> Applied AI for SMEs

Start by scanning your operations to find where AI can make a real impact. Look for repetitive tasks, data-driven decisions, or areas where mistakes are costly. This step helps you focus on the most promising opportunities—like optimizing production schedules, improving quality control, or predicting delivery delays where AI can bring measurable value.

Once you've identified where AI could help, it's time to turn those ideas into concrete requirements. This includes defining what data you need, who will use the AI output, and what outcomes you expect. For example, if your goal is to reduce production downtime, you'll define how much improvement is needed and how the system will communicate that to your team.

Before implementing AI, your data must be ready. This means organizing, cleaning, and structuring the data you already collect. Even with limited resources, you can start by organizing your most valuable data — such as production logs, warehouse records, or delivery schedules. A strong data foundation ensures that your AI can learn from accurate, consistent, and relevant information.

With clear requirements and a solid data foundation in place, we bring your AI idea to life by developing a solution that's built to solve your unique business challenges. This could be a forecasting model for inventory planning, a real-time monitoring system for production lines, or an AI-driven recommendation engine for sales.

After building your AI solution, the focus shifts to making it part of your everyday operations. This involves integrating AI with your existing systems like ERP, warehouse management, and production planning tools so it becomes a seamless part of your workflow and processes, training your team to use it effectively, and ensuring that it becomes a trusted tool that supports smarter, faster decision-making.

To ensure your AI solution keeps delivering, you need to track its performance over time. This means setting up key performance indicators (KPIs) such as accuracy, cost reduction, or time saved. By monitoring these metrics, you can detect when the model's accuracy or impact starts to decline due to changing conditions—like new production patterns or shifting customer behavior—and take corrective action before performance suffers.