Answers in your context
Models trained on your proprietary data give accurate, context-aware responses for your customers and processes.
LLM training · Data to benchmark
Large Language Model (LLM) Training Services engineered for accuracy and efficiency
Training a language model well is an engineering task: the right data, a pipeline that uses compute efficiently, and proof that the model works before it reaches production. We curate your datasets, build distributed training pipelines, tune hyperparameters and benchmark the result on real-world test cases.
- Curated, high-quality training data
- Training pipelines that use compute efficiently
- Models benchmarked before deployment
- 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 well-trained models deliver
Training decides how accurate, efficient and reliable your model is in day-to-day use.
Lower training cost
Scalable pipelines and systematic hyperparameter tuning make the most of computing resources and reduce costs.
Proven before go-live
Validation with standard metrics and real-world benchmarks confirms accuracy, efficiency and robustness before deployment.
What we deliver
Our LLM training services
The full training cycle, from raw data to a validated model that stays current.
- 01
Data preparation
We preprocess and curate large, diverse datasets, with cleaning and data augmentation to improve accuracy and contextual understanding.
- 02
Training pipeline development
End-to-end pipelines built for your needs, using distributed computing, cloud environments and high-performance GPUs to speed up training.
- 03
Model fine-tuning
We adapt pre-trained models with your domain-specific data for better accuracy and relevance.
- 04
Hyperparameter optimisation
We test and adjust parameters systematically to improve training efficiency, reduce overfitting and help the model generalise.
- 05
Validation and benchmarking
We evaluate trained models against standard metrics and real-world benchmarks before they go live.
- 06
Secure training
Data security and compliance measures protect sensitive training datasets throughout the process.
- 07
Deployment and integration
We help deploy the trained model into production and integrate it with your workflows and applications.
- 08
Monitoring and retraining
We monitor performance and update the model with new data, so it stays accurate and relevant over time.
Technologies we work with
- Python
- TensorFlow
- Scikit-learn
- Pandas
- Jupyter
- Databricks
- Kubernetes
- AWS
- Azure
- Oracle
- Grafana
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
Semantic company search for private equity deal sourcing
1 week of analyst work saved per new niche market
An end-to-end NLP solution that lets private equity funds and venture capital firms find and cluster relevant companies by meaning rather than by industry code or keyword.
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 skill gap analytics for a science and technology company
~10,000 man-hours saved
An interactive NLP tool that assesses employees' skills, compares skill profiles and identifies skill gaps between business-critical staff and their potential successors.
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, 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.
-
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).
-
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.
“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 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.
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
-
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
LLM training by industry
We tailor datasets, workflows and training to the needs of each sector.
Finance
Models trained on financial data and documents.
- Fraud detection
- Market analysis
- Automated trading
- Financial document processing
Healthcare
Models for clinical and patient-facing tasks.
- Clinical decision support
- Medical transcription
- Automated patient interaction
E-commerce
Models that improve how customers find and buy products.
- Recommendation systems
- Personal shopping assistants
- Automated customer support
Legal
Models trained on contracts, case material and regulation.
- Contract analysis
- Legal research
- Automated compliance monitoring
Marketing and content creation
Models that write and optimise content in your voice.
- Personalised ad copy
- Automated blog writing
- Social media optimisation
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.
-
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
What is the difference between training and fine-tuning?
Training covers the full process of developing a model on large datasets with substantial computing resources. Fine-tuning adapts an existing pre-trained model with your domain-specific data. We advise which route fits your goals and often combine both.
Why train a custom LLM?
A model trained on your proprietary data is more accurate and more efficient for your tasks, whether that is an AI assistant for your customers, industry-specific document analysis or a multilingual system.
Can training be tailored to our industry?
Yes. We tailor the training datasets, workflows and fine-tuning to your industry’s requirements and your business objectives.
Is our training data secure?
We use encryption, access controls and compliance measures to protect sensitive information throughout training.
How do you know the trained model is good enough?
We validate it with standard metrics, real-world test cases and benchmarks before deployment, and keep monitoring its performance in production.
What does LLM training cost?
It depends on data complexity, model size and computing requirements. We put together a training plan and estimate based on your requirements.
How do we get started?
Contact us to discuss your training needs. We propose a tailored strategy for developing, fine-tuning and deploying your model.
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.