Models that stay reliable
Continuous monitoring, optimisation and proactive issue resolution keep automation, predictive analytics and deep learning models performing.
AI managed services · AI in production
AI Managed Services that keep your models accurate
AI systems do not stay accurate on their own. Data changes, workloads grow and security requirements tighten. We take over the day-to-day operation of your AI: monitoring, retraining, tuning, infrastructure and compliance, so your models keep delivering and your team can focus on the business.
- Models monitored, retrained and kept accurate
- Less downtime through proactive issue resolution
- Security and compliance managed continuously
- 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 managed AI gives you
One team responsible for keeping your AI healthy across its whole lifecycle.
Security and compliance
Encrypted model deployment, vulnerability monitoring and governance frameworks protect sensitive data and support responsible AI use.
Operations that adapt
Retraining, cloud integration and resource management let your AI scale and change as the business does.
What we deliver
Our AI managed services capabilities
Everything it takes to run AI in production, as a continuous service.
- 01
AI model lifecycle management
We oversee models from deployment through ongoing optimisation and retraining.
- 02
Monitoring and performance analytics
Monitoring tools assess efficiency, detect anomalies and flag degradation before it affects accuracy.
- 03
Automated retraining
We update models with new data so they stay relevant and keep their decision-making accuracy.
- 04
Performance tuning and debugging
We refine algorithms, resolve model inefficiencies and optimise models for accuracy.
- 05
AI infrastructure management and scaling
We handle cloud-based deployments, computing resource allocation and scaling on AWS, Google Cloud and Microsoft Azure.
- 06
Resource and cost optimisation
We optimise AI workloads for computational efficiency and cost-effectiveness.
- 07
Security and risk management
Encryption, access control and compliance monitoring safeguard your AI systems and the data they handle.
- 08
AI governance and ethical compliance
We implement frameworks that promote responsible, fair AI use.
- 09
Workflow automation
We integrate AI management into your operations to streamline workflows and improve productivity.
- 10
End-to-end system support
Full monitoring, maintenance and troubleshooting of your AI infrastructure.
Technologies we work with
- PyTorch
- TensorFlow
- PySpark
- Hadoop
- 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
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.
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.
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.
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.
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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.
“They are not only great theoretical experts, but also deliver with their awesome hands-on skills.”
“I would be more than happy to speak to anyone about how much of a rock star you guys are.”
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
Managed AI across industries
Examples of the AI systems we monitor, tune and maintain in each sector.
Insurance
Managed AI for claims automation, fraud detection and policy recommendations.
- Fraud detection system maintenance
- Model tuning for claims processing
- Risk assessment optimisation
Finance
Secure, accurate models for fraud detection and customer automation.
- Fraud monitoring and anomaly detection
- Risk model maintenance
- Investment prediction optimisation
Pharmaceuticals
Accurate, well-maintained analytics for healthcare and pharma applications.
- Clinical trial model monitoring
- Drug research automation tuning
- Predictive healthcare analytics maintenance
Construction
Managed analytics for safety monitoring and operational efficiency.
- Predictive maintenance tracking
- Compliance monitoring
- Risk analysis model maintenance
Real Estate
Up-to-date valuation, documentation and search models.
- Pricing prediction updates
- Document processing management
- Property search optimisation
Oil and Gas
Monitoring and automation for AI systems in the energy sector.
- Exploration data model tuning
- Predictive maintenance optimisation
- Risk assessment model updates
Media
Managed content, audience and recommendation models.
- Recommendation optimisation
- Sentiment model updates
- Video and image processing management
Government
Managed automation for public administration and compliance.
- Document automation management
- Predictive analytics for governance
- Citizen service chatbots
Tech Start-ups
Optimised AI workloads that scale with the platform.
- Customer analytics model optimisation
- Chatbot tuning
- Personalisation engine management
Space
Managed models for satellite data and mission planning.
- Satellite imaging model optimisation
- Mission planning automation
- Anomaly detection tuning
NGOs and Non-profits
Maintained automation and analytics for non-profit operations.
- Impact assessment model updates
- Fundraising automation
- Policy research analytics
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 are AI managed services?
AI managed services are the continuous monitoring, optimisation, retraining and security management of AI applications and workflows once they are in production.
Why do AI models need ongoing management?
Models lose accuracy as data and conditions change, workloads grow, and security requirements evolve. Continuous monitoring and retraining keep models accurate, secure and scalable, and reduce downtime.
How does a managed services engagement start?
With a discovery stage in which we assess your AI system requirements, identify optimisation opportunities and define a long-term management strategy. We then configure monitoring tools, establish security protocols and set up lifecycle management before scaling the service.
Which platforms do you manage AI on?
We optimise AI workloads on cloud platforms such as AWS, Google Cloud and Microsoft Azure, and work with frameworks including PyTorch, TensorFlow, PySpark and Hadoop.
How do you keep managed AI secure and compliant?
We apply encryption and access control, monitor applications for vulnerabilities, and implement governance frameworks for responsible and fair AI use.
How is this different from AI cloud services?
AI cloud services focus on building and deploying AI on cloud platforms. Managed services cover what comes after: keeping models accurate, secure and efficient in production through monitoring, retraining and tuning.
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