LIBERO AI SOLUTIONS: DATA QUALITY
Enrich - Data Quality Assistant
Make every record count — whether it was entered today or ten years ago
Ideal for: Any team sitting on years of inconsistent, unstructured, or incomplete operational data — and anyone still capturing that data poorly today.
Poor data quality is the single biggest reason AI and analytics initiatives stall. Enrich uses AI to clean, standardise, and add structure to the free-text, high-volume records every heavy industry organisation depends on — incident and hazard reports, maintenance work orders, inspection logs, and compliance records.
It works two ways: correcting and completing records the moment they're entered, and reprocessing years of historical data that's been written off as "too broken" to analyse. Either way, the result is the same — data your team can finally trust enough to act on.
Key benefits
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AI-powered standardisation, categorisation, and enrichment of existing records
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Works on both real-time capture and legacy/historical data
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Natural Language Processing for free-text fields (incident narratives, work order descriptions)
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Reduces manual data entry effort and improves record completeness
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Makes existing data AI-ready without large-scale data migration
THE POWER OF ENRICH
The foundational technology of Enrich is IBM's watsonx. This enterprise-grade technology provides the integrated ecosystem for developing, training and deploying AI-driven solutions and enables the following features:
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Seamless integration with enterprise systems
Provides an intuitive interface that guides users through each step of data entry.
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Enhanced legacy data
Identifies and corrects categorisations, can add new fields and complete based on record, significantly boosting accuracy and consistency of legacy data.
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Real-time Language Processing
Monitors user input for clarity and prompts for additional details for corrections to ensure high-quality, actionable information.
Guided data entry
Offers targeted suggestions to help users capture all relevant details while reducing the burden of manual input.
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Contextual hazard detection
AI-driven analysis is used to identify concerns - such as changing operational conditions or emerging risk and behaviours.
Scalable, enterprise-ready platform
Smooth deployment and expansion to new sites or use cases, ensuring robust AI-driven support.
DATA QUALITY EXAMPLE IN NUMBERS
The quality of safety reports for a typical mining client shows:
38%
of Safety Reports
are considered poor quality
84%
of poor quality reports had no identifiable hazard
26%
of Observation Reports were categorised incorrectly
