Better products and features
Enhancing the features, functions and performance of products is the main goal companies pursue with AI.
Technology · Advanced AI
Cutting Edge Artificial Intelligence: deep learning, reinforcement learning and GANs
Many companies know what they want from AI but lack the data science talent to get there. Our team keeps working with the latest AI methods and applies them where they add value: in products, processes and research projects for our clients.
- Deep learning on the labelled data you already have
- Models that learn decisions by reward and penalty
- Generative models that create new, realistic content
- 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 advanced AI makes possible
The use cases range from process automation and smart email to conversational AI, chatbots and personal assistants.
Insights from more data
Deep learning handles more data, larger models and more computation, which leads to better algorithms and new insights.
Expertise you do not have to hire
A shortage of data science talent is a common barrier to AI. We bring the specialists who master these methods.
What we deliver
Methods we work with
Some of the advanced AI approaches our team has been applying recently.
- 01
Deep learning
Neural networks modelled loosely on the brain. Particularly strong at supervised learning from labelled data, and flexible and scalable enough to keep improving beyond their first version.
- 02
Reinforcement learning
Training models to make the correct decision through reward and penalty for each action, so they become better at achieving the desired outcome.
- 03
Generative adversarial networks (GANs)
A generator learns patterns in the training data to create new content, while a discriminator tries to tell real from generated. Content that passes the discriminator can be trusted to look real.
- 04
Deep learning for computer vision
Detection, counting and segmentation in images and scans, as in our pill counting and eye MRI projects.
- 05
Custom language models
Private, self-hosted LLMs behind chatbot interfaces, so organisations can use generative AI on their own data.
- 06
Generative AI in medical imaging
Research into how AI can support cancer image translation in medical imaging.
- 07
Research on new methods
We seek out new AI technology and master it, then apply it to problems that off-the-shelf approaches do not solve.
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, Generative AI
Medical image translation between tissue stains for a pharma company
Reusedone 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.
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.
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.
Object detection in hyperspectral satellite imagery
10 x 8 px smallest 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.
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.
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
“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.
“They are not only great theoretical experts, but also deliver with their awesome hands-on skills.”
“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.
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
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
When is deep learning the right choice?
When you have large amounts of data, especially labelled data, and the problem is too complex for simpler models. Deep learning can handle more data, larger models and more computation, which often leads to better algorithms and new insights.
What problems does reinforcement learning solve?
Problems where a system has to learn to make the right decision step by step. The model receives a reward or a penalty for each action and improves at achieving the desired outcome over time.
What are GANs used for?
Generating new content that follows the patterns of the training data. A generator creates content and a discriminator tries to classify it as real or fake. Applications include image generation and image translation, for example in medical imaging research.
We lack data science talent. Can you still help?
Yes. A shortage of data science talent is one of the most common barriers to AI adoption. Our team brings the expertise in these methods, builds the solution with you and can help your people learn to work with it.
Do you only use the newest methods?
No. We use advanced methods where they add value. Where a simpler machine learning model solves the problem well, we recommend it.
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.