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Anomaly AI

A traceable AI data analyst that turns plain-English database questions into live answers, charts, and saved insights.

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Live DB testedSQL traceabilityAuto insightsFree plan
TL;DR — our verdictUpdated August 2026 · 16 test artifacts

Excellent analyst copilot, with one real follow-up caveat.

Where it wins
  • You want non-technical users to ask plain-English questions of a live database and get back readable answers.
  • You want automatic charts, tables, and saved insights without designing the dashboard yourself.
  • You care about traceability: visible SQL, explicit calculations, and validation before publication.
Main limitation
  • You need referents in follow-up threads to stay perfectly stable when a phrase like 'top 3' is reused.
Pricing (verified plans)
Free $0 /monthPro $25 /monthAnalyst $90 /monthTeam $45 /seat/month
Strongest test artifacts
Result →

Our take

Anomaly AI is a strong fit for non-technical business users who want live database answers with visible SQL, automatic charts, and saved insights. It is especially strong on traceability and self-checking, but one follow-up chain showed that referents can drift when the same phrase, like "top 3," is reused later in the conversation.

Download video: Demo videoWatch video (streaming): Demo video0:00 — Introduction to Animally AI0:52 — Project Setup and Database Connection1:42 — Querying Data and Generating Insights3:03 — Automation and Advanced Features4:24 — Final Overview and Call to Action
Marketing homepage walkthrough showing Anomaly AI's positioning as an AI data analyst for recurring business reports.

In-Depth Review

Our detailed analysis of Anomaly AI — features, performance, and real-world testing.

AD
AI Demos Team
Expert Reviewer
Verified Review

Feature-by-Feature Breakdown

Natural-Language Database Q&A
Strong
▾
Test Summary
Feature tested: Natural-Language Database Q&A
Result: Passed — Strong

Feature tested: Natural-Language Database Q&A

Result: Passed

Verdict: Strong

Expected behavior: Answers business questions against a live connected database in plain English, returning readable narratives, tables, and summaries without requiring SQL. The proofs exercised live database questions rather than a single fixed query shape.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Returned a plain-language answer saying no customers were created in the last 90 days versus 13 in the previous 90 days, and showed the empty current-period customer list. — input1-answer-text-and-verification.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Returned a plain-language answer saying no customers were created in the last 90 days versus 13 in the previous 90 days, and showed the empty current-period customer list. — input1-answer-text-and-verification.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Created the reusable customer_value_ranking transformation and spelled out the ranking assumptions. — d3c4dc7e53c64af199ab4b67e54dad75.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Created the reusable customer_value_ranking transformation and spelled out the ranking assumptions. — d3c4dc7e53c64af199ab4b67e54dad75.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Returned a 93-order summary across seven current stages, with the stage counts listed in natural language. — input3-main-summary-and-stage-list.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Returned a 93-order summary across seven current stages, with the stage counts listed in natural language. — input3-main-summary-and-stage-list.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Strong across all three tasks; it consistently turned plain-English business questions into correct live-database answers.

Answers business questions against a live connected database in plain English, returning readable narratives, tables, and summaries without requiring SQL. The proofs exercised live database questions rather than a single fixed query shape.

text
Show all customers created in the last 90 days. How does new customer acquisition compare to the previous 90 days?
↓
image
Output artifact for "Natural-Language Database Q&A" test: Returned a plain-language answer saying no customers were created in the last 90 days versus 13 in the previous 90 days, and showed the empty current-period customer list., input1-answer-text-and-verification.png
Returned a plain-language answer saying no customers were created in the last 90 days versus 13 in the previous 90 days, and showed the empty current-period customer list.
text
Who are my best customers — the ones who order the most and spend the most?
↓
image
Output artifact for "Natural-Language Database Q&A" test: Created the reusable customer_value_ranking transformation and spelled out the ranking assumptions., d3c4dc7e53c64af199ab4b67e54dad75.png
Created the reusable customer_value_ranking transformation and spelled out the ranking assumptions.
text
How many orders do we have at each stage right now?
↓
image
Output artifact for "Natural-Language Database Q&A" test: Returned a 93-order summary across seven current stages, with the stage counts listed in natural language., input3-main-summary-and-stage-list.png
Returned a 93-order summary across seven current stages, with the stage counts listed in natural language.
Bottom Line
Strong across all three tasks; it consistently turned plain-English business questions into correct live-database answers.
From our researchQuery Live Databases Using Plain English with AI
Derived Table Creation and Reuse
Strong
▾
Test Summary
Feature tested: Derived Table Creation and Reuse
Result: Passed — Strong

