Anomaly AI in AI Database Agents

Anomaly AI showed the questions asked across two accounts and attributed them to the right account. The owner's usage view contained five insights for five questions: three from the owner and two from the member.

Anomaly AI handled this trend/comparison request with a visual, not just rows. The request asked only for monthly counts, and the answer produced a month-axis bar chart with July 18, August 27, and September 3.

Anomaly AI presented a headline figure alongside its breakdown. It reported that August revenue fell 19.14% from July and accompanied the answer with a monthly comparison and a category chart.

Anomaly AI changed the July-versus-August revenue comparison into a bar chart while preserving the underlying answer. July remained $10,877.75 and August $8,795.45; the revenue change, average order values and category contributions also stayed unchanged.

Anomaly AI suggested a relevant follow-up after answering a question. For an August revenue answer, it offered a July comparison that the same database could support.

Anomaly AI chose a comparison rather than clarifying an ambiguous follow-up. It compared five customers on order count, total spend and average order.

Anomaly AI passes this narrowing-follow-up scenario. The second turn adds Texas while keeping the earlier 90-day window, and the result shows only the three matching orders while the outside-window Texas order stays absent.

Anomaly AI could not restrict a user's table access without deleting the table from that user's project. For the same membership question, it returned Plus to the owner and no tier to the member.

Anomaly AI said when the connected data could not answer a question. Asked about five-star reviews, it explained that the data held orders and support tickets but no reviews or star ratings, and gave no count.

Anomaly AI interpreted gift vouchers as gift-card payments and returned the matching orders. It listed 14 orders against a reference count of 14, including two cancelled orders.

Anomaly AI refreshed the saved report from 24 to 27 orders, matching the new database count of 27. It preserved the original reporting period after three orders were added within it.

Anomaly AI used a trusted question-and-query example to answer both the original wording and a rephrased question. Both answers returned the verified result of 42 orders, compared with 38 before training on a different definition of success.