Updated information about AI algorithms in anomaly detection

docs/categories-of-data-quality-checks/how-to-detect-anomaly-data-quality-issues.md

@@ -12,6 +12,15 @@ We can detect two types of anomalies in numeric columns with a data quality plat
 which are new minimum and maximum values, or we can detect if the typical values such as mean (average), median, or sum
 have changed.
 
+!!! note "Anomaly detection with AI"
+
+    The free version of DQOps uses a machine-learning algorithm that detects anomalies by calculating the Z-score over rolling quadrilles.
+    This algorithm does not detect seasonality and will not accurately detect seasonal patterns in your data, such as volume drops during weekends.
+
+    The commercial version of DQOps uses an advanced AI algorithm that considers seasonality and predicts anomalies accurately, reducing alert fatigue and minimizing false positives.
+
+    Please [get in touch with DQOps sales](https://dqops.com/contact-us/) for more information.
+
 ### Sample data for anomaly detection
 We will use a latitude column in a 311 Austin municipal services call history table,
 which stores the latitude of the reported incident's location.