Understand the potential of your data
Find out what your data can support, and which decisions and AI products it can drive, before you invest in development.
AI consulting · Data strategy
Data Strategy that turns your data into an AI foundation
We align your data assets with your business goals and give you an actionable strategy for using them. Our consultants work with your subject matter experts, apply best practices and support every data-related activity on the way to AI in production.
- Data assets aligned with your business goals
- Noisy, incomplete or unreliable sources fixed
- A roadmap from current state to AI-ready data
- 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
When a data strategy makes the difference
Companies usually come to us in one of these situations.
Fix problematic data sources
Noisy or incomplete data and inaccurate existing models are audited, cleaned and corrected, so insights are not built on corrupted inputs.
Close gaps in data experience
Where you lack data expertise or resources, our consultants fill the gap and give your analytics continuous support as it evolves.
What we deliver
What our data strategy work covers
Big Data, data science, AI and machine learning consulting, from the first audit to ongoing support.
- 01
Data auditing and cleansing
We audit, verify, validate and cleanse your data so poor or corrupted records are not used to derive insights or train models.
- 02
Data and AI roadmap
We analyse your current situation, agree on a target vision with you and turn it into a step-by-step strategic roadmap.
- 03
Data architecture design
We design and evaluate data architectures that let your teams handle your data efficiently.
- 04
Improving existing data science solutions
We review your current models and analytics, apply best practices and adjust what is underperforming.
- 05
Tools and technology selection
We help you choose and adopt the data science and AI tools that fit your use cases.
- 06
Data governance and privacy
We set up governance structures that improve data quality and handle sensitive data in line with privacy regulations such as GDPR.
- 07
Collaboration with your experts
Our consultants work closely with your subject matter experts, so the strategy reflects how your business actually uses data.
- 08
Continuous analytics support
We support the ongoing evolution of your analytics after the strategy is in place.
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
Media and Marketing · Machine Learning
Social media analytics for a bank’s marketing activities
Lower churnmost at-risk clients retained, per client feedback
An interactive tool that analyses social media data to reveal audience interests, social group affiliation and demographics, so a bank can target offers that build loyalty and reduce churn.
Read the case study
Risk estimation model for a medical insurance company
Outperformed statistical baseline models
A neural network model, trained on five consecutive years of historical medical data, that estimates the risk of economic loss so a niche health insurer can optimise its pricing policies.
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.
Candidate scoring and matching for an online recruiting platform
800 man-hours of expert scoring saved per month
Automated candidate scoring and candidate-to-company matching for an online recruiting platform, replacing manual assessment of technical skills.
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.
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 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.
“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.
“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
Why does AI need a data strategy?
Models are only as good as the data behind them. A data strategy makes sure the right data is available, clean and governed before you decide which AI products to develop and deploy, which reduces the risk of projects that stall on data problems.
Our data is noisy and incomplete. Can you still work with it?
Yes. Problematic data sources are one of the most common reasons clients come to us. We audit, validate and cleanse the data and assess what it can realistically support.
We already have models in production that are not accurate enough. Can you help?
Yes. We review your existing data science solutions, apply best practices and adjust them, working closely with your subject matter experts.
We do not have a data team. Is that a problem?
No. Our consultants support all data-related activities. If you want to build your own capability, we can also help you plan and hire an in-house data science team.
How do you handle sensitive data?
We follow data privacy regulations such as GDPR when handling sensitive data, and we help you set up governance structures that keep data use compliant and secure.
Does the engagement end with a strategy document?
It does not have to. We can support the continuous evolution of your analytics and help implement the roadmap, from proof of concept to production.
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