Validate before you commit
A proof of concept on a small data sample shows whether the solution is viable before you invest in development.
AI components · PoC to production
Development of AI Components with risk managed at every stage
We build AI modules, machine learning and data science components that fit your requirements and leave room for future evolution. Our AI Project Life Cycle Framework moves each component from proof of concept to a running system, so you invest in the next stage only once the last one has proven itself.
- Feasibility proven before larger investment
- Tested with real users in a limited pilot
- Runs in the cloud or on-premises at full scale
- 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 build AI in stages
Our value proposition is managing success and risk for you in AI projects.
Clear performance at each step
Every stage has a defined goal, so you know the component meets expectations before it moves on.
Built to evolve
Components meet your requirements today and stay flexible for future changes to your product and data.
What we deliver
From proof of concept to running system
The stages of our AI Project Life Cycle Framework, plus the research that supports it when there is no ready-made answer.
- 01
Proof of concept
A small-scale feasibility study, often on a sample of your data, that validates a business hypothesis. It usually takes from several days to several weeks.
- 02
Working prototype
Refines the concept, resolves uncertainties about how it works and adds data sources to enrich functionality. Typically several weeks to three months.
- 03
Pilot
A running system in production for a limited audience, so you can see how the service works in reality and act on its insights. Typically 30 days to several months.
- 04
Running data-driven system
Analytical software that generates insights at full scale, online or on demand, in the cloud or on-premises. Usually a couple of months to build.
- 05
AI research
Scientific answers to theoretical or practical research questions: compare alternative solutions, validate existing approaches or tackle a problem not addressed before.
- 06
Publications and research coordination
We support your research activities or take over coordination entirely, including papers for journals, conferences and workshops.
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
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.
NLP chatbot for centralised meeting scheduling
3,000 man-hours saved per month
A natural language processing chatbot for a centralised meeting scheduling system that automated the process and reduced the number of human operators needed.
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.
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.
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
“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 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 are not only great theoretical experts, but also deliver with their awesome hands-on skills.”
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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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 is the difference between a proof of concept, a prototype and a pilot?
A proof of concept tests whether a solution is viable, usually at small scale on a data sample. A prototype refines it and resolves how it works, looks and feels, often with more data sources. A pilot is a running system in production for a limited audience, where you can still make minor changes.
How long does each stage take?
As a guide, a proof of concept takes several days to several weeks, a prototype several weeks to three months, a pilot 30 days to several months, and a full-scale analytical system a couple of months. The exact duration depends on your data and requirements.
Can the final system run on our own infrastructure?
Yes. A running data-driven system can operate online or on demand and be hosted in the cloud or on-premises.
When does a project need AI research?
When you need to understand the alternative solutions to your problem, solve a problem that has not been addressed before, validate existing approaches in depth, or improve the performance of your current AI.
Can you publish research results with us?
Yes. Our data scientists publish, attend conferences and take part in workshops, and we have experience writing papers for journals, conferences and workshops. We work with partners at universities and research centres.
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