Make sense of many tables
Tabular data is easy to collect and read, but insight gets harder as tables multiply. Machine learning finds the patterns across them.
Technology · Data science and ML
Core Data Science and Machine Learning: actionable insights from tabular and time-series data
Most business data sits in tables, often hundreds or thousands of them. We use data science and machine learning to turn that data, and the time series within it, into forecasts, predictions and decisions.
- Insights from hundreds of business tables
- Accurate forecasts from time-series data
- Models for churn, fraud and lifetime value
- 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 your data can do
Core data science extracts insights from your data; machine learning learns from it to make predictions.
Forecast with time series
Time series add an explicit order to your observations. Modelling that structure is essential for accurate forecasts.
Predict what matters
From customer lifetime value to machine failures, models turn historical data into predictions your teams can act on.
What we deliver
What you can do with it
Typical goals we help businesses reach with core data science and machine learning.
- 01
Customer lifetime value prediction
Estimate the future value of each customer to focus acquisition and retention effort.
- 02
Customer churn analysis
Understand churn rates and identify the customers most likely to leave.
- 03
Product recommendations
Recommend relevant products to each customer based on behaviour and history.
- 04
Financial analysis and forecasting
Forecasts built on time-series data that account for multiple series and their dependencies.
- 05
Predictive maintenance
Predict when equipment needs attention before it fails.
- 06
Fraud detection
Detect fraudulent transactions and activity in your data.
- 07
Spam detection
Classify and filter unwanted messages automatically.
- 08
Eliminating manual data entry
Automate the capture of data that teams currently type in by hand.
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
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.
Personalised content recommendation for a media company
+5% diversity of content consumed by users
A personalised recommendation system that learns each user’s consumption patterns and preferences across diverse media content, helping a media company raise engagement, retention and customer lifetime value.
Property price estimation and market prediction for real estate
1M+ real estate items analysed to train the model
A machine learning tool that estimates property prices, monitors market trends and finds attractive offers, which guided around €20 million of real estate investments.
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.
“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.
“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.
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 core data science and machine learning?
Core data science focuses on extracting insights from the data you provide. Machine learning focuses on learning from that data to make predictions. Most projects combine both.
Our data is mostly in spreadsheets and databases. Is that enough?
Tabular data is the most common type of business data and a good basis for machine learning. The challenge is usually volume and spread across many tables, which is where data science helps.
Why does time-series data need special treatment?
A time series adds an explicit order of dependence between observations. That structure, and data sets with many different time series, add complexity, but factoring it in is essential for accurate forecasts.
Which business problems do you typically solve?
Customer lifetime value prediction, product recommendations, eliminating manual data entry, financial analysis and forecasting, predictive maintenance, spam and fraud detection, and customer churn analysis, among others.
Do you also work with other types of data?
Yes. Our projects also use sensor data, maps and medical scans, for example in usage-based insurance from smartphone telematics, urban zone pricing and eye tissue measurement from MRI.
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