Frequently Asked Questions

The jump from pilot to production rarely fails because of the model — it fails because of missing operational rules. You need a clear 90-day production protocol with roles, go/no-go gates, and KPI thresholds. Typical steps: fix a production target, build an integration-ready pilot, start shadow operations, prepare operational approval, and make a go/no-go decision.

A realistic AI ROI doesn't come from a single percentage — it comes from a use-case-specific calculation model. For mid-sized companies, quality, downtime, and planning are usually the three ROI levers with the highest relevance. Blanket ROI promises are a red flag — serious consulting delivers a transparent calculation model with clear assumptions.

The fastest AI wins in industrial environments come from clearly scoped decisions with existing data. Typically fast: visual quality inspection, anomaly detection on equipment, and planning support for recurring processes. What matters is not technical complexity, but whether data is already being generated in operations.

The first decision is not tool selection — it's control requirements: What do you need to control yourself regarding data, runtime, and compliance? From there, Build vs Buy can be reliably resolved with a clear decision tree. Key criteria: data sovereignty, latency requirements, compliance mandates, and team capacity.

You don't need perfect data, but you do need a clear minimum standard. What matters is whether data for the target process is consistent, traceable, and temporally stable enough to support real decisions. A data quality scorecard helps assess the status objectively.

Yes. We process all data in accordance with GDPR. With locally operated solutions, your data never leaves your machine. For cloud-based projects, we exclusively use servers located in Germany and transparently document every data flow.

Yes — that's our core business. We develop AI solutions for specific industrial processes: from requirements analysis to architecture to production integration. No off-the-shelf software, but solutions designed for your data, processes, and quality requirements.

Integration follows a phased interface strategy with defined fallback modes. We create an integration plan that doesn't interrupt existing automation systems but extends them step by step. Key principle: every phase has its own independent fallback level.

For mid-sized companies, a lean team is enough: one internal lead (domain expert), one technical point of contact, and an external AI partner. What matters is not team size but clear roles and decision paths. We handle the technical implementation — you contribute domain expertise and process ownership.

With clear pilot exit criteria and a kill/scale decision rule defined before project start. The most common PoC traps: no defined success criterion, no operations owner after pilot end, and monitoring introduced too late. We build these gates into the project setup from day one.