DataGroomr’s AI features are designed to reduce manual work, improve accuracy, and accelerate the entire data quality process. By combining smart matching models, AI-driven recommendations, and natural-language assistance, teams can focus on strategy and validation instead of repetitive setup or decision-making. Below is an overview of the key areas where AI adds value and how each contributes to productivity.
Managing Matching Models
Matching models are the core of how DataGroomr detects duplicates. There are two main types: classic models, which use rule-based comparisons, and machine learning models, which learn from examples to recognize patterns. Within the settings, you can create, edit, and assign models to datasets. When building a model, you choose which fields to consider, decide on comparison types such as exact or similar, and apply advanced options like field weights, transforms, synonyms, and rules for handling blanks or abbreviations.
On initial onboarding or when customizing, for faster setup, the AI Assist option can suggest fields to include based on your data. Models can be cloned to make variations so you can retrain without losing older configurations. Once created, models are assigned to datasets or set as defaults, ensuring consistent logic is applied across all deduplication work. This flexibility allows you to fine-tune accuracy, adapt quickly as data structures evolve, and maintain reliable duplicate detection at scale.
Training Machine Learning Models
Machine learning matching models can be trained either manually or with the help of the AI Assistant. In manual mode, you label record pairs as duplicates or not, teaching the model to recognize patterns in your data. The AI Assistant, on the other hand, automates much of this training and fine-tuning, reducing the effort required when working with large or messy datasets.
Training includes a coverage metric that shows how much of your data the model can confidently assess. As you add more labeled examples, the coverage improves, giving you a clear sense of when the model is ready for use. This combination of automation, manual feedback, and measurable coverage ensures your models become more accurate without requiring repeated complex setups.
AI Recommendations for Duplicate Resolution
Once models have identified groups of potential duplicates, AI Recommendations streamline the next step: deciding what to do with those groups. Instead of manually inspecting every set, you can request AI suggestions for single or multiple groups at once. The system proposes actions such as merge, split, link, or hold for manual review, along with reasoning that explains why it recommends that choice.
Recommendations can also be filtered by match confidence, group size, or model used, which makes it easier to focus on the most important cases first. This capability speeds up decision-making, reduces errors, and allows large numbers of duplicates to be handled efficiently without overwhelming users.
AI Assistant in Rule Designer
The AI Assistant inside the rule designer further accelerates setup by letting you describe the logic you want in plain language. Instead of building complex filters or transformations by hand, you can type instructions that reference fields, and the assistant generates the corresponding rule blocks for you. These rules can then be reviewed, adjusted, or regenerated as needed.
This not only saves time but also lowers the barrier for non-technical users, who can rely on natural-language prompts rather than having to understand every detail of rule syntax. It also encourages experimentation: users can try different approaches quickly and refine them until the logic matches business needs.
Combined Productivity Gains
Together, these AI features deliver major productivity improvements. Managing and training matching models ensures high accuracy and adaptability, while AI Recommendations handle decision-making at scale so users can focus only on ambiguous or high-value cases. The Rule Designer Assistant speeds up rule creation and makes advanced configuration more accessible.
The result is a streamlined data quality process where setup is faster, duplicate detection is more accurate, and day-to-day work requires less manual effort. By offloading repetitive tasks to AI, DataGroomr helps teams maintain clean Salesforce data more efficiently and with greater confidence.
Learn more: Manage Matching Models Training Machine Learning Matching Model Get AI Recommendations for Duplicate Resolution AI Assistant in Rule Designer