AI and machine learning
AI demos are impressive. AI in production is hard. We build machine learning that solves actual problems, not PowerPoint presentations.
Everyone talks about AI. Few deploy it in production. The gap between a demo and a running system is enormous, and that's where we operate. Practical models, trained on your data, solving your problems. Quality prediction, anomaly detection, process optimization. If it doesn't deliver measurable results, we don't ship it.
Common challenges
- Hype vs. practical applications unclear
- Data quality issues blocking progress
- Models that work in labs fail in production
- No internal ML expertise
Our approach
- Identify high-value use cases
- Assess and prepare data
- Train and validate models
- Deploy with monitoring and retraining
Use cases
Quality prediction models that flag potential defects before products leave the production line.
Process optimisation using historical data to find the ideal parameters for yield and energy consumption.
Natural language search across maintenance logs and technical documentation.