EXPLORE OSOL

Custom AI & MLSolution Development

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.

The value of custom AI
Diagram of an AI model core fed by data sources and connected to analytics, computer-vision and language outputs, with cloud servers, security and developer tools below

The value of custom AI

Build around your data and operating reality, then prove the outcome before committing to scale.

20%

More uptime.

Predictive maintenance can deliver 10–20% more uptime and 5–10% lower maintenance cost (Deloitte estimates).

50%

Sharper forecasts.

AI forecasting can cut forecast errors by 20–50% (McKinsey estimate), meaning less waste and fewer lost sales.

1 baseline

Proof before commitment.

Value is proven on your own baseline data at pilot scale before any investment in roll-out.

What’s included

From a measurable business problem to a production-ready model and operating plan.

Predictive analytics and forecasting

Demand, churn, credit-risk, maintenance and yield models.

NLP, computer vision and deep learning

Document understanding, visual inspection, and fraud and anomaly detection.

GenAI and LLM fine-tuning

Cloud, on-premises or open-weight deployment for data that must stay in-country.

Pilot-to-production engineering

Recommendation engines, integration, monitoring and a practical retraining plan.

Engagement at a glance

Duration

8–12 weeks from problem framing to a validated pilot, with production roll-out in follow-on increments.

Your engagement team

Engagement Lead, two Data Scientists, an ML Engineer, a Data Engineer and a Domain / Process SME.

Tools and technologies

Python, PyTorch and scikit-learn; MLOps such as MLflow, Azure ML and SageMaker; and open-weight models such as Llama and Mistral.

Delivery partners

Delivered in-house, with cloud and model providers engaged where required.

Pricing

A fixed-fee pilot, plus a subscription for model enhancement and extension to new use cases.

What we’ll need from you

A business owner and KPI baseline, historical data, and domain SMEs for labelling and validation.

Ready to get started?

Start with a fixed-fee pilot and see your model proven on your own data before you scale.

LET’S MAKE INTELLIGENCE USEFUL

A real challenge. A new possibility.

Tell us what you’re working on and the Osol team will get back to you.