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AI for Database

Plain-English live database querying with inline SQL, charts, follow-ups, and cost visibility.

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Live PostgreSQLInline SQL traceFollow-up chatFree plan tested
TL;DR — our verdictUpdated August 2026 · 19 test artifacts

Strong analytics surface, but the prose layer needs verification.

Where it wins
  • You want to query a live database in plain English with no SQL.
  • You need inline SQL plus charts/tables for business stakeholders.
  • You want follow-up questions to stay in context.
Main limitation
  • You need the prose summary to be perfect without checking the underlying table.
Pricing (verified plans)
Free $0 / monthPro $19 / monthEnterprise Custom
Strongest test artifacts
RESULT →

Our take

AI for Database is a strong fit when you want self-serve live-database answers with visible SQL, charts, follow-ups, and unusually strong cost transparency. The tradeoff is that the natural-language summaries and some chart choices need checking, because the report found verified arithmetic, attribution, and labeling issues even when the SQL and tables were correct.

Download video: Demo videoWatch video (streaming): Demo video0:00 — Introduction to AI for Database Tool0:41 — Dashboard Creation and Customization1:42 — Workflow Automation and Database Connection2:53 — Settings, AI Model Selection, and Query Execution4:14 — Advanced Analytics and Performance Monitoring5:40 — Interactive Features and Final Showcase
Screen recording that moves from the marketing page into the live app, then into a generated chart-and-trace result page.

In-Depth Review

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

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Verified Review

Feature-by-Feature Breakdown

Natural-Language Database Querying
▾
Test Summary
Feature tested: Natural-Language Database Querying
Result: Passed

Feature tested: Natural-Language Database Querying

Result: Passed

Expected behavior: Accepts natural-language questions against the live PostgreSQL dataset and returns readable answers grounded in the database rather than requiring SQL editing. It handled a simple acquisition question, a ranked best-customers analysis, and an order-status breakdown.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Answered the acquisition question directly from the live database, reporting 0 new customers in the last 90 days versus 13 in the previous 90 days. — input1-answer-and-diagnostic-chain.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Answered the acquisition question directly from the live database, reporting 0 new customers in the last 90 days versus 13 in the previous 90 days. — input1-answer-and-diagnostic-chain.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 two ranked tables for total spending and order frequency, and identified Rahul Sharma as the customer who led both dimensions. — input2-main-two-ranking-tables.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Returned two ranked tables for total spending and order frequency, and identified Rahul Sharma as the customer who led both dimensions. — input2-main-two-ranking-tables.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): Summarized all 93 orders across seven current statuses with a table and pie chart. — input3-main-status-breakdown-table-and-pie.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Summarized all 93 orders across seven current statuses with a table and pie chart. — input3-main-status-breakdown-table-and-pie.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Strong: it handled simple, analytical, and grouped queries against the live database without requiring SQL.

Accepts natural-language questions against the live PostgreSQL dataset and returns readable answers grounded in the database rather than requiring SQL editing. It handled a simple acquisition question, a ranked best-customers analysis, and an order-status breakdown.

INPUT
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 Querying" test: Answered the acquisition question directly from the live database, reporting 0 new customers in the last 90 days versus 13 in the previous 90 days., input1-answer-and-diagnostic-chain.png
Answered the acquisition question directly from the live database, reporting 0 new customers in the last 90 days versus 13 in the previous 90 days.
INPUT
Who are my best customers — the ones who order the most and spend the most?
↓
image
Output artifact for "Natural-Language Database Querying" test: Returned two ranked tables for total spending and order frequency, and identified Rahul Sharma as the customer who led both dimensions., input2-main-two-ranking-tables.png
Returned two ranked tables for total spending and order frequency, and identified Rahul Sharma as the customer who led both dimensions.
INPUT
How many orders do we have at each stage right now?
↓
image
Output artifact for "Natural-Language Database Querying" test: Summarized all 93 orders across seven current statuses with a table and pie chart., input3-main-status-breakdown-table-and-pie.png
Summarized all 93 orders across seven current statuses with a table and pie chart.
Bottom Line
Strong: it handled simple, analytical, and grouped queries against the live database without requiring SQL.
From our researchQuery Live Databases Using Plain English with AI
Conversation Context Retention
▾
Test Summary
Feature tested: Conversation Context Retention
Result: Passed

Feature tested: Conversation Context Retention

Result: Passed

Expected behavior: Carries earlier results into later turns so users can drill into the same dataset without restating everything. It reused the best-customer result to check unpaid orders and payment methods, and it carried the order-pipeline context across several follow-ups, including a later disambiguation.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Kept the best-customer context and flagged Rahul Sharma's one unpaid order, while reporting no unpaid orders for Deepak Kulkarni and Karan Joshi. — input2-followup1-unpaid-orders-table-and-pie.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Kept the best-customer context and flagged Rahul Sharma's one unpaid order, while reporting no unpaid orders for Deepak Kulkarni and Karan Joshi. — input2-followup1-unpaid-orders-table-and-pie.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 same top-3 customer set and summarized each customer's payment methods, including Rahul Sharma's UPI use and Karan Joshi's Net Banking order. — input2-followup2-payment-methods.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Reused the same top-3 customer set and summarized each customer's payment methods, including Rahul Sharma's UPI use and Karan Joshi's Net Banking order. — input2-followup2-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): Kept the pipeline context, compared April 2026 with May 2026, and then connected the result back to the two previously identified pending-and-paid orders. — input3-followup3-full-breakdown-april-vs-may.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Kept the pipeline context, compared April 2026 with May 2026, and then connected the result back to the two previously identified pending-and-paid orders. — input3-followup3-full-breakdown-april-vs-may.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 explicit disambiguation correctly by narrowing the scope to pending-and-paid only and confirming there were zero such orders in April and May 2026. — input3-followup3-disambiguated-pending-paid-only.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Handled the explicit disambiguation correctly by narrowing the scope to pending-and-paid only and confirming there were zero such orders in April and May 2026. — input3-followup3-disambiguated-pending-paid-only.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Strong: it preserved conversation state across multiple follow-ups and reused earlier results correctly.

