Deploy AI without on-premise infrastructure
Host models, process data in real time and run cloud-based inference with computing power and resources allocated as you need them.
AI cloud services · AWS, Google Cloud, Azure
AI Cloud Services for AI that scales without hardware
We build, train and run AI on cloud platforms, so you can scale machine learning, data processing and inference without investing in on-premise hardware. Our cloud architects, AI engineers and data scientists design the set-up, move your models into it and keep it secure and efficient.
- AI workloads on AWS, Google Cloud or Azure
- Train and scale models without on-premise hardware
- Encrypted storage and compliant model deployment
- 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 in the cloud makes possible
The cloud removes infrastructure as the bottleneck for AI. We make sure it also stays secure and cost-efficient.
Security and compliance in the cloud
Encryption, cloud-based access controls and privacy-focused design protect sensitive data and support compliance with industry regulations.
Scale training and processing
Process large data sets, train deep learning models and deploy AI applications on infrastructure that grows with the workload.
What we deliver
Our AI cloud capabilities
From the data pipeline to the model in production, built and run on cloud platforms.
- 01
AI model deployment and hosting
We deploy and manage AI models on cloud platforms with the accessibility, scalability and availability your applications need.
- 02
Scalable machine learning infrastructure
We set up cloud infrastructure to train and fine-tune machine learning models, reducing costs and improving computational efficiency.
- 03
AI-powered data processing
Cloud-based pipelines for real-time data processing, analytics and automation, including data collection, cleaning and preparation for model training.
- 04
Cloud automation
We implement AI-powered automation in cloud environments to optimise workflows and reduce manual processes.
- 05
Cloud-based NLP and computer vision
We develop cloud-based chatbots, sentiment analysis and vision systems that analyse images and detect objects.
- 06
Predictive analytics and forecasting
We build predictive models on cloud platforms that help you anticipate trends and improve forecasting accuracy.
- 07
Edge AI development
We optimise models for edge computing, enabling real-time decisions on connected, cloud-enabled devices.
- 08
AI security, ethics and governance
Encrypted data storage, secure model deployment, bias mitigation and governance frameworks that support compliance and responsible AI use.
Technologies we work with
- AWS
- Google Cloud
- Microsoft Azure
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
Government · Computer Vision, Machine Learning
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.
Read the case study
Object detection in hyperspectral satellite imagery
10 x 8 px smallest object size recognised
An environmental monitoring solution, developed as part of our social responsibility programme, that detects small objects in hyperspectral satellite imagery with very high accuracy.
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.
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.
Pill detection and counting for a healthcare technology provider
99.9% precision, recall and F1 across all predictions
A computer vision system that identifies, classifies and counts pharmaceutical pills by National Drug Code (NDC) from photos, replacing error-prone manual counting.
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.
-
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.
-
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).
-
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.
-
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.
-
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.
“I would be more than happy to speak to anyone about how much of a rock star you guys are.”
“They are not only great theoretical experts, but also deliver with their awesome hands-on skills.”
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
-
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
How we apply AI cloud services across industries
Examples of cloud-based AI solutions we build for each sector.
Insurance
Cloud AI that supports risk assessment, claims processing and customer service.
- Policy document automation
- Cloud-based claims processing
- Fraud detection and risk assessment
- Personalised insurance recommendations
Finance
Streamlined operations, stronger security and better customer experience.
- Financial reports and analytics
- Fraud detection and risk assessment
- Market trend analysis
- Investment strategy recommendations
Pharmaceuticals
Cloud AI for research, clinical reporting and patient care.
- Drug development simulations
- Clinical trial report automation
- Personalised treatment recommendations
Construction
Cloud-based AI for safety, planning and project management.
- Site monitoring and safety analysis
- Project risk assessment
- Labour and material forecasting
Real Estate
AI that supports property management, valuation and marketing.
- Pricing prediction models
- Property descriptions and listings
- Document processing
- Personalised property recommendations
Oil and Gas
Predictive analytics and monitoring for exploration, production and maintenance.
- Drone and satellite data analysis
- Predictive maintenance
- Geological report generation
- Resource allocation
Media
Cloud AI for content production and audience engagement.
- Article and news summaries
- Video and image enhancement
- Sentiment analysis
- Content recommendation engines
Government
Efficiency, transparency and data-driven decisions in public administration.
- Report automation
- Intelligent document processing
- Public service chatbots
- Predictive analytics for governance
Tech Start-ups
Scalable cloud AI for product features and customer engagement.
- Product and marketing content
- Chatbots and support automation
- User behaviour analytics
- UI and UX optimisation
Space
Cloud AI for satellite monitoring and mission planning.
- Satellite imaging analysis
- Predictive mission diagnostics
- Anomaly detection in space systems
NGOs and Non-profits
Cloud automation for social impact, fundraising and policy research.
- Grant application automation
- Impact assessment reporting
- Fundraising engagement strategies
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.
-
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 are AI cloud services?
AI cloud services cover deploying, managing and optimising AI models and data processing on cloud infrastructure, so AI applications can scale without on-premise hardware.
Why run AI in the cloud rather than on-premise?
The cloud gives you computing power for training and inference on demand, scales with the workload and avoids investment in dedicated hardware. With the right security set-up, it also supports encryption, access control and compliance requirements.
Which cloud platforms do you work with?
We work with AWS, Google Cloud and Microsoft Azure, including multi-cloud set-ups, and with custom machine learning models deployed on them.
Can you integrate cloud AI with our existing systems?
Yes. We integrate AI solutions with your existing infrastructure and workflows, and design them so they can be optimised and scaled later.
How long does an AI cloud project take?
It depends on model complexity and integration needs; typically weeks to months. We start with a discovery stage and usually validate the approach with an MVP before scaling it across your processes.
Is this only for language models?
No. We run many kinds of AI in the cloud: machine learning and predictive models, NLP, computer vision and data pipelines. For language models specifically, see our LLM hosting and deployment services.
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