Our AI and LLM solutions help companies put their data to work: predicting outcomes, answering questions from internal knowledge and automating routine workflows.
Yotec helps startups and enterprises take AI from idea to production — from predictive analytics to conversational assistants built on large language models (LLMs). We focus on solutions that are secure, measurable and integrated with the software you already use.
From classic machine learning to generative AI, we choose the simplest approach that solves your business problem reliably. In addition, our security practices follow the OWASP Top 10 for LLM Applications.
Customer support and internal assistants that answer in natural language, follow your rules and hand over to a human when needed.
Retrieval-augmented generation that grounds answers in your documents, wikis and databases — with sources and access control.
Agents that read emails and documents, call your APIs and complete multi-step tasks, with approvals for sensitive actions.
Forecasting, anomaly and fraud detection, recommendation systems and time series models trained on your historical data.
Extraction of data from invoices, forms and contracts, document summarization, and image and video recognition.
Data pipelines, warehouses and vector databases, plus model training, versioning, deployment and monitoring.
We start small, prove value on real data and only then scale.
Many AI initiatives stall after an impressive demo. Our process is built to reach production: every step has clear success criteria, and the solution is integrated with your existing .NET, Java, Python or JavaScript systems.
Want the full picture first? Our AI & Machine Learning Development Guide explains the key concepts and decisions.
We identify where AI can save time or money, define success metrics and check that the necessary data exists.
A small prototype on your real data shows whether the approach works before you invest in a full build.
We add security, access control, evaluation tests and integrations with your systems and user interfaces.
We monitor quality, cost and drift, retrain models and improve prompts as your data and needs change.
AI that touches business data must be trustworthy. These principles are part of every project we deliver.
We are model-agnostic and choose tools based on your requirements, budget and data policies.
Practical articles from our team on building AI and LLM solutions that work in production.
Not always. LLM assistants and RAG search can work with your existing documents from day one. Predictive machine learning models usually need historical data, and we assess what you have during discovery before any development starts.
For most business cases that rely on company knowledge, RAG is faster and cheaper to build and easier to keep up to date. Fine-tuning helps when you need a specific style, format or behavior. Our article on fine-tuning vs. RAG explains the trade-offs.
We design every solution around your data policies: access control, data minimization, logging and, where required, deployment of models in your own cloud environment. We also test against prompt injection and data leakage before release.
A focused proof of concept on real data usually takes a few weeks. It gives you evidence of quality and cost before you decide on a full production build.
Yes. Most of our AI work extends existing products and internal systems through APIs, so your team keeps the tools it already knows. We integrate with .NET, Java, Python and JavaScript applications.
