What we build
AI & Machine Learning
LLM applications, RAG and predictive models
AI features fail in production when nobody measured them. We build evaluation harnesses alongside the model work, so you know accuracy, latency and cost per request before launch — and can prove the system stayed correct after every prompt or model change.
You leave with
- Production inference service with fallbacks
- Evaluation dataset and regression suite
- Vector store and ingestion pipeline
- Model governance and monitoring plan
Stack
- Python
- PyTorch
- LangChain
- pgvector
- Pinecone
- Claude API
- Vertex AI
What this includes
Tailored on the call. This is the foundation for ai & machine learning.
RAG assistants grounded in your own documents and data
Document extraction and classification pipelines
Forecasting, recommendation and anomaly detection models
Eval suites, guardrails, PII redaction and cost controls
30 minutes · CET · no deck
Book the call. Leave with a plan.
Bring the paper, the WhatsApp thread, or the spreadsheet. We will tell you what to build first — and what not to.