More uptime.
Predictive maintenance can deliver 10–20% more uptime and 5–10% lower maintenance cost (Deloitte estimates).
Production AI models built on your own data, for forecasting, vision and language, running in your operations
Off-the-shelf AI rarely fits your data, your processes or your operating conditions.
This service designs, trains and deploys custom machine-learning and generative-AI solutions for a specific business problem, from forecasting demand and detecting fraud to predicting equipment failure. Every solution is validated against your own baseline before it scales.

Build around your data and operating reality, then prove the outcome before committing to scale.
Predictive maintenance can deliver 10–20% more uptime and 5–10% lower maintenance cost (Deloitte estimates).
AI forecasting can cut forecast errors by 20–50% (McKinsey estimate), meaning less waste and fewer lost sales.
Value is proven on your own baseline data at pilot scale before any investment in roll-out.
From a measurable business problem to a production-ready model and operating plan.
Demand, churn, credit-risk, maintenance and yield models.
Document understanding, visual inspection, and fraud and anomaly detection.
Cloud, on-premises or open-weight deployment for data that must stay in-country.
Recommendation engines, integration, monitoring and a practical retraining plan.
8–12 weeks from problem framing to a validated pilot, with production roll-out in follow-on increments.
Engagement Lead, two Data Scientists, an ML Engineer, a Data Engineer and a Domain / Process SME.
Python, PyTorch and scikit-learn; MLOps such as MLflow, Azure ML and SageMaker; and open-weight models such as Llama and Mistral.
Delivered in-house, with cloud and model providers engaged where required.
A fixed-fee pilot, plus a subscription for model enhancement and extension to new use cases.
A business owner and KPI baseline, historical data, and domain SMEs for labelling and validation.
Start with a fixed-fee pilot and see your model proven on your own data before you scale.