Services
Machine learning
& AI systems
We train models, build reproducible data pipelines, and implement foundation, meta, and agent architectures — from first hypothesis to integration with your embedded, IoT, and cloud landscape.
Model training & fine-tuning
- Supervised learning for industrial data
- Hyperparameters, validation, metrics
- Export and deployment readiness
- Reproducible training runs
Data pipelines & MLOps
- ETL, labeling, feature engineering
- Training jobs, artifact versioning
- Monitoring data and model drift
- Integration with existing IoT and backend stacks
Foundation & meta-models
- Base models and domain adaptation
- Ensembles, routing, specialists per sub-task
- Cost, latency, and quality trade-offs
- On-premise, cloud, or hybrid
AI agents & orchestration
- Multi-step pipelines with supervisor control
- Tool use and secure interfaces to systems
- MCP and function calling where appropriate
- Integration into dashboards, APIs, and field devices
Stack
From data to production
Python
PyTorch, scikit-learn, pandas
Pipelines
Airflow-style jobs, CI for models
Serving
FastAPI, batch & streaming inference
LLM / agents
OpenAI-compatible APIs, MCP, RAG
LLM and agent systems in production need security by design — OWASP LLM, prompt injection, and audit logs are covered under AI security. AI security & LLM audits →
ML in machinery: know your EU AI Act provider duties
If you embed an ML model in a machine and place it on the EU market:
- → You are a provider under the EU AI Act (Art. 3)
- → If the model affects safety functions: high-risk under Annex I
- → 12 duties under Art. 16 apply — from 2 August 2026
Developing the ML model technically is one thing. Regulatory classification, documentation, and conformity assessment is another. Solvetronix supports both.
EU AI Act for machinery manufacturers →Planning ML or agents in production?
Describe your data, constraints, and target — we respond within 48 hours with an initial assessment.
Request a free assessment