Carries earlier results into later turns so users can drill into the same dataset without restating everything. It reused the best-customer result to check unpaid orders and payment methods, and it carried the order-pipeline context across several follow-ups, including a later disambiguation.

INPUT
Follow-up 1: For the top 3 from that list — do any of them have unpaid orders?
↓
image
Output artifact for "Conversation Context Retention" test: Kept the best-customer context and flagged Rahul Sharma's one unpaid order, while reporting no unpaid orders for Deepak Kulkarni and Karan Joshi., input2-followup1-unpaid-orders-table-and-pie.png
Kept the best-customer context and flagged Rahul Sharma's one unpaid order, while reporting no unpaid orders for Deepak Kulkarni and Karan Joshi.
INPUT
Follow-up 2: What payment methods do these top 3 usually use?
↓
image
Output artifact for "Conversation Context Retention" test: Reused the same top-3 customer set and summarized each customer's payment methods, including Rahul Sharma's UPI use and Karan Joshi's Net Banking order., input2-followup2-payment-methods.png
Reused the same top-3 customer set and summarized each customer's payment methods, including Rahul Sharma's UPI use and Karan Joshi's Net Banking order.
INPUT
Follow-up 3: Compare that to last month — same breakdown, I want to see if things have improved or got worse.
↓
image
Output artifact for "Conversation Context Retention" test: Kept the pipeline context, compared April 2026 with May 2026, and then connected the result back to the two previously identified pending-and-paid orders., input3-followup3-full-breakdown-april-vs-may.png
Kept the pipeline context, compared April 2026 with May 2026, and then connected the result back to the two previously identified pending-and-paid orders.
INPUT
Follow-up 3, disambiguated: I want to know about pending and paid orders only compare that.
↓
image
Output artifact for "Conversation Context Retention" test: Handled the explicit disambiguation correctly by narrowing the scope to pending-and-paid only and confirming there were zero such orders in April and May 2026., input3-followup3-disambiguated-pending-paid-only.png
Handled the explicit disambiguation correctly by narrowing the scope to pending-and-paid only and confirming there were zero such orders in April and May 2026.
Bottom Line
Strong: it preserved conversation state across multiple follow-ups and reused earlier results correctly.
From our researchQuery Live Databases Using Plain English with AI
Query Trace and SQL Visibility
▾
Test Summary
Feature tested: Query Trace and SQL Visibility
Result: Passed

Feature tested: Query Trace and SQL Visibility

Result: Passed

Expected behavior: Shows the generated SQL and a step-by-step trace inline with the answer so users can inspect how each result was built. The traces included intermediate diagnostic queries, not just the final statement.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Displays the generated SQL and a 5-step trace covering the last-90-day count, previous-90-day count, date-range sanity checks, and the final response. — input1-generated-sql-and-trace.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Displays the generated SQL and a 5-step trace covering the last-90-day count, previous-90-day count, date-range sanity checks, and the final response. — input1-generated-sql-and-trace.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): Shows the customer-ranking SQL and a trace built from two query steps that compare spending and frequency. — input2-main-generated-sql-trace.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Shows the customer-ranking SQL and a trace built from two query steps that compare spending and frequency. — input2-main-generated-sql-trace.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): Shows the grouped status-count SQL and a short trace for the seven-status order breakdown. — input3-main-generated-sql-trace.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Shows the grouped status-count SQL and a short trace for the seven-status order breakdown. — input3-main-generated-sql-trace.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): Shows the multi-step SQL trace used to find the two pending-but-paid orders and calculate how long they had been stuck. — Screenshot 2026-08-14 150515.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Shows the multi-step SQL trace used to find the two pending-but-paid orders and calculate how long they had been stuck. — Screenshot 2026-08-14 150515.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Strong: every tested answer exposed its SQL and step trace inline.

Shows the generated SQL and a step-by-step trace inline with the answer so users can inspect how each result was built. The traces included intermediate diagnostic queries, not just the final statement.

INPUT
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 Trace and SQL Visibility" test: Displays the generated SQL and a 5-step trace covering the last-90-day count, previous-90-day count, date-range sanity checks, and the final response., input1-generated-sql-and-trace.png
Displays the generated SQL and a 5-step trace covering the last-90-day count, previous-90-day count, date-range sanity checks, and the final response.
INPUT
Who are my best customers — the ones who order the most and spend the most?
↓
image
Output artifact for "Query Trace and SQL Visibility" test: Shows the customer-ranking SQL and a trace built from two query steps that compare spending and frequency., input2-main-generated-sql-trace.png
Shows the customer-ranking SQL and a trace built from two query steps that compare spending and frequency.
INPUT
How many orders do we have at each stage right now?
↓
image
Output artifact for "Query Trace and SQL Visibility" test: Shows the grouped status-count SQL and a short trace for the seven-status order breakdown., input3-main-generated-sql-trace.png
Shows the grouped status-count SQL and a short trace for the seven-status order breakdown.
INPUT
Are there any orders that are pending but already paid?
↓
image
Output artifact for "Query Trace and SQL Visibility" test: Shows the multi-step SQL trace used to find the two pending-but-paid orders and calculate how long they had been stuck., Screenshot 2026-08-14 150515.png
Shows the multi-step SQL trace used to find the two pending-but-paid orders and calculate how long they had been stuck.
Bottom Line
Strong: every tested answer exposed its SQL and step trace inline.
From our researchQuery Live