Precise, relevant answers
Training on your domain-specific data improves contextual accuracy, personalisation and relevance for your use case.
LLM fine-tuning · Domain adaptation
Large Language Model (LLM) Fine-Tuning Services: adapt a proven model to your domain
You do not always need a new model. Fine-tuning adapts a pre-trained LLM to your domain with your own data, so it gives precise, context-aware answers with fewer biases. We prepare the dataset, fine-tune and align the model, optimise hyperparameters and benchmark the result before it goes live.
- Domain-specific accuracy from your own data
- Bias mitigation and aligned model output
- Benchmarked before it reaches production
- 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 fine-tuning changes
A pre-trained model knows language. Fine-tuning teaches it your domain.
Fairer, aligned output
We analyse and refine outputs to reduce bias and align responses with ethical AI standards across user interactions.
Efficient use of compute
Hyperparameter optimisation improves generalisation, minimises overfitting and keeps computational costs down.
What we deliver
Our LLM fine-tuning services
End-to-end fine-tuning, from the dataset to a model running in your applications.
- 01
Custom dataset preparation
We curate, preprocess and augment domain-specific datasets, so the model learns from relevant, high-quality data.
- 02
Fine-tuning pre-trained models
We fine-tune pre-trained LLMs on your proprietary data for better domain-specific language processing, sentiment analysis and accuracy.
- 03
Hyperparameter optimisation
We adjust hyperparameters systematically to lift performance, minimise overfitting and reduce computational costs.
- 04
Bias mitigation and alignment
We review and refine model outputs to mitigate bias and keep responses fair and aligned with ethical AI standards.
- 05
Evaluation and benchmarking
We test fine-tuned models in real-world scenarios and against industry benchmarks for accuracy, robustness and reliability.
- 06
Deployment and integration
We integrate the fine-tuned model into your applications and support the transition into production.
- 07
Monitoring and updates
We monitor performance and update the model with new data to keep it accurate and relevant.
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
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.
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.
Candidate scoring and matching for an online recruiting platform
800 man-hours of expert scoring saved per month
Automated candidate scoring and candidate-to-company matching for an online recruiting platform, replacing manual assessment of technical skills.
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
“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 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 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
Fine-tuned LLMs by industry
Fine-tuning on sector data makes a general model useful for specialised work.
Finance
Fine-tuned models for financial analysis and documents.
- Fraud detection
- Market analysis
- Automated trading
- Financial document processing
Healthcare
Refined models that support clinicians and patients.
- Clinical decision support
- Medical transcription
- Automated patient interaction
E-commerce
Models tuned to your catalogue and customers.
- Product recommendations
- Personal shopping assistants
- Automated customer support
Legal
Models fine-tuned on regulatory and legal documents.
- Contract analysis
- Legal research automation
- Compliance monitoring
Marketing and content creation
Models that write in the voice and format you need.
- Content generation
- Ad copy customisation
- 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.
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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
Should we fine-tune an existing model or build a new one?
If a pre-trained model handles language well but lacks your domain knowledge, fine-tuning with your data is usually the more efficient route. If you need full control over the model itself, we also offer custom LLM creation.
What are the benefits of fine-tuning?
Better domain-specific accuracy, more relevant and personalised responses, fewer biases in generated content and a more efficient model for your use case.
What data do we need?
Domain-specific data that reflects your use case, such as regulatory documents, technical documentation or customer interactions, and diverse language data if you need multilingual output. We curate, clean and augment it with you.
Can fine-tuning be tailored to our industry?
Yes. We tailor the fine-tuning process to your industry’s requirements and your business objectives.
Is our data secure during fine-tuning?
We use encryption, access controls and compliance measures to protect your proprietary datasets.
What does LLM fine-tuning cost?
It depends on data complexity, model size and computing requirements. We prepare a fine-tuning plan and estimate once we understand your needs.
How do we get started?
Contact us to discuss your fine-tuning needs. We propose a tailored strategy for refining 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.