Integration without disruption
The model is connected to your existing systems, APIs and business applications, so AI-driven workflows run alongside the processes you already have.
LLM deployment · From model to production
Large Language Model (LLM) Deployment Services into your systems and workflows
We take a language model from selection to production: we plan the deployment, set up the infrastructure, connect the model to your applications and APIs, and tune it until it performs in real use. Off-the-shelf model or custom-trained, the goal is the same: an LLM your teams can rely on without disrupting how the business runs.
- A deployment roadmap built on your infrastructure
- LLMs integrated with your applications and APIs
- Tuned for latency, accuracy and running cost
- 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-planned LLM deployment delivers
Deployment is where a language model starts to pay off, or stalls. We focus on the steps that decide which.
Scalable and secure by design
Deployment strategy, infrastructure and security measures are planned together, so the model can grow with demand and handle sensitive data safely.
Performance that holds up
After go-live we test, tune and monitor the model for responsiveness, accuracy and efficiency, and retrain it as your needs change.
What we deliver
Our LLM deployment services
Each step of the deployment lifecycle, from the first assessment to ongoing monitoring.
- 01
Deployment strategy and planning
We analyse your business needs, technical infrastructure and deployment goals, and turn them into a deployment roadmap focused on scalability, security and efficiency.
- 02
Model selection and preparation
We select or customise the most suitable model, whether an established LLM such as GPT-4 or LLaMA or a custom-trained one, and test it before deployment.
- 03
Infrastructure setup and optimisation
We configure cloud, on-premises or hybrid environments and optimise server configuration, storage and networking for LLM workloads.
- 04
Model deployment and API integration
We connect the model to your existing systems, APIs and business applications and make it available across the platforms your teams use.
- 05
Performance tuning
We tune the deployed model to reduce latency, improve processing speed and accuracy, and keep operating costs under control.
- 06
Monitoring and maintenance
Continuous monitoring, model updates, retraining and performance evaluations keep the deployed LLM reliable as your requirements evolve.
- 07
LLM-powered applications
Where you need more than a model, we build the application around it: chatbots, virtual assistants, speech recognition and content generation tools.
Technologies we work with
- Python
- TensorFlow
- Scikit-learn
- Pandas
- Databricks
- Jupyter
- Kubernetes
- AWS
- Azure
- Oracle
- Grafana
- Tableau
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.
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.
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.
“They are not only great theoretical experts, but also deliver with their awesome hands-on skills.”
“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
Use cases by industry
LLM deployment by industry
Where we see deployed language models doing the most useful work.
Finance
Language models that support analysis, risk assessment and customer assistance.
- Real-time financial analysis
- Automated risk assessment
- Customer assistance
Healthcare
Models that support clinical work and reduce documentation effort.
- Diagnostics support
- Medical research analysis
- Automated documentation
E-commerce
Language models that improve engagement across the customer journey.
- Customer chatbots
- Personalised recommendations
- Automated product descriptions
Legal
Faster, more consistent handling of large document volumes.
- Document review
- Contract analysis
- Legal research automation
Marketing and Advertising
Content and targeting support for campaign teams.
- Generated content
- Ad copy
- Audience targeting
Education
Learning experiences that adapt to each learner.
- Tutoring systems
- Content generation
- Adaptive learning
Customer Service
Assistants that answer enquiries instantly and consistently.
- AI chatbots
- Virtual assistants
- Automated enquiry handling
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
What does LLM deployment involve?
Deployment covers the full path into production: assessing your infrastructure and goals, selecting or preparing the model, configuring cloud or on-premises infrastructure, integrating the model with your applications and APIs, testing and tuning it, and monitoring it afterwards.
Can you deploy an LLM on-premises?
Yes. We support cloud, on-premises and hybrid deployments and choose the set-up that fits your data, security and performance requirements.
Do we need a custom model, or can we deploy an existing one?
Both are possible. Many projects start with an established model such as GPT-4, LLaMA or Claude; others need a model customised or trained on your own data. We select or customise the model during preparation, based on your use case.
How long does LLM deployment take?
It depends on model complexity, the infrastructure set-up and how many systems the model must integrate with. We give you a realistic estimate after the initial assessment.
How do you keep a deployed LLM secure?
We build security protocols, data encryption and compliance measures into the deployment from the planning stage, rather than adding them afterwards.
What happens after the model goes live?
We test performance, accuracy and efficiency in real use, tune the configuration, and continue with monitoring, updates and retraining so the model keeps pace with your business. If you want us to run the model long term, see our LLM hosting services.
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.