Fix the processes that hold you back
Find the pain points in your organisation, improve processes and procedures, and spot problems before they grow.
Business intelligence · Statistical analysis
Business Intelligence and Statistical Analysis for decisions based on evidence
We drill into your business data to find new opportunities, pinpoint organisational pain points and show where operations can improve. The result is reporting and analysis that tells managers what happened, why it happened and what is likely to happen next.
- Pain points found and fixed in less time
- Dashboards that track the metrics that matter
- Predictions to test projects before you commit
- 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 business intelligence lets you do
Track and optimise performance
Advanced dashboards track key statistics and performance metrics, and benchmarking compares your data with market leaders and competitors.
Plan with more confidence
Predict the success of new projects, changes and opportunities, identify market trends and build strategic plans on data.
What we deliver
What our BI and statistical analysis includes
- 01
Data mining
Explore large volumes of business data to surface patterns, opportunities and ways to increase profit.
- 02
Querying and reporting
Structured queries and reports that give managers reliable answers to their operational questions.
- 03
Descriptive analytics
Company reports that give a historic review of performance, so trends and changes are clear.
- 04
Diagnostic analytics
Statistical analysis that traces an issue back to its source, rather than treating its symptoms.
- 05
Predictive analytics
Models that forecast the future of the business and the likely outcome of projects and changes.
- 06
Performance metrics and benchmarking
Organisational performance metrics, tracked in dashboards and compared with competitors and market leaders.
- 07
Pain point analysis
A structured review of where the organisation loses time or money, with the improvements that address it.
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
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.
Social media analytics for a bank’s marketing activities
Lower churn most 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.
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.
Land degradation and conflict prevalence research for an NGO
74% accuracy predicting conflict occurrence 6 months ahead
Data science research with the humanitarian NGO World Vision, analysing satellite vegetation data (NDVI) and historical conflict records to study how land degradation relates to conflict prevalence.
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 descriptive, diagnostic and predictive analytics?
Descriptive analytics reviews historic performance through company reports. Diagnostic analytics determines the source of a specific issue. Predictive analytics forecasts what is likely to happen next, for example whether a new project or change will succeed. We combine all three depending on the decisions you need to make.
Can you build dashboards for our management team?
Yes. We create advanced dashboards that track the key statistics and performance metrics within your organisation, so managers can monitor operations and act on what they see.
Can we compare our performance with competitors?
Yes. Benchmarking is part of our work: we compare your organisational data with market leaders and competitors to show where you lead and where you fall behind.
How does business intelligence help find pain points?
By drilling into operational data, we identify where processes are inefficient or problems keep recurring, trace them to their source with diagnostic analysis and recommend improvements to processes and procedures.
How does BI relate to machine learning?
BI and statistical analysis explain past and current performance; predictive analytics and machine learning extend this to forecasts. Our data scientists cover both, so reporting and predictive models can be built on the same data foundation.
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