Feature tested: Derived Table Creation and Reuse

Result: Passed

Verdict: Strong

Expected behavior: Creates named derived tables from exploratory analysis and lets later follow-up questions reuse them as persistent analysis assets. The evidence shows one exploratory result being turned into something reusable across later questions.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Returned a customer ranking table that split the answer into order frequency, total spend, average order value, and separate ranks for both dimensions. — a5acbd76bf834a3f88b1d6461f00f8b7.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Returned a customer ranking table that split the answer into order frequency, total spend, average order value, and separate ranks for both dimensions. — a5acbd76bf834a3f88b1d6461f00f8b7.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Reused the saved ranking context to answer a follow-up about unpaid orders among the selected top three customers. — input2-followup1-insights-top3-payment-status-table.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Reused the saved ranking context to answer a follow-up about unpaid orders among the selected top three customers. — input2-followup1-insights-top3-payment-status-table.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Built on the same customer-ranking workflow to summarize payment methods for the selected high-value customers. — input2-followup2-insights-payment-chart-and-table.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Built on the same customer-ranking workflow to summarize payment methods for the selected high-value customers. — input2-followup2-insights-payment-chart-and-table.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Strong; it can turn one exploratory result into a reusable analysis object and keep using it in later questions.

Creates named derived tables from exploratory analysis and lets later follow-up questions reuse them as persistent analysis assets. The evidence shows one exploratory result being turned into something reusable across later questions.

text
Who are my best customers — the ones who order the most and spend the most?
↓
image
Output artifact for "Derived Table Creation and Reuse" test: Returned a customer ranking table that split the answer into order frequency, total spend, average order value, and separate ranks for both dimensions., a5acbd76bf834a3f88b1d6461f00f8b7.png
Returned a customer ranking table that split the answer into order frequency, total spend, average order value, and separate ranks for both dimensions.
text
For the top 3 from that list — do any of them have unpaid orders?
↓
image
Output artifact for "Derived Table Creation and Reuse" test: Reused the saved ranking context to answer a follow-up about unpaid orders among the selected top three customers., input2-followup1-insights-top3-payment-status-table.png
Reused the saved ranking context to answer a follow-up about unpaid orders among the selected top three customers.
text
What payment methods do these top 3 usually use?
↓
image
Output artifact for "Derived Table Creation and Reuse" test: Built on the same customer-ranking workflow to summarize payment methods for the selected high-value customers., input2-followup2-insights-payment-chart-and-table.png
Built on the same customer-ranking workflow to summarize payment methods for the selected high-value customers.
Bottom Line
Strong; it can turn one exploratory result into a reusable analysis object and keep using it in later questions.
From our researchQuery Live Databases Using Plain English with AI
Query Transparency and Validation
Strongest
▾
Test Summary
Feature tested: Query Transparency and Validation
Result: Passed — Strongest

Feature tested: Query Transparency and Validation

Result: Passed

Verdict: Strongest

Expected behavior: Shows the generated SQL, explains calculations in plain language, and surfaces validation or integrity checks before publishing an insight. The examples included both human-readable explanations and raw SQL/verification steps.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): The acquisition summary modal explains the calculation in plain mathematical notation on the Meaning tab. — input1-acquisition-summary-meaning-tab.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): The acquisition summary modal explains the calculation in plain mathematical notation on the Meaning tab. — input1-acquisition-summary-meaning-tab.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): The same modal shows the raw SQL for the current and previous 90-day windows and the percentage-change calculation. — input1-acquisition-summary-sql-query-tab.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): The same modal shows the raw SQL for the current and previous 90-day windows and the percentage-change calculation. — input1-acquisition-summary-sql-query-tab.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): The workflow shows SQL exploration plus a validation pass that checks current order status against status history before the summary is published. — input3-main-chat-sql-and-integrity-check.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): The workflow shows SQL exploration plus a validation pass that checks current order status against status history before the summary is published. — input3-main-chat-sql-and-integrity-check.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: This was the strongest explanation surface in the test set: answers were traceable in both business language and raw SQL, with validation shown before publication.

