For most manufacturers, the ERP system sits at the centre of operations. It connects engineering, production, inventory, purchasing, and finance, and keeps everything moving in the same direction.
AI is now being applied on top of that foundation.
Genius Cortex is designed as an embedded AI layer within Genius ERP. Rather than replacing existing workflows, it works directly with the data already in your system. The goal is straightforward: make information easier to access, reduce repetitive tasks, and support better decisions throughout the day.
In practical terms, that includes capabilities such as a built-in assistant, natural language search, automated data capture, and contextual recommendations within the ERP itself.
Genius Cortex is designed to make everyday work in Genius ERP easier. Built directly into the system by our in-house team, it helps users work with data in a more natural way, without changing how the ERP itself is set up.
Working in an ERP system typically means working with data. Users search for records, apply filters, and move between screens to find what they need. Over time, teams learn where information lives and how to navigate the system efficiently.
Genius Cortex doesn’t change that underlying structure. What it changes is how users interact with it. One example is our new built-in assistant. Instead of searching through documentation or navigating menus, users can ask questions directly within the system and receive answers based on available help content and training material. The interaction happens in context, without needing to step outside the workflow.
The same approach applies to data searches. Rather than manually building filters, users can describe what they are looking for in plain language. Cortex interprets the request and returns the relevant results.
The information itself remains the same. The difference is how quickly and easily it can be accessed.
A key factor in how Cortex is designed is its placement within the system. It is not an external platform or an add-on that requires separate processes. It operates within Genius ERP, using the same data and following the same workflows already in place.
That makes adoption more straightforward. Teams don’t need to learn an entirely new system or change how they work. Instead, the functionality builds on familiar processes.
A significant amount of data used in manufacturing doesn’t originate inside the ERP. It often comes from emails, spreadsheets, or customer communications. Before that information can be used, someone has to interpret it and enter it into the system.
Genius Cortex helps streamline that step. With features such as smart paste, users can bring information from external sources into the ERP, and the system can identify key elements — such as items, quantities, and units — and organise them into the correct structure.
The user still reviews and confirms the details. However, instead of starting from scratch, they are working from a structured draft. Over time, this reduces both the effort required and the likelihood of errors.
Communication plays a role in nearly every process, but it often exists outside the ERP. Emails, updates, and clarifications are typically handled separately, which can create gaps between what is recorded in the system and what has actually been discussed.
Genius Cortex is beginning to address this. With the new Outlook integration, emails can be linked directly to customers, quotes, and orders. The system can identify incoming messages, associate them with the correct records, and gradually build a more complete picture of each interaction.
This helps provide context. Instead of relying on separate threads or manual tracking, users can see both the data and the conversations that led to it.
One of the most valuable roles for AI within an ERP is decision support.
Take purchasing as an example. Traditional systems rely on rules such as reorder points, minimum levels, and lead times. These are necessary, but they do not always reflect real-world conditions. Experienced buyers regularly adjust recommendations based on supplier performance, demand patterns, and practical knowledge.
By analysing historical data, Genius Cortex builds on how your business has actually operated over time, suggesting what to order based on real patterns rather than static rules. It also provides an indication of how reliable those suggestions are. Where patterns are consistent, recommendations are more predictable. Where variability exists, the system highlights that uncertainty.
This gives users a more informed starting point, combining system logic with real-world behaviour.
In a typical workflow, a buyer reviews items that need to be replenished, checks stock levels, looks at usage trends, and adjusts quantities based on experience.
Genius Cortex uses demand history, supplier performance, and ordering patterns to suggest quantities and timing, helping guide the process. It also flags which recommendations are more stable and which may require closer attention.
The responsibility still sits with the buyer. The difference is that much of the data gathering is already done, allowing them to focus on evaluating the recommendation rather than assembling it.
As AI becomes more involved in operational decisions, accuracy and data protection become critical considerations.
Genius Cortex operates within a defined environment, using structured system data and approved sources such as documentation. This helps reduce the risk of irrelevant or incorrect outputs, although, like any AI system, it continues to be refined over time.
Data security is also treated as a priority. Customer data is not used to train shared models, and information remains isolated within each environment.These safeguards are particularly important in manufacturing, where data can be sensitive and subject to regulatory requirements.
In many organisations, decision-making relies heavily on the experience of key individuals. They understand patterns that may not be fully captured in system rules and adjust accordingly.
Genius Cortex is designed to complement that expertise. By identifying patterns in historical data, it helps bring some of that insight into the system itself. Over time, this can lead to more consistent outputs and reduce reliance on individual interpretation alone.
The role of the user does not change. Decisions are still reviewed and confirmed. The system simply provides a stronger starting point.
AI does not need to transform every process to be useful.
In many cases, the biggest gains come from smaller improvements — finding information more quickly, reducing repetitive work, and making decisions with better context.
That is the approach behind Genius Cortex. It focuses on helping manufacturers get more value from the ERP system they already depend on, without requiring a complete shift in how they operate.
If you want to explore how it works in more detail, the full walkthrough is available in the related webinar.
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