Efficient operations
Removing manual, repetitive tasks reduces human error, raises productivity and improves how resources are allocated.
AI automation · Operations
AI Automation Services that take manual work off your teams
We build AI automation that handles repetitive tasks, processes documents and supports decisions, and we integrate it into your existing infrastructure. Unlike rule-based automation alone, these systems learn from data and adapt as your processes change.
- Fewer manual tasks and fewer human errors
- Documents extracted, classified and checked
- Automation that scales with your operations
- 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 automation improves
Accurate and secure processes
Secure data handling, encrypted processing and AI-driven anomaly detection protect process integrity and support regulatory compliance.
Scalable, adaptive systems
Cloud integration and real-time processing let automation handle complex tasks and grow with your business.
What we deliver
Our AI automation capabilities
From high-volume back-office tasks to predictive decisions, pick the processes that cost your teams the most time.
- 01
Robotic process automation (RPA)
AI-powered RPA for fast, accurate execution of high-volume, repetitive tasks.
- 02
Intelligent document processing
Extraction, categorisation and analysis of structured and unstructured data, plus automated document classification and compliance verification.
- 03
Workflow automation
Business process automation tools that streamline workflows and reduce manual workload.
- 04
Predictive decision-making
Systems that analyse data and use predictive analytics to automate decisions and optimise workflows.
- 05
Chatbots and virtual assistants
Conversational AI that automates customer interactions and support.
- 06
Supply chain automation
Automation for logistics, inventory and supply chain operations.
- 07
HR automation
AI in HR processes for automated candidate screening and employee onboarding.
- 08
Fraud and anomaly detection
Automation that improves security by detecting fraud and anomalies.
- 09
Monitoring and predictive maintenance
Proactive monitoring that flags problems and schedules maintenance before failures occur.
- 10
Scalable automation infrastructure
Cloud-integrated automation systems built for scalability and adaptability.
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
Oil and Gas · Computer Vision, NLP
Automated information extraction from invoices
25xfaster 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.
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.
Drone-based construction debris detection for a city municipality
40% lower average inspection costs
A drone-based application that detects and reports 25 types of construction debris, automating construction site inspections and saving 320 man-hours per month.
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.
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, 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.
“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.
“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
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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
How we apply AI automation across industries
Examples of the processes we automate in each sector.
Insurance
Automation for claims, risk assessment and fraud detection.
- Claims automation
- Fraud detection models
- Underwriting and risk management
Finance
Automated financial workflows with stronger security and compliance.
- Risk assessment models
- Fraud detection and anomaly tracking
- Automated financial reporting and audit
Government
More efficient administrative processes and public services.
- Document processing and archiving
- Policy compliance monitoring
- Public service chatbots
Construction
Automation for safety, resource management and operational efficiency.
- Risk assessment
- Automated compliance tracking and reporting
- Predictive project scheduling
Pharmaceuticals
Automation in healthcare and pharmaceutical systems for research and operations.
- Clinical data automation
- Patient monitoring and diagnostics
- Automated drug research systems
Real Estate
Automation for property management, valuation and customer engagement.
- Property price prediction
- Automated documentation processing
- Smart property search
Tech Start-ups
Streamlined operations and personalised experiences for growing companies.
- Workflow automation
- Business intelligence analytics
- Automated customer service
Media
Automated content creation, distribution and audience engagement.
- Automated video and image editing
- Content recommendation
- Sentiment and trend tracking
Oil and Gas
Automation for exploration, maintenance and operational efficiency.
- Asset monitoring and optimisation
- Predictive maintenance
- Automated exploration data analysis
Space
Automation in space research, satellite monitoring and mission planning.
- Anomaly detection in satellite data
- Mission planning automation
- Automated research data processing
NGOs and Non-profits
Automation for impact assessment, data analysis and policy research.
- Policy recommendation systems
- Donor engagement and fundraising
- Resource allocation and volunteer management
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 the difference between RPA and AI automation?
Robotic process automation executes rule-based, repetitive tasks. AI automation adds machine learning and decision-making, so the system can analyse data, learn and optimise the process over time. We often combine the two.
Which processes are good candidates for AI automation?
Any process that depends on repetitive tasks, data analysis or customer interactions. Common examples are document processing, customer support, financial workflows, HR screening and onboarding, and supply chain operations.
Can AI automation work with our existing systems?
Yes. We integrate automation into your existing IT infrastructure, enterprise software and cloud platforms with minimal disruption.
Is AI automation secure?
We use encryption, access control and AI-driven monitoring to protect data privacy and support compliance with industry regulations.
How does an automation project run?
We assess your requirements and identify automation opportunities, collect workflow data and prepare the infrastructure, then build an MVP to test the automation. Once validated, we scale it into your enterprise environment and keep monitoring and fine-tuning the models.
How long does it take to implement AI automation?
It depends on the complexity and your specific requirements. Some solutions can be deployed in weeks; larger implementations may take several months.
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