Shows the generated SQL, explains calculations in plain language, and surfaces validation or integrity checks before publishing an insight. The examples included both human-readable explanations and raw SQL/verification steps.

text
Show all customers created in the last 90 days. How does new customer acquisition compare to the previous 90 days?
↓
image
Output artifact for "Query Transparency and Validation" test: The acquisition summary modal explains the calculation in plain mathematical notation on the Meaning tab., input1-acquisition-summary-meaning-tab.png
The acquisition summary modal explains the calculation in plain mathematical notation on the Meaning tab.
text
Show all customers created in the last 90 days. How does new customer acquisition compare to the previous 90 days?
↓
image
Output artifact for "Query Transparency and Validation" test: The same modal shows the raw SQL for the current and previous 90-day windows and the percentage-change calculation., input1-acquisition-summary-sql-query-tab.png
The same modal shows the raw SQL for the current and previous 90-day windows and the percentage-change calculation.
text
How many orders do we have at each stage right now?
↓
image
Output artifact for "Query Transparency and Validation" test: The workflow shows SQL exploration plus a validation pass that checks current order status against status history before the summary is published., input3-main-chat-sql-and-integrity-check.png
The workflow shows SQL exploration plus a validation pass that checks current order status against status history before the summary is published.
Bottom Line
This was the strongest explanation surface in the test set: answers were traceable in both business language and raw SQL, with validation shown before publication.
From our researchQuery Live Databases Using Plain English with AI
Automated Insight and Chart Generation
Strong
▾
Test Summary
Feature tested: Automated Insight and Chart Generation
Result: Passed — Strong

Feature tested: Automated Insight and Chart Generation

Result: Passed

Verdict: Strong

Expected behavior: Automatically turns query results into saved insight cards with relevant charts and tables, without the user designing the visualization first. The tests produced multiple visualization types, including bar, scatter, donut, and table-based outputs.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Created a persistent acquisition insight with a bar chart and an empty current-period customer table. — input1-insights-auto-dashboard-chart.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Created a persistent acquisition insight with a bar chart and an empty current-period customer table. — input1-insights-auto-dashboard-chart.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Auto-generated a scatter plot of order frequency versus total spend to show how frequency and value relate. — input2-main-scatter-plot-freq-vs-spend.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Auto-generated a scatter plot of order frequency versus total spend to show how frequency and value relate. — input2-main-scatter-plot-freq-vs-spend.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Produced a donut chart for delivered versus cancelled order share, with both the subset split and the share of all orders explained. — input3-followup1-insights-donut-chart.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Produced a donut chart for delivered versus cancelled order share, with both the subset split and the share of all orders explained. — input3-followup1-insights-donut-chart.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Created a saved insight table for the two pending-but-paid orders and included delivery dates in the output. — input3-followup2-insights-table-with-delivery-dates.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Created a saved insight table for the two pending-but-paid orders and included delivery dates in the output. — input3-followup2-insights-table-with-delivery-dates.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Strong; the tool consistently picked the right visual for each question and saved the result as a reusable insight.

Automatically turns query results into saved insight cards with relevant charts and tables, without the user designing the visualization first. The tests produced multiple visualization types, including bar, scatter, donut, and table-based outputs.

text
Show all customers created in the last 90 days. How does new customer acquisition compare to the previous 90 days?
↓
image
Output artifact for "Automated Insight and Chart Generation" test: Created a persistent acquisition insight with a bar chart and an empty current-period customer table., input1-insights-auto-dashboard-chart.png
Created a persistent acquisition insight with a bar chart and an empty current-period customer table.
text
Who are my best customers — the ones who order the most and spend the most?
↓
image
Output artifact for "Automated Insight and Chart Generation" test: Auto-generated a scatter plot of order frequency versus total spend to show how frequency and value relate., input2-main-scatter-plot-freq-vs-spend.png
Auto-generated a scatter plot of order frequency versus total spend to show how frequency and value relate.
text
What percentage of our orders were successfully delivered vs cancelled?
↓
image
Output artifact for "Automated Insight and Chart Generation" test: Produced a donut chart for delivered versus cancelled order share, with both the subset split and the share of all orders explained., input3-followup1-insights-donut-chart.png
Produced a donut chart for delivered versus cancelled order share, with both the subset split and the share of all orders explained.
text
Are there any orders that are pending but already paid?
↓
image
Output artifact for "Automated Insight and Chart Generation" test: Created a saved insight table for the two pending-but-paid orders and included delivery dates in the output., input3-followup2-insights-table-with-delivery-dates.png
Created a saved insight table for the two pending-but-paid orders and included delivery dates in the output.
Bottom Line
Strong; the tool consistently picked the right visual for each question and saved the result as a reusable insight.
From our researchQuery Live Databases Using Plain English with AI
Threaded Conversational Analysis
Mixed
▾
Test Summary
Feature tested: Threaded Conversational Analysis
Result: Partial — Mixed

