ANILOX DATABASE – UNTAPPED INTELLIGENCE

In any flexo plant with a good measurement culture, a shelf, shared drive or desk drawer full of anilox audit reports can be found. The cell volume can be checked, the screen count confirmed and the condition assessed. Each report is read once, perhaps actioned and then filed away. For most of the industry, the story ends there.

This is the strange reality of anilox management in 2026. Instruments have never been better and data has never been richer. However, the intelligence generated by this measuring is almost entirely wasted. It is not deleted, but stranded in reports and databases that are rarely queried when a decision is actually needed. Measurement happens, but learning does not. 

A single audit can provide the condition of a roll today, but a sequence of audits tells its story. It reveals how fast cell volume decays, whether a cleaning regime genuinely recovers volume and if one supplier’s rolls consistently outlast another’s. Crucially, it reports if a specific roll will remain within specification when a big job lands in a few months. 

That long-term view is the difference between simple quality control and true fleet intelligence. It supports wear prediction, supplier benchmarking, evidence-based re-ordering and smarter job matching. Nearly every converter who measures regularly already owns this raw material. The question is when anyone last looked at it. 

Data capture has never been better. The latest measurement instruments – such as Troika System’s Troika EVO – pair significantly higher speeds with improved sensor technology, changing the economics of measurement. When a scan takes seconds rather than minutes, auditing an entire fleet on a regular cycle becomes routine rather than an aspiration. The quality, density and frequency of anilox data – now within an ordinary converter’s reach – would have been unthinkable a decade ago. 

Storage and organisation have matured simultaneously. Fleet-management platforms – such as the Troika Management System – consolidate measurement records across presses, plants and continents. This gives multi-site companies a single global picture of their anilox assets. The pipeline from the anilox cell to the database is complete. 

“AI can help to shift from research tasks to 30-second queries”

In most operations, not enough is done with this data. Data-management systems are built as repositories and reporting engines. They are excellent at storing information and answering questions configured in advance. However, they demand that the user comes to them – log in, navigate, filter and export. Even then, this assumes that the operator is trained to use the system. 

This approach can work for a quality manager conducting a quarterly review, but it is of no use to a press-room manager making a roll decision. That person cannot stop production to build a report. The bottleneck in anilox intelligence is now accessibility at the moment of decision. 

“An AI layer is only ever as good as the underlying measurement data”

Troika Systems believes large-language artificial intelligence (AI) models will overcome this barrier. A conversational layer can now sit directly on top of fleet data and answer plain-English questions. No dashboard configuration or training is required. Questions can be answered such as “Which of my 4cm³/m² rolls are within ten per cent of end-of-life?” “How do wear rates compare between our two engravers?” “What should I re-order before the autumn peak?” AI can help to shift from research tasks to 30-second queries. 

This should not be mistaken for the claims currently filling the press about AI transforming print. However, this is a real, tangible use case that takes existing measurement records and makes them accessible to anyone needing such information. 

For example, a press-room manager scheduling a long UV-flexo run requiring a tight volume band. Verifying the fleet against that requirement currently means time spent digging through reports and menus. More often, it simply does not happen and the job runs on whatever is 
to hand. 

With a conversational AI assistant, the manager can ask one question and learn that five rolls are nominally in specification and two are trending towards the tolerance floor (and are predicted to fail mid-run). Additionally, a recently refurbished sister roll is sitting idle at press four. On the one hand, there is an unplanned stop and remake. On the other, a 30-second conversation prevents it entirely. 

“The Troika Management System consolidates measurement records across presses, plants and continents”

An AI layer is only ever as good as the underlying measurement data. If cell volumes are drawn from infrequent audits or measured inconsistently, conversational access simply delivers the wrong answers faster. The foundation remains regular, repeatable 3D measurement of volume and condition, recorded per roll, over time. AI does not replace good measurement. 
It gives that process a return on investment (ROI) lasting beyond the day of a written report. 

Troika Systems has built a working demonstration of exactly this – the Depth and Volume Expert AI (DAVE). It is a fleet-management assistant that sits on top of Troika measurement data and answers natural-language questions about roll condition, history and availability. 

What struck the company most during testing was the absence of any barrier. Navigating queries that once demanded a trained operator and a quiet afternoon is now open to anyone. Training becomes unnecessary as everyone knows how to ask a question.

Drawing on consolidated Troika Management System records, DAVE delivers clear insights into fleets far larger than any individual could hold in their head and it delivers it in seconds. As lead times shorten and quality expectations tighten, the converter who knows their fleet’s state in 30 seconds holds a distinct advantage over the one who discovers it mid-run. Early testing confirms the premise that the interest is not for more data. It is also for answers. A fleet has been talking for years. AI has created a way to talk back.