Most AI programs produce a model that works and a business that doesn't change. The forecast lives in a dashboard nobody opens. The anomaly is detected, and nobody is told. We build AI that lands where the work already happens - a maintenance order in SAP, a quality hold on the line, a price recommendation in front of the person quoting. Deployed in your environment, integrated with your systems, operated after go-live.
Seven areas where we see AI change an operational number rather than a slide. Each one ends in a system your people already use.
Condition and failure models trained on your sensor history, maintenance records and inspection findings - including assets running well past their design life, and new plants where there is no failure history to learn from yet. The output is not a risk score on a dashboard. It is a maintenance notification in SAP PM or a maintenance plan in Dynamics 365, in the queue the technician already works from.
Visual inspection models and process-parameter analysis that catch defects at the station where they occur rather than at final inspection, when the value is already added. Integrated with your quality management so a detection becomes a hold, a rework instruction and a traceable record - with the audit trail your certification regime requires.
Energy and raw material are the two largest controllable cost lines in most process and metals operations, and almost nobody can attribute either to a specific cause in real time. We model the relationship between process parameters, input quality and output, then put the recommendation in front of the operator in the interface they already watch.
Demand forecasts that account for the seasonality, project cycles and lead-time volatility your planners already know about but cannot encode in a spreadsheet. Applied to spare parts across distributed sites, it is the difference between capital sitting on shelves and a line stopped waiting for a part.
In distribution and trading, margin is decided in the minutes before a quote goes out, on incomplete information, by a person under time pressure. We surface landed cost, current stock position, customer history and live market movement as a recommended price range inside the quoting process - not as a report reviewed at month end.
Material certificates, inspection reports, contracts, specifications and supplier documentation - arriving as scans, in inconsistent formats, in more than one language and often more than one script, in volumes no team can read. We extract the fields that matter, validate them against the order or the standard, and route exceptions to a person. The routine cases never reach a desk.
Decades of maintenance logs, shift handovers, incident reports and failure analyses hold the answer to most recurring problems, and are functionally unsearchable. We make them answerable in plain language, with every answer citing the record it came from. Particularly valuable where experienced staff are retiring or rotating out faster than knowledge is being transferred.
Straight answers to what industrial buyers actually ask us in the first conversation. If yours isn't here, ask us directly.
With an AI readiness assessment: two to three weeks, fixed scope. We map the data you actually have, score candidate use cases on value and feasibility, and hand back a prioritised roadmap with a business case for each. It is yours to keep regardless of what you decide next.
If a case is clearly worth doing, the next step is a pilot - typically eight to twelve weeks from kickoff to a result visible in your own system, with success defined as a change in an operational number agreed before we start. Scoped so that a decision not to continue costs a defined amount and nothing more.
Nobody's is. Data readiness is an outcome of the first phase, not a prerequisite for it. What matters is whether the data needed for one specific decision exists and is reachable - a much smaller question than whether your entire estate is ready. The assessment answers it in weeks, not quarters.
Not to start. A first use case can run against existing systems. But if you intend more than one, building each on its own extract creates a maintenance burden that eventually exceeds the value. We usually recommend a thin foundation in parallel with the first use case rather than ahead of it.
By defining success as a change in an operational number, agreed before kickoff, rather than as model accuracy. A model that is 94% accurate and changes nothing has failed. We scope the integration into the target system as part of the pilot, not as a follow-on project that needs separate approval.
Wherever your regulatory position and policy require it to stay - including entirely within your own environment. Data location, access control and retention are fixed in the design phase and documented before any data moves.
You do. Models, pipelines, documentation and code are your property and are handed over in a state your own team can operate. We are happy to run them for you under an SLA; that should be a choice, not a dependency.
Algorithm-based applications for highly sensitive market analyses.
+35% Forecast Accuracy
SAP S/4HANA implementation with 24×7 support.
Go-live in 6 Months
AI-based drone LiDAR systems as part of BIM.
40% Time Saving
SaaS platform for digital transformation in the automotive supplier industry.
Real-time Transparency
MS Dynamics CRM implementation with 24×7 support.
24/7 Support SLA
Shared service support with continuous improvement.
30% Efficiency GainAlgorithm-based applications for highly sensitive market analyses.
+35% Forecast AccuracySAP S/4HANA implementation with 24×7 support.
Go-live in 6 MonthsAI-based drone LiDAR systems as part of BIM.
40% Time SavingSaaS platform for digital transformation in the automotive supplier industry.
Real-time TransparencyMS Dynamics CRM implementation with 24×7 support.
24/7 Support SLAShared service support with continuous improvement.
30% Efficiency GainWe chose ZirkelTech because we needed a strategic partner with world-class expertise to work alongside our multi-national SAP S/4 team. We found ZirkelTech to be the most flexible, well-organized and trustworthy. We highly appreciate not just the subject matter knowledge, but also the enthusiasm and readiness consistently shown by the colleagues to discuss and try out novel approaches to solving complex problems.
ZirkelTech has supported us from the idea of a collaboration platform to its final marketability with high competence and reliability. Thanks to the partnership and agile cooperation, the idea was quickly transformed into a proof of concept. ZirkelTech continues to be our strategic partner for future projects, due to its commitment and flexibility.
With ZirkelTech we have found a competent partner who has reliably supported us in software development. Besides expert colleagues, who convinced us with their professional competence and creativity, Zirkel Tech is also characterized by an organized cooperation and goal-oriented communication. Also, for future projects ZirkelTech is the first choice for us.
DLC GmbH has experienced a significant boost in efficiency and project management thanks to the exceptional technical solutions provided by Zirkel Tech. Their team’s technical expertise, prompt response times, and unwavering dedication to our unique needs have been invaluable. We highly recommend their reliable and professional services to any business seeking top-notch IT support. Zirkel Tech has truly become an integral part of our success.
ZirkelTech was instrumental in building our Personal Finance platform, MoolahMate. They delivered seamless integration with another solution provider, robust security, and stayed on time and on budget. Their ability to listen, suggest ideas, and their professionalism make them an invaluable partner. A pleasure to work with.
From the very first discovery workshop, the ZirkelTech team demonstrated a rare combination of deep technical knowledge and genuine curiosity about our manufacturing operations. Rather than proposing a generic off-the-shelf solution, they invested heavily in understanding our specific OEE (Overall Equipment Effectiveness) measurement methodology, our rejection-tracking workflows, and our compliance obligations under IATF 16949. This groundwork paid dividends throughout the entire project.
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