The situation
A major European refinery needs to prepare data for every RBI update. The integrity engineer gathers thickness readings from recent PDF reports and Excel files, transfers values manually into corrosion tracking spreadsheets, calculates corrosion rates by hand, and cross-references maintenance records to check whether repairs have broken the data history. When a value looks implausible, they contact the inspection department or external contractor and wait. By the time the data is assembled and verified, the actual risk assessment has been compressed into a fraction of the available time. It is made on data whose quality has been checked manually, inconsistently, and under pressure.
What changed
deeplify connects inspection history, asset hierarchy, and maintenance records into one intelligence layer. New findings are automatically linked to the corresponding CMLs and prior measurement history. Corrosion rates are calculated automatically. The system runs plausibility checks at the raw data level and flags anomalies for engineer review, surfacing the source data directly rather than requiring a contractor call. For the RBI update, the engineer opens one view: all relevant assets, current corrosion rates, data quality scores, and flagged findings ready for judgment.
What this enabled
Integrity engineers spend their time on risk assessment, not data collection. RBI updates are based on the most complete, highest-quality inspection data available. When a value is questioned, the raw data is immediately at hand.