AI/ML Integration
We bring artificial intelligence from hype to production. Our AI/ML engineers integrate large language models, build custom data pipelines, implement computer vision solutions, and create recommendation systems — all designed to deliver measurable business value, not just impressive demos.
Key Benefits
LLM integration with OpenAI, Anthropic, and open-source models
Custom ML models for prediction, classification, and anomaly detection
Computer vision for document processing, quality control, and analytics
RAG pipelines and knowledge bases for enterprise AI assistants
Technologies We Use
How We Deliver
Use case assessment
Data pipeline design
Model development & training
Integration & testing
Monitoring & retraining
Related Projects
Retail Bank — Internal Tools
Built internal workflow automation and data analytics dashboards for a major Czech retail bank. Reduced manual processing time by 60%.
FinCORTEX
Financial management platform for Czech SMBs. A comprehensive CFO-as-a-service platform with financial diagnostics, cash flow management, and reporting dashboards.
Frequently Asked Questions
What is the ROI of integrating AI into my product?
ROI varies by use case, but our clients typically see 20–40% reduction in manual processing costs, 15–30% improvement in decision accuracy, and measurable revenue gains from personalization and automation. We begin every engagement with a use case assessment to identify the highest-impact opportunities and estimate concrete returns before writing any code.
What data do I need to get started with AI/ML?
The minimum viable dataset depends on the task — classification models need at least a few thousand labeled examples, while LLM-based solutions can work with your existing documents and knowledge bases. We help you audit your data assets, identify gaps, and build data pipelines to collect and clean the information needed for effective model training.
How do you deploy and maintain ML models in production?
We use MLOps best practices including containerized model serving, A/B testing, automated retraining pipelines, and real-time performance monitoring. Models are deployed behind API endpoints with versioning and rollback capabilities. We track model drift and accuracy metrics to ensure predictions remain reliable as your data evolves.