Predictive Filtering is a brand-new AI feature in Synerise Search, designed to significantly improve how search results are filtered and displayed—without relying on dozens of manual query rules.
This feature uses a predictive model to assign the most relevant category to each search query. That means when someone types "shoes", the system can narrow results automatically to “sports shoes”, based on behavioral data and patterns.
For every prediction in addition, you’ll get:
1. Reduces complexity
Predictive Filtering significantly reduces the need for manually configuring complex query rules for each search term.
2. Improves precision
The model narrows down results based on real-world user behavior, reducing noise and making the search experience more accurate. This leads to higher engagement and better conversion in commerce use cases.
3. Saves time
Teams no longer need to define and maintain many category-matching rules. Automated prediction allows for faster deployment of new indices and continuous improvements without deep manual tuning.
Predictive Filtering is powered by a Synerise AI model that uses historical search behavior, click data, and indexing structure to calculate category relevance on the fly. Each query is evaluated in context, and predictions can be adapted based on filter types—stricter logic for static filters, looser thresholds for flexible filters. This approach is especially effective in large catalogs with many overlapping or ambiguous terms, helping improve search relevance and make Synerise AI Search more scalable in enterprise environments.
Predictive Filtering is a brand-new AI feature in Synerise Search, designed to significantly improve how search results are filtered and displayed—without relying on dozens of manual query rules.
This feature uses a predictive model to assign the most relevant category to each search query. That means when someone types "shoes", the system can narrow results automatically to “sports shoes”, based on behavioral data and patterns.
For every prediction in addition, you’ll get:
1. Reduces complexity
Predictive Filtering significantly reduces the need for manually configuring complex query rules for each search term.
2. Improves precision
The model narrows down results based on real-world user behavior, reducing noise and making the search experience more accurate. This leads to higher engagement and better conversion in commerce use cases.
3. Saves time
Teams no longer need to define and maintain many category-matching rules. Automated prediction allows for faster deployment of new indices and continuous improvements without deep manual tuning.
Predictive Filtering is powered by a Synerise AI model that uses historical search behavior, click data, and indexing structure to calculate category relevance on the fly. Each query is evaluated in context, and predictions can be adapted based on filter types—stricter logic for static filters, looser thresholds for flexible filters. This approach is especially effective in large catalogs with many overlapping or ambiguous terms, helping improve search relevance and make Synerise AI Search more scalable in enterprise environments.
