Individual service at scale
With data mining and micro-segmentation, media can serve individual requests quickly and become an information source on the user's request, not only an intermediary.
Industries · Media and entertainment
AI in Media and Entertainment for more personal, relevant content
Audiences now reach media through more channels than ever, and they expect content that fits them. AI is already used in media for personal recommendations, news editing assistants such as fact-checking, and news flow optimisation. We build the models that let media companies understand their audiences and serve them individually.
- Content recommended to each user individually
- Audiences segmented from your own data
- Editorial work supported by AI assistants
- 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 in media
AI makes media more personalised and differentiated, and changes how companies relate to their audiences.
New audience relationships
Media can act proactively (personalising content without a request), reactively (through recommendations) and passively (through voice assistants).
Higher engagement and lifetime value
Recommendations based on consumption patterns increase the diversity of content users consume and help reduce churn.
What we deliver
AI solutions for media companies
Use cases we see across TV, radio, podcasts, publishing and digital media.
- 01
Personalised recommendations
Collaborative filtering, item-to-item and hybrid recommenders that learn each user's consumption patterns and content preferences.
- 02
Audience micro-segmentation
Data mining that groups consumers into clusters, so you can target each group with the content it is most likely to enjoy and estimate demand for specific content.
- 03
Topic discovery and content tagging
NLP modules that tag content automatically and measure how similar items are from different perspectives.
- 04
Custom information products and subscriptions
Subscriptions that deliver a requested finished product and specific information products built around what each user asks for.
- 05
News editing assistants
Assistants for editorial teams, such as fact-checking support and news flow optimisation.
- 06
Protection of authors' identity
Registration of a journalist's personal brand, voice and appearance, so their uniqueness cannot be replicated to package and distribute content.
- 07
Audience and social media analytics
Analysis of audience interests, demographics and group affiliation to shape offers, service packages and marketing activities.
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
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.
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.
Custom LLM chatbot with document references for a consulting firm
In-house LLM no reliance on third-party LLM services
A private, hosted chatbot built on a custom large language model, giving a consulting firm answers with source references while keeping full control over its data.
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. 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).
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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.
“They are not only great theoretical experts, but also deliver with their awesome hands-on skills.”
“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.
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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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
What results can a media recommendation system deliver?
For a media company with TV and radio channels, podcasts, magazines and newspapers, our recommendation system increased the diversity of content consumed by users by 5%, which raised customer lifetime value. It also let the company identify groups of similar consumers and estimate demand for specific content.
Can one system handle very different content types?
Yes. The same system can recommend TV programmes, shows and news articles, because it combines user consumption patterns with content similarity assessed from several perspectives.
What data do we need for personalisation?
Mainly how users consume your content and the content itself. From these, models estimate each user's preferences, topics of interest and the devices they use to access content.
Can AI support editorial teams, not only audiences?
Yes. Typical examples are fact-checking assistants, automatic tagging of content and news flow optimisation, which take routine work off editorial teams.
How do you start an AI project in media?
We begin by agreeing the business goal, such as engagement or churn, and checking the available data. The project then follows a lifecycle in which each phase has its own goals and quality criteria that must be met before the next stage starts.
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.