Lower operating costs
Automating repeated procedures speeds up processes and reduces errors and the losses they cause, so staff can focus on work that improves service quality.
Industries · Internet and tech start-ups
AI in Internet Industry and Tech Start-Ups built into your product and operations
Internet businesses and technology start-ups run on data. We help them use it: faster and more accurate analysis, automation of repetitive processes, and machine learning features that reach their own customers, from churn prediction to natural-language interfaces.
- Lower costs through automation of repetitive work
- Risks predicted before they hit performance
- AI features your customers use directly
- 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 AI changes for internet businesses
The value comes from faster, more accurate data analysis and from automating processes that do not need people.
More productive operations
Automated methods are often faster and more efficient, which makes the business more productive and supports steady long-term growth.
Earlier view of risk
Machine learning on large volumes of data from across the business shows the state of each division and predicts problems early enough to prevent them.
What we deliver
AI solutions for tech companies
Use cases we build for internet platforms and technology start-ups.
- 01
Process automation
Automation of repetitive procedures to raise productivity, reduce errors and lower the demand for manual work.
- 02
Production and process optimisation
AI-based redesign of workflows so the business runs faster with fewer losses.
- 03
Risk prediction for decision-making
Models that collect and analyse data from across the business and give executives the information they need to act before problems grow.
- 04
Customer service chatbots
Chatbots that handle routine customer questions, collect more accurate information about customer needs and reduce the load on support agents.
- 05
Customer churn prediction
Models that learn user behaviour and predict churn for a given time horizon, combined with A/B testing of retention strategies.
- 06
Natural-language automation
NLP systems that read user messages and act on them, for example accepting, declining or rescheduling meeting offers without a human operator.
- 07
Candidate scoring and matching
Automated skill assessment and candidate-to-company matching for recruiting platforms.
- 08
Computer vision features
Object detection models, for example to assess whether workplace areas need cleaning while respecting privacy.
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
Internet and Tech · Machine Learning
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.
Read the case study
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.
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.
NLP skill gap analytics for a science and technology company
~10,000 man-hours saved
An interactive NLP tool that assesses employees' skills, compares skill profiles and identifies skill gaps between business-critical staff and their potential successors.
Object detection for workplace cleanliness monitoring
Automated detection of areas that need cleaning
Two object detection models that recognise when a workplace area needs cleaning and detect people without processing or storing their images.
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.
-
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).
-
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
“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.
“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.
“Overall, our experience with AI Superior has been extremely positive, and we have been highly satisfied.”
Delivered a highly specialized AI chatbot with custom AI logic for a foundation supporting children and young adults.
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
-
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.
-
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
Which AI use cases pay off first for an internet company?
Usually the ones closest to revenue and cost: churn prediction, automation of repetitive processes and customer service chatbots. For an online gaming platform, our churn model helped reduce churn to 11.3%.
Can you build AI features into our own product?
Yes. We build models and components that run inside your platform, such as candidate scoring and matching for a recruiting platform, which saved its scoring experts 800 hours of work per month.
Will integrating AI disrupt our running platform?
It should not. For the gaming platform we built a data collection and processing pipeline with almost no downtime, because stable operations were a core requirement.
What if an off-the-shelf AI service does not perform well enough?
We build custom models. A tech solutions company first used Google DialogFlow for meeting scheduling, but it could not fully automate the process; our NLP solution replaced it and reduced the number of human operators needed.
How do you run an AI project with a fast-moving team?
We follow an AI project lifecycle adapted from software development standards. Each phase has its own goals and quality criteria, and these must be met before the next stage starts, so quality is checked throughout rather than at the end.
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.