AI inside existing workflows
Chatbots, recommendation systems, predictive analytics and automation tools are added to your current workflows without disrupting operations.
AI integration · Existing systems
AI Integration Services without rebuilding your systems
We embed AI into the systems you already run: CRM and ERP platforms, business intelligence tools, customer support and cloud infrastructure. You gain automation, predictive analytics and better decisions while your operations keep running.
- AI added to current systems, not a replacement
- Secured with encryption and model governance
- Tested for performance before it goes live
- 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 integration delivers
Secure and reliable adoption
Encryption, authentication and AI model governance protect sensitive information and keep your systems stable and compliant.
Better system performance
Automated processes, real-time analytics and improved user interactions make your platforms more efficient, accurate and scalable.
What we deliver
Our AI integration capabilities
We connect machine learning, NLP and computer vision models to the platforms your teams use every day.
- 01
AI system integration
We embed AI models into your existing business applications and make sure they are compatible and efficient.
- 02
Machine learning model deployment
Trained models deployed within your business systems for automation and insights.
- 03
Chatbot and NLP integration
Chatbots in your customer support platforms, plus sentiment analysis and automated text processing.
- 04
CRM and ERP integration
AI enhancements to your business management platforms for better automation and customer engagement.
- 05
Computer vision integration
Image processing and visual recognition embedded into your existing platforms.
- 06
Business intelligence and analytics
AI analytics built into your BI platforms for real-time insights and better forecasting.
- 07
Predictive maintenance
Models that analyse data patterns to predict failures and optimise maintenance.
- 08
Data engineering for AI
We gather, clean and structure your data and build the data flows the integrated models depend on.
- 09
Cloud and edge integration
AI capabilities for cloud platforms for scalable, secure processing, and on IoT and edge devices for real-time decisions.
- 10
Model governance, security and risk
Ethical AI frameworks and security measures that support compliance and reduce bias, plus AI-based compliance monitoring and risk assessment.
Technologies we work with
- TensorFlow
- PyTorch
- OpenAI
- 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
Professional Services · Generative AI, NLP
Custom LLM chatbot with document references for a consulting firm
In-house LLMno 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.
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.
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.
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.
Pothole detection and road damage assessment for a public body
Automated pothole detection, severity scoring and mapping
A deep learning platform that detects and evaluates potholes and road damage on different types of roads with minimal human intervention, and maps them for maintenance teams.
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. Ivan Tankoyeu CEO 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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Dr. Sergey Sukhanov Advisor PhD in Machine Learning, data scientist since 2013. Publications, patents and 30+ implemented AI use cases; several IEEE awards.
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.
“Overall, our experience with AI Superior has been extremely positive, and we have been highly satisfied.”
Delivered a highly specialized AI chatbot with custom AI logic for a foundation supporting children and young adults.
“They are not only great theoretical experts, but also deliver with their awesome hands-on skills.”
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
Use cases by industry
How we integrate AI across industries
Examples of the AI components we add to existing platforms in each sector.
Insurance
AI in insurance platforms for claims, risk assessment and fraud detection.
- Claims automation
- Fraud detection
- Underwriting and policy recommendations
Finance
AI in financial systems for security, customer service and investment decisions.
- Fraud detection and risk assessment
- Financial analytics and reporting
- Portfolio management systems
Pharmaceuticals
AI analytics in healthcare and pharmaceutical systems for research and patient care.
- Clinical trial analytics
- Personalised treatment recommendations
- Predictive analytics for healthcare trends
Construction
AI in construction management platforms for safety, efficiency and forecasting.
- Risk assessment
- Site monitoring and compliance tracking
- Predictive project scheduling
Real Estate
AI in real estate platforms for automation, valuation and customer engagement.
- Property price prediction
- Search and recommendation engines
- Transaction document processing
Government
AI for automation, data-driven decisions and citizen services.
- Public service chatbots
- Document processing
- Predictive analytics for governance
Tech Start-ups
AI in your platform for automation, personalisation and business intelligence.
- Recommendation engines
- Customer analytics
- Automated chatbots
Media
AI in media platforms for content, audience engagement and trend analysis.
- Content recommendation engines
- Media sentiment analysis
- Automated video and image enhancement
Oil and Gas
AI models in energy systems for exploration, maintenance and risk management.
- Geological data analysis
- Predictive maintenance for equipment
- Operational risk assessment
Space
AI in space research and satellite systems for data processing and mission planning.
- Satellite imaging analysis
- Mission diagnostics
- Anomaly detection
NGOs and Non-profits
AI integration for automation, data analysis and policy research.
- Impact assessment
- Policy recommendation systems
- Fundraising and engagement platforms
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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Top Artificial Intelligence Company Germany 2026 · Clutch -
Top Machine Learning Company Germany 2026 · Clutch -
Top Computer Vision Company Germany 2026 · Clutch -
Top Generative AI Company Germany 2026 · Clutch -
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
Does AI integration mean rebuilding our systems?
No. AI integration adds AI capabilities to your current systems rather than replacing them. We design each integration for compatibility with your existing infrastructure.
How does an AI integration project run?
We start by analysing your current systems and defining integration goals. We then prepare your data, select and deploy models that fit your processes, embed them into your platforms and test for performance and scalability. After go-live we monitor and adjust the system.
Which systems can you integrate AI into?
Typical targets are customer support platforms, CRM and ERP systems, business intelligence tools, cloud platforms, and IoT or edge devices. We work with TensorFlow, PyTorch, OpenAI and cloud AI services from AWS, Google Cloud and Microsoft Azure.
How do you keep integrated AI secure?
We use encryption, authentication and AI model governance to protect sensitive information, and ethical AI frameworks to support compliance and reduce bias.
How long does AI integration take?
It depends on the complexity of your existing systems, the type of models and the level of customisation. Most integration projects take from a few weeks to a few months to implement and optimise.
How do we get started?
Contact us to discuss your project. Our experts assess your current systems, define an integration strategy and implement the solution.
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.