Tobias Wrenfeld
Researcher and writer focused on how AI systems get applied — or fail to get applied — inside physical industrial environments. The work here covers implementation patterns, tooling decisions, and the operational gaps that rarely appear in vendor documentation.
Background and approach
"Most AI projects in industry stall not because the algorithms are wrong, but because the integration assumptions were never tested against real plant conditions."
Tobias spent several years working adjacent to manufacturing operations — first in process engineering, then in technology evaluation roles where the gap between vendor claims and shop-floor reality became a recurring theme.
Since founding recoveryb in 2019, the focus has been on documenting what actually happens when AI tools meet legacy infrastructure, inconsistent sensor data, and teams without dedicated ML staff. The writing draws on direct observation, operator interviews, and hands-on testing of deployment tooling.
The site does not advocate for particular vendors or platforms. Articles describe observed outcomes, note where evidence is thin, and flag when a technique requires conditions that most facilities cannot meet.
Areas of focus
- Predictive maintenance Sensor selection, model drift, and when rule-based systems outperform ML
- Edge deployment Running inference at the machine level without cloud dependency
- Data quality in OT environments Missing values, clock drift, and labeling problems specific to industrial data
- Adoption barriers Organizational and technical reasons AI pilots do not reach production
Articles are published when there is something specific to document — not on a fixed schedule. Subscribe to receive updates when new material is added.
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