churn prediction for an online gaming platform
11.3% churn rate after new retention strategies
A machine learning model that learns player behaviour during the game and predicts the probability of churn over a given time horizon, so the platform can apply the most relevant retention strategy.
Medical image translation between tissue stains for a pharma company
Reused one disease classifier across stain reagents
An AI component that translates histological images from one stain domain to another, so existing software and machine learning models can be reused across all reagents.
Telematics-based driver scoring for usage-based insurance
Per trip driving score and personal discount
A deep learning model that analyses telematic data from drivers’ phones to detect driving behaviour, score each trip and calculate personalised discounts and safe-driving recommendations.
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
“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.
“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.
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
AI and ML across industries
Examples of the models we develop for each sector.
Insurance
Automated risk assessment, fraud detection and claims processing.
- Fraud detection algorithms
- Claims processing with machine learning
- Risk assessment and policy pricing models
Construction
Site monitoring, safety compliance and predictive maintenance.
- Predictive maintenance for equipment
- Real-time safety monitoring
- Project scheduling and risk management
Finance
Fraud detection, investment analysis and customer service automation.
- Fraud and anomaly detection models
- Risk assessment for lending and investments
- Portfolio optimisation tools
Government
Automation, better public services and data-driven policy decisions.
- Document processing for public administration
- Predictive analytics for strategic planning
- Citizen service chatbots
Tech Start-ups
Automation, personalised recommendations and business intelligence.
- Customer behaviour analytics
- Chatbots for user engagement
- Personalisation engines for platforms and apps
Media
Content recommendations, automated editing and audience sentiment.
- Personalised content recommendation engines
- Video and image enhancement
- Audience sentiment analysis
Oil and Gas
Resource exploration, predictive maintenance and operational risk management.
- Seismic data analysis for exploration
- Predictive maintenance for drilling and pipelines
- Operational risk assessment
Pharmaceuticals
Drug discovery, predictive diagnostics and personalised treatment.
- Clinical trial analytics and monitoring
- Personalised treatment recommendation engines
- Models for disease diagnosis and progression
Real Estate
Valuation, predictive analytics and property recommendations.
- Dynamic property pricing and valuation models
- Property search and recommendation engines
- Contract and document processing
Space
Satellite data processing and mission optimisation.
- Satellite imaging and remote sensing analysis
- Mission diagnostics and risk assessment
- Spacecraft navigation and anomaly detection
NGOs and Non-profits
Automation, policy research and data analysis.
- Impact assessment and monitoring
- Fundraising and donor engagement platforms
- Analytics for social policy recommendations
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
What does AI & ML development involve?
Creating applications and models that use machine learning, deep learning and automation to improve efficiency and decision-making. In practice that means preparing data, selecting and training models, integrating them into your systems and keeping them accurate in production.
Why invest in AI and machine learning?
AI and ML automate processes, generate insights from data you already hold and improve customer interactions, which leads to higher efficiency and growth.
How do you run an AI & ML project?
Discovery defines objectives and scope and identifies the right ML opportunities. We then collect and pre-process data, select models and set up pipelines, and build an MVP to validate the approach. Once validated, we scale and integrate the solution, then monitor model performance and optimise it for long-term impact.
How do you keep models accurate after deployment?
Deployed models are monitored continuously. We update and retrain them, optimise the algorithms and improve predictive accuracy as your data and business change.
How long does AI & ML development take?
It depends on complexity, but projects typically take from several weeks to a few months.
How much does AI & ML development cost?
It depends on scope, requirements and complexity, as well as customisation, data availability and integration needs. We cannot give a figure before we understand the project, but we use a transparent pricing model and scope the project with you before we quote.
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