New capabilities in existing processes
Automate document creation, personalise marketing and generate insights inside the workflows your teams already use.
Generative AI · Integration into your operations
Generative AI Integration Services, secure, monitored and built into your workflows
We connect generative AI to the systems your business already runs: data pipelines, document workflows, customer channels and cloud infrastructure. The focus is on reliable output, secure deployment and compliance with data privacy rules, so the models become part of daily operations rather than a side experiment.
- Generative AI embedded in existing workflows
- Secure deployment with real-time monitoring
- Cloud infrastructure that scales as demand grows
- 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 a well-integrated generative AI delivers
Integration decides whether generative AI is used every day or stays a demo.
Accurate, secure and compliant output
Secure model deployment, real-time monitoring and data privacy compliance, with bias mitigation and transparency so outputs can be trusted and user data stays protected.
Solutions that scale and adapt
Models incorporated into cloud infrastructure, optimised for real-time processing and continuously refined as your requirements change.
What we deliver
Our generative AI integration capabilities
What we build and connect, and how we keep it secure and scalable once it runs.
- 01
Model development and deployment
We build, integrate and optimise generative models for your specific business applications.
- 02
Natural language generation
Automated reports, chat responses and marketing copy generated from your data.
- 03
Chatbots and virtual assistants
Conversational agents that automate customer support and user interactions.
- 04
Intelligent document processing
Automation tools that generate, edit and organise business documents.
- 05
Data synthesis, reporting and decision support
Tools that generate reports, summaries and data-driven predictions, and decision support that turns them into business insights.
- 06
AI-powered code generation
Generative models that automate software development tasks, including code suggestions and optimisations.
- 07
Personalisation from real-time data
Models that generate personalised content, recommendations and user interactions based on live data.
- 08
Image and video synthesis
Visual content generation for marketing and design teams.
- 09
Secure deployment and monitoring
We deploy models securely, monitor them continuously and keep them compliant with data privacy laws.
- 10
Scalable AI infrastructure
Cloud-based platforms built for growth, so the solution expands smoothly as usage increases.
Technologies we work with
- PyTorch
- TensorFlow
- PySpark
- Hadoop
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
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.
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.
Medical image translation between tissue stains for a pharma company
Reused one disease classifier across stain reagents
An AI component that translates histological images from one stain domain to another, so existing software and machine learning models can be reused across all reagents.
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
“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.
“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.”
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
Generative AI integration across industries
Where we connect generative AI to existing platforms in each sector.
Insurance
Integrated into insurance platforms for customer communication, claims and risk reporting.
- Automated policy document generation
- Personalised customer communication and chatbots
- Predictive risk assessment reports
Construction
Applied to project planning, report generation and safety analysis.
- Predictive maintenance insights for equipment
- Compliance documentation and safety reports
- Risk models for project forecasting
Finance
Real-time analysis, fraud detection and automated financial reporting.
- Financial reports and investment summaries
- Fraud detection and anomaly analysis
- Predictive analytics for market trends
Government
Document automation, citizen engagement and decision-making in public administration.
- Policy documentation and public reports
- Chatbots for government assistance
- Data analysis for policy recommendations
Tech Start-ups
Personalised customer experiences, automated workflows and faster product work.
- Marketing content and branding materials
- Chatbots and virtual assistants
- Business intelligence and predictive analytics
Media
Integrated into media platforms for content creation, engagement and creative workflows.
- Video, image and text creation tools
- Content recommendation and curation engines
- Audience sentiment analysis
Oil and Gas
Exploration analysis, operational efficiency and safety monitoring.
- Geological data analysis for exploration
- Predictive maintenance for equipment monitoring
- Risk assessment reports for operational safety
Pharmaceuticals
Support for drug discovery, research documentation and patient care.
- Clinical trial documentation and analysis
- Predictive models for drug development
- Patient care and treatment insights
Real Estate
Automated property descriptions, pricing models and customer experience.
- Property listings and virtual tour content
- Property pricing and market analysis
- Transaction document processing
Space
Satellite data analysis and more efficient mission planning.
- Satellite image analysis and pattern detection
- Mission planning simulations and risk assessments
- Anomaly detection in space research
NGOs and Non-profits
Impact reporting, fundraising and resource allocation.
- Donor engagement content and reports
- Social impact assessment
- Volunteer and resource 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 do generative AI integration services cover?
Incorporating generative models into your business operations: connecting them to your data pipelines, configuring the workflows they support, testing them on an MVP, deploying them to cloud infrastructure and monitoring them in production. Typical uses are content creation, customer interactions and decision support.
Can generative AI be customised to our business?
Yes. Models can be fine-tuned and customised to your goals, whether that is customer service automation, personalised recommendations or automated data processing, and configured to fit your operational requirements.
What are the security risks, and how do you address them?
The main risks are data privacy, misinformation and model bias. We address them with data encryption, ethical AI frameworks, secure deployment and ongoing model monitoring, so the solution stays compliant and misuse is prevented.
How do you integrate without disrupting operations?
We start by assessing your integration needs and defining goals, then configure models and data pipelines. An MVP tests the generative features on real tasks before we scale, integrate cloud-based models and roll out fully. After launch we monitor performance and refine the models.
How does generative AI support data analysis and reporting?
It processes large data sets, generates trend analyses and produces reports and executive summaries in readable language. That saves time for analysts and gives decision-makers faster access to the findings.
How does generative AI improve marketing and customer engagement?
It automates content creation, generates targeted ad copy, personalises customer interactions and optimises campaigns with real-time insights, for example in email campaigns, chatbots and social media posts.
Does integration scale with the size of our organisation?
Yes. Solutions are sized to your needs and built on scalable cloud infrastructure, so they can start with one workflow and expand without extensive new infrastructure.
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