With the release of DQC’s AI agents for data improvement, customers can now improve their data automatically, saving hours of manual effort each week and enabling more reliable, higher-quality data-driven work across systems.

Key capabilities
DQC’s AI agents can be used across a range of data-cleansing and enrichment tasks, including:
Correcting and completing addresses using internal, external, and web sources (e.g., OpenCage)
Identifying and merging duplicates, even across datasets and naming variants
Applying industry classification standards such as ETIM, ECLASS, GS1
Standardizing formats such as units, dates, and telephone numbers
Enriching missing or incomplete records with contextual external data
Executing complex operations using custom Python logic where needed
Plus many additional functionalities that are either in private-beta mode, or company-specific (don’t hesitate to reach out to your DQC contact!)
These tasks can be configured individually or combined within DQC workflows. The AI agent operates based on users’ input and adapts to specific project requirements.
