Lower operating costs
Automate time-consuming, repetitive tasks to reduce errors and operational costs, and give your employees time for work that needs their judgement.
Custom AI software · Built for your data
AI Software Development, custom-built for your business
Off-the-shelf AI is built for everyone. We build AI software for one organisation: yours. Our data scientists analyse your processes and data, design software around your workflows and integrate it into your existing systems, then keep it running after launch.
- Software designed around your processes and data
- Integrated into your existing systems
- Continuous support and maintenance after launch
- 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 custom AI software changes in your business
Custom software is worth building when it moves costs, revenue or the speed of decisions.
Decisions on real-time data
AI processes large volumes of data quickly and extracts insights and predictions, so you can react faster, including in a crisis.
New revenue opportunities
Use AI-driven insights to optimise marketing campaigns, pricing and product offerings, and to find opportunities you are not yet using.
What we deliver
What we build into your software
Custom AI software across the core disciplines, plus integration of third-party AI services where they are the better fit.
- 01
Computer vision and image processing
Video and image analysis, object detection, contextual image classification, face recognition, image segmentation and optical character recognition.
- 02
Natural language processing
Extract meaning from text and build NLP applications with semantic search, speech recognition, sentiment detection and question answering.
- 03
Predictive analytics
Models that detect fraud, forecast market trends or predict equipment failure, so decisions rest on data rather than estimates.
- 04
BI solutions
Turn raw, unstructured data into clear visualisations that show operational inefficiencies, areas for improvement and new opportunities.
- 05
Big data analytics
Find patterns, gauge risks, spot gaps in the market and anticipate customer needs from large data volumes.
- 06
Third-party AI API integration
We integrate vision, speech, generative, text and document-parsing APIs, such as Google Cloud Vision, AWS Rekognition, Azure speech services, Amazon Textract and Google Document AI.
- 07
AI components for existing products
We develop AI modules and build them into the products you already sell or run, so you add intelligence without replacing the system.
- 08
Support and maintenance
After deployment we monitor, maintain and update the software so it keeps performing as your business changes.
Technologies we work with
- Python
- TensorFlow
- Scikit-learn
- OpenCV
- Pandas
- Jupyter
- Databricks
- Tableau
- Grafana
- Oracle
- Azure
- AWS
- Kubernetes
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
Energy · Computer Vision
Roof detection and segmentation for a solar energy company
Automatedroof detection and roof plane segmentation
Deep learning models that detect residential roofs in satellite images, segment them into distinct planes and estimate the area and dimensions of each, so solar panel installations can be planned efficiently.
Read the case study
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.
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.
Property price estimation and market prediction for real estate
1M+ real estate items analysed to train the model
A machine learning tool that estimates property prices, monitors market trends and finds attractive offers, which guided around €20 million of real estate investments.
Map segmentation for urban zone pricing on a real estate platform
5 classes of land use mapped pixel by pixel
A deep learning system that segments city maps, compares them with historical maps and tracks open land over time to support price assessment of urban zones.
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. 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.
“They successfully fulfilled every component of the project and exceeded our expectations.”
Built an AI model that analyzes the roofs of residential and commercial properties, including data collection and labeling. All key deliverables were completed on time.
“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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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 custom AI software and off-the-shelf AI?
Off-the-shelf AI is a generic product sold to many businesses. Custom AI software is built for one company: it is designed around your processes, data and user requirements and integrated into your existing systems, so it fits your workflows instead of forcing you to adapt to it.
How do you build custom AI software?
We validate the problem against your business goals, gather requirements and define scope and deliverables. We then prepare and clean the data, select the technology and model, design the architecture, and agree evaluation metrics. Development and testing run in continuous cycles, followed by deployment into your infrastructure and ongoing support.
How do you measure whether the software works?
Before development starts we define precise performance metrics for effectiveness and accuracy. These metrics guide testing and let you see whether the software achieves the outcomes you agreed on.
Can AI be added to software we already use?
Yes. We design the architecture for integration with your existing systems, and we can develop AI components that plug into products you already run. Where a proven third-party service fits, such as a speech or document-parsing API, we integrate it rather than build from scratch.
Which AI capabilities can be built into business software?
Common examples are NLP for chatbots and virtual assistants, computer vision for image recognition, speech recognition for voice commands and transcription, and pattern and anomaly detection for fraud prevention.
How long does it take to develop custom AI software?
It depends on your requirements, the complexity of the project and its scope. Projects range from a couple of weeks to a few months. We give you a realistic estimate once we understand the solution you need.
What happens after the software goes live?
We provide continuous support and maintenance for all software we deliver, so it keeps performing and stays up to date with changes in your business.
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