Making AI practical on the factory floor

At ManIQ, we follow a structured, hands-on process to ensure that machine learning leads to real, measurable improvements in your production process. Here's how;

Step 0: Quick Scan & Feasibility Check

Before diving deep, we start with a quick but focused scan of your production process. This no-obligation assessment allows us to understand your operation, review available data, and identify potential areas for optimization. This helps us - and you - decide if our approach can add real value to your business.

Step 1: Process and Data Assessment

Now we roll up our sleeves and take a closer look at how your production works and what data we can use to start improving it. We examine control loops, sensor data, machine parameters, and historical trends to get the full picture.

Step 2: Data Preparation and Analysis

We clean and structure the data, then apply advanced analytics and machine learning techniques to uncover hidden patterns, inefficiencies, and optimization opportunities.

Step 3: Model Development

We develop JIT machine learning models tailored to your specific process—whether it's predictive control, quality optimization, or energy efficiency. Our advanced algorithms are designed for transparency and clarity, clearly linking outcomes back to input data.

Step 4: Control Strategy Design

Our models reveal bottlenecks, inefficiencies, and clear opportunities to improve productivity a/o quality. They generate real-time recommendations for optimal process configurations. Together with your team, we turn these insights into practical control strategies—like fine-tuning setpoints, adjusting control logic, or changing how certain steps respond in real time.

Step 5: Implementation

Here's where we truly stand apart: we don't just deliver recommendations—we implement them. Our engineers work together with your technicians to integrate the optimized strategies into your automation systems and PLC's, ensuring changes are embedded where they matter most.

Step 6: Testing and Validation

Before anything goes live, we rigorously test and validate the new controls—either in simulation or on a test setup—ensuring they work safely, reliably, and effectively.

Step 7: Continuous Monitoring and Refinement

Optimization is not static. Once deployed, we monitor the results closely. Based on real-time feedback and performance data, we continue to fine-tune both the machine learning models and the control logic to keep delivering long-term value.

Collaborative Implementation

We know we can't do this alone. That's why we work closely with your automation technicians and plant operators—discussing the recommendations, testing them through simulations, and aligning on the changes. Once everyone's on board, we move forward together to safely implement the improvements in your production process.