We make hidden
production potential visible.
You first see the benefits and then decide whether we implement it productively – secured by our money-back guarantee.
Who is this for?
When our offer is a good fit for you.
When machines produce data nobody evaluates, wear only shows when it's too late, or unplanned downtime sets the pace – there is measurable savings potential waiting to be uncovered.
Machine data goes unused
Sensors and controllers continuously produce data — but it isn't systematically evaluated or used for decision-making.
Downtime comes as a surprise
Machine failures are fixed reactively rather than prevented proactively — maintenance happens after the damage, not before.
Transparency & quality are critical
Lead times, back-and-forth, or error rates are noticeable — but without clean data they can't be measured and managed reliably.
Measurable impact matters
You want clear impact (KPIs, process metrics) — with a traceable effort/benefit rationale, not just an 'idea.'
Not sure? In the Potential Check we assess data readiness, effort, and expected impact — before you invest.
Our approach
Our 4-step plan
Clearly structured and low-risk – we deliver measurable results in 4 steps.
Potential Check
We analyze your process, data sources, and KPI goals — free of charge/no obligation — and identify concrete Machine Learning potentials. Result: prioritized use cases, data gaps, an effort/benefit assessment, and a clear roadmap.
Data foundation & KPI definition
We identify relevant data sources (e.g., machine data, sensors, Excel), establish clear definitions, and verify data quality. Result: a reliable data foundation for the selected use case — clearly documented.
Pilot with measurable impact
We train and deploy the first ML model (e.g., wear prediction, anomaly detection, or downtime alert) and measure its impact against KPIs. Result: a working solution with a clear before/after measurement.
Commissioning & handover
We put the solution into operation and stabilize it. Then we hand over transparently and fully documented (no black box) — with the option to scale to additional use cases.
Money-back guarantee: If we don't meet your expectations, we refund 50% of the implementation fee.
Flat-rate pricing would be misleading, as the effort depends heavily on your data situation and integrations.
What we do know: especially in mid-sized companies, use cases that pay for themselves quickly are usually found fast. To prove it, we invest upfront: in the free Potential Check, we calculate in black and white when the investment will pay off.
Only once this business case convinces you do we start implementation. And even then we have you covered: if targets are unexpectedly not met, our 50% money-back guarantee applies.
You receive a prioritized use-case list, an assessment of your data situation (gaps/risks), defined target KPIs, and a roadmap with effort and timeline. To ensure measurability, we establish a baseline (current state) and measure impact in the pilot with a clear before/after comparison.
Typical sources include ERP, DMS, CRM, Excel/CSV, email inboxes, ticketing systems, and production/quality data (e.g., MES). Perfect data isn't required — we assess quality and linkability and show what can be done immediately and what preparation work makes sense.
Usually you'll need a business owner and a few short workshops to clarify processes and KPIs, plus IT support for access/interfaces. We keep the effort lean and work iteratively so you see value quickly without launching a large internal project.
Changes are normal. We evaluate new requirements transparently against effort and expected impact and prioritize together. Small adjustments flow in continuously; larger changes are handled as a documented scope update so budget and timeline remain controllable.
It depends heavily on your data situation. If machine data is already structured and available, we can reach first measurable results quickly in the pilot. If preparation is needed, we invest that time properly — so the result holds. Our standard: no promises that aren't backed up, just a clear before/after comparison.
The best fit is where machines already produce data but nobody evaluates it systematically. Classics: tool wear that announces itself in sensor data, patterns before machine failures, or quality deviations that would be detectable early. In the Potential Check we look together at where those levers are in your production.
We work strictly GDPR-compliant and follow the principle of data minimization. Confidentiality is standard: we can work under an NDA and with clear access controls. We only process what's needed for the use case and align processing and permissions upfront. Hosting can be on-prem or in your cloud, depending on your requirements.
We deliver full transparency: documentation, data models, and source code are handed over as agreed — no black box. After commissioning we don't leave you on your own: we stabilize operations and offer support or further development on request — flexibly as a package or on-demand.
Still have questions?
We're happy to help if you couldn't find the right answer. Let's have a quick chat.