Optimised operations
Intelligent automation and machine learning streamline workflows, automate customer interactions and improve how resources are allocated.
Enterprise AI · Secure, scalable, integrated
Enterprise AI Development Services built to scale with your organisation
Large organisations need AI that fits their IT landscape, meets their security and compliance requirements and keeps working as volumes grow. We design, integrate and scale AI for automation, analytics and customer insight across enterprise operations.
- Integrated with your existing IT systems
- Data privacy and security designed in
- Architecture that scales from pilot to company-wide
- 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 enterprise AI must deliver
Security and compliance
Data privacy, ethical deployment and security frameworks protect sensitive information, supported by monitoring, risk assessment and automated compliance tools.
Room to grow
Cloud, edge AI and real-time analytics create systems that adapt as your needs change, from predictive maintenance to AI-driven business intelligence.
What we deliver
Our enterprise AI capabilities
The functions where enterprise AI most often pays off, and the strategy and infrastructure work that makes it last.
- 01
Enterprise AI strategy and consulting
AI strategies aligned with your enterprise goals and digital transformation objectives.
- 02
Business process automation
Solutions that automate enterprise workflows, reduce manual effort and lower overhead costs.
- 03
Intelligent document processing
Automation that extracts, classifies and processes enterprise documents.
- 04
Predictive analytics and forecasting
Models that analyse data patterns to forecast trends and support data-driven decisions.
- 05
Predictive maintenance
Models that detect equipment failures before they happen, reducing downtime and operating costs.
- 06
Risk management and fraud detection
Models that identify suspicious patterns in financial and business transactions.
- 07
Customer insights and service automation
Customer behaviour analysis for personalisation, plus chatbots and voice assistants that provide round-the-clock support.
- 08
NLP for enterprises
Text analysis and sentiment detection for business applications.
- 09
AI for cybersecurity
Threat detection systems that help protect the organisation from security breaches.
- 10
Scalable cloud and edge infrastructure
AI infrastructure in the cloud and at the edge that scales with business needs.
Technologies we work with
- PySpark
- PyTorch
- Hadoop
- TensorFlow
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
Insurance · Machine Learning
Risk estimation model for a medical insurance company
Outperformedstatistical 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.
Read the case study
Automated information extraction from invoices
25x faster invoice processing
A web service that extracts data from different types of invoices, automating data entry for a multinational equipment distributor and saving 850 man-hours per month.
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.
Road traffic analytics from city cameras for a system integrator
7,000+ car models recognised
A road traffic analysis system that processes traffic camera video to detect and track road users, recognise vehicles and plates, and flag traffic jams and violations without human monitoring.
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.
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, takes a different kind of team. This is what you get with us.
01
Scientists, not generalists
AI Superior was founded in 2019 by AI researchers with published papers and patents. That depth runs through the whole team.
- 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 goes only into solutions proven 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 together, not handed over between separate 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
“They delivered an extra module that we were not expecting but turned out to be very useful.”
Research and custom software development applying generative adversarial networks for a pharmaceutical company with 5,000+ employees.
“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.
How we work
A proven AI project life cycle
Every stage ends with a result you can check. You never commit to the next stage before seeing the previous one work, so scope, budget and risk stay under your control.
- 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
Together we evaluate the results of the implementation and make sure they are interpreted correctly.
You get: A clear picture of the value delivered and where to improve next
Use cases by industry
Enterprise AI by industry
Examples of enterprise AI solutions we develop for each sector.
Insurance
Sharper risk assessment, automated claims and better fraud detection.
- Claims automation for faster processing and approvals
- Predictive analytics for fraud and risk
- Underwriting and policy personalisation
Finance
Financial models that automate risk assessment, detect fraud and optimise investment strategies.
- Financial forecasting and risk analysis
- Automated compliance monitoring and fraud detection
- Transaction processing and anomaly detection
Pharmaceuticals
Optimised research, automated drug discovery and better patient care.
- Predictive modelling for drug development and trials
- Automated clinical data processing
- Risk analysis for pharmaceutical supply chains
Construction
Safety monitoring, cost estimation and predictive maintenance for construction management.
- Project cost estimation and budgeting
- Predictive analytics for construction risk
- Site monitoring and compliance tracking
Real Estate
Property valuation, search optimisation and transaction automation.
- Property valuation and price prediction
- Customer analytics for targeted recommendations
- Automated contract processing and compliance tracking
Oil and Gas
Better asset management, predictive maintenance and operational efficiency.
- Predictive maintenance for equipment and pipelines
- Asset tracking and supply chain optimisation
- Anomaly detection for safety and compliance
Media
Content creation, recommendation systems and audience engagement.
- Content recommendation engines
- Video and image analysis for content moderation
- Audience sentiment and trend tracking
Government
Data-driven decisions, efficient public services and stronger security.
- Automation for tax processing and financial auditing
- Citizen service chatbots and administrative automation
- Predictive analytics for economic and policy planning
Tech Start-ups
Automated workflows, customer engagement and operations for scaling companies.
- Workflow automation for scaling operations
- Customer analytics and business intelligence
- Automated customer support and chatbots
Space
Data analysis and decision support for space research, satellite monitoring and mission planning.
- Predictive analytics for satellite data
- Risk assessment for mission planning and logistics
- Automated research models
NGOs and Non-profits
Impact assessment, donor engagement and policy optimisation.
- Analytics for donor engagement and campaigns
- Automated financial and resource tracking
- Predictive models for social impact assessment
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 enterprise AI development?
Designing AI solutions and integrating them across a large organisation to automate workflows, improve business intelligence and support decisions. The difference from a single AI project is scale: the solution has to fit existing IT, meet security requirements and grow with the business.
How does enterprise AI benefit operations?
It automates processes, reduces costs, predicts market trends and improves data-driven decision-making. Typical starting points are business process automation, document processing, predictive maintenance and customer service.
How do you roll out AI across a large organisation?
In stages. Discovery defines goals and the improvements AI can bring. We then gather the relevant data, configure the models and set the integration strategy. An MVP validates the models on real processes before we scale the solution, integrate it into enterprise systems and optimise real-time performance.
Will the AI work with our existing IT systems?
Yes. Integration with existing systems is part of the design from the start, and we plan the integration strategy during initial setup rather than after development.
How do you handle data privacy and security?
Data privacy, ethical AI deployment and security frameworks for sensitive business information are core to our enterprise work. We can add monitoring, risk assessment models and automated compliance tools so your AI adoption meets the regulations that apply to you.
What happens after the solution is live?
We keep monitoring its effectiveness, refine the models and extend automation where it adds value, so adoption holds up over the long term.
How do we get started?
Contact us to discuss your business requirements. We assess your needs, design the solution and support implementation.
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