Feature tested: Threaded Conversational Analysis

Result: Partial

Verdict: Mixed

Expected behavior: Supports follow-up questions that build on earlier results and maintains conversational context across a thread. The evidence covered multi-turn analysis chains, including cases with referent drift in one thread.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Answered the first follow-up using the saved ranking context and showed one unpaid order for Rahul Sharma. — input2-followup1-chat-unpaid-answer.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Answered the first follow-up using the saved ranking context and showed one unpaid order for Rahul Sharma. — input2-followup1-chat-unpaid-answer.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Answered the next follow-up with payment-method breakdowns, but the referenced 'top 3' set had shifted to a different trio. — input2-followup2-chat-payment-methods.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Answered the next follow-up with payment-method breakdowns, but the referenced 'top 3' set had shifted to a different trio. — input2-followup2-chat-payment-methods.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Handled the month-over-month follow-up by comparing April and May 2026 while preserving the pending-versus-paid breakdown. — input3-followup3-chat-cohort-comparison.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Handled the month-over-month follow-up by comparing April and May 2026 while preserving the pending-versus-paid breakdown. — input3-followup3-chat-cohort-comparison.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Mixed: follow-up analysis is often solid, but the best-customers chain showed a real referent drift when the same phrase was reused.

Supports follow-up questions that build on earlier results and maintains conversational context across a thread. The evidence covered multi-turn analysis chains, including cases with referent drift in one thread.

text
For the top 3 from that list — do any of them have unpaid orders?
↓
image
Output artifact for "Threaded Conversational Analysis" test: Answered the first follow-up using the saved ranking context and showed one unpaid order for Rahul Sharma., input2-followup1-chat-unpaid-answer.png
Answered the first follow-up using the saved ranking context and showed one unpaid order for Rahul Sharma.
text
What payment methods do these top 3 usually use?
↓
image
Output artifact for "Threaded Conversational Analysis" test: Answered the next follow-up with payment-method breakdowns, but the referenced 'top 3' set had shifted to a different trio., input2-followup2-chat-payment-methods.png
Answered the next follow-up with payment-method breakdowns, but the referenced 'top 3' set had shifted to a different trio.
text
Compare that to last month — same breakdown, I want to see if things have improved or got worse.
↓
image
Output artifact for "Threaded Conversational Analysis" test: Handled the month-over-month follow-up by comparing April and May 2026 while preserving the pending-versus-paid breakdown., input3-followup3-chat-cohort-comparison.png
Handled the month-over-month follow-up by comparing April and May 2026 while preserving the pending-versus-paid breakdown.
Bottom Line
Mixed: follow-up analysis is often solid, but the best-customers chain showed a real referent drift when the same phrase was reused.
From our researchQuery Live Databases Using Plain English with AI

Plans

Monthly billing shown in the screenshot.

TESTED
Free
$0 /month
15 free credits. Enough to try core analysis and dashboard workflows. Current Plan.
Pro
$25 /month
Most popular. 7 days free with 200 credits, then $25/month.
Analyst
$90 /month
4,000 credits/month.
Team
$45 /seat/month
Enterprise
Custom
Contact Sales.
✓ Use This If
●You want non-technical users to ask plain-English questions of a live database and get back readable answers.
●You want automatic charts, tables, and saved insights without designing the dashboard yourself.
●You care about traceability: visible SQL, explicit calculations, and validation before publication.
✕ Skip This If
●You need referents in follow-up threads to stay perfectly stable when a phrase like 'top 3' is reused.
●You need every order-level exception table to include related names automatically without changing the query.
business-marketingother-business-marketingtextOther
Yes. In the acquisition test it showed SQL inline and also opened an Acquisition summary modal with both a plain-language Meaning tab and a raw Code tab. In the order-pipeline test it also exposed SQL exploration plus a validation step before publishing the insight.
It generated a bar chart for customer acquisition, a scatter plot for order frequency versus spend, a donut chart for delivered versus cancelled orders, and several table-based insight cards. The outputs appeared as saved Insight views with Add to Dashboard controls.
Yes, but with a caveat. The order-pipeline chain held context well, and the acquisition follow-up also worked. In the best-customers chain, however, the meaning of 'top 3' changed between two consecutive follow-ups without being called out.
The Free plan was shown as the current plan at $0/month with 15 free credits. The billing card also said it was enough to try core analysis and dashboard workflows, and the pricing screenshot showed all data sources supported and live chat support on the Free tier.
Yes. The report shows separate validation passes, plus explicit integrity checks. For example, the order-stage workflow checked whether current order statuses matched the latest status-history entries before publishing the summary.
Yes. The report says PostgreSQL was not listed on the vendor site, but the benchmark successfully ran against a live PostgreSQL database during testing.

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