AI tailored to your needs
We select, tune, and deploy AI models so that they perform a specific function in your system — search, classification, transcription, or an assistant based on your data. Where necessary, we run AI locally — without sending data outside.
Application
AI as a real solution
We don’t deploy artificial intelligence models for the sake of AI. We start with the function that needs to actually work in your product. We adapt what’s already available to your needs and goals, and optimize it for deployment.
Speech to text
- Transcription and subtitles Automatic conversion of speech to text for recordings, lectures, or meetings. Subtitles adapted to accessibility requirements.
Content search and analysis
- Content search and analysis Semantic search across documents and materials. Automatic tagging, categorization, or sorting of documents and tickets.
Assistant
- An assistant based on your data A model that answers questions based on your documentation, knowledge base, or terms and conditions, with references to sources.
How we work
From function to a working model
First we establish what the model is supposed to do and what its constraints are — then we select the solution. We don’t start with the technology, but with the task.
Task and data analysis
We determine what function AI is to perform, what data is available, and what the requirements are regarding privacy, accuracy, and cost.
Data preparation
We clean, structure, and index the data so the model has something to work with. Without this, even a good model gives poor answers.
Model selection
We choose a model suited to the task: off-the-shelf, open, or commercial. We adapt it to the working mode: locally, on your side, or in the cloud.
Tuning to context
We fit the model to your data through RAG or fine-tuning, and where necessary by training on a prepared dataset.
Safeguards and cost control
We set limits, content filters, and query logging. The model behaves predictably and doesn't generate uncontrolled bills.
Evaluation and quality testing
We test the model on real cases: we measure accuracy, hallucinations, and costs. You know whether it works before it goes into production.
Integration with the system
We plug the model into the application as a real feature: API, background processing, and error handling.
Deployment and maintenance
We launch the solution in the target environment, measure the quality of responses, and hand over the project with documentation.
Services
A scope of services matched to your needs
Not every project needs its own training or dedicated solutions. We calibrate the level of customization to what your vision genuinely requires, and match the solution to the expected results.
Selection and integration
Choosing an off-the-shelf model and plugging it into the system as a working feature.
Tuning to your data
Grounding responses in your knowledge and tuning the model to the context.
A made-to-measure model
Training on a prepared dataset or building an AI feature from scratch.
AI consulting and audit
An assessment of where AI actually pays off, before we build anything.
Our approach
Substance, stability, and security
We start with the function
First we ask what AI is actually supposed to do in your product. If a simpler solution will do, we'll say so plainly instead of forcing a model into place.
Your data stays with you
Where privacy requires it, we run models locally or within your infrastructure. Materials don't have to leave your server room.
Tuning to real needs
We fit the model to your data and process, not the other way round. We calibrate the level of tuning so that the quality justifies the cost.
Quality control
We measure the accuracy of responses and make sure the model doesn't provide false information. Behind the result there's always a team of specialists.
CASE STUDY
A multimedia repository for a university
For a public university, we built a platform serving as a repository of multimedia materials. AI performs a specific function here: automatic subtitles for recordings — entirely locally, on the university’s server.
We deployed the transcription model (Whisper) within the university’s infrastructure, so that no recording ever leaves the server room. The entire process – from uploading the material, through transcoding, to generating subtitles and publishing — runs automatically and in the background. This illustrates our approach: AI plugged into a real workflow, matched to privacy and accessibility requirements, rather than added for show.
Our approach
What do we offer?
We name goals clearly and set priorities. We focus on concrete solutions rather than overblown promises.
We don't promise magic
AI models have limitations and can get things wrong. We apply them where they genuinely help, and we measure whether they work well enough for your task.
We safeguard data privacy
We agree with you on where and how data is processed. For sensitive projects, we opt for local solutions and GDPR compliance.
What works comes first
If the task can be solved without costly training, we’ll propose the cheaper route. Training only comes into play when it genuinely raises quality.
Code we understand
An AI feature is just one part of the system, which we maintain like any other: with documentation, tests, and room for further development.
Want to tailor AI to your needs?
Describe what you want to achieve and what data you have. Together we’ll work out a proposal for which model and level of customization you need, and choose the implementation approach that makes the most sense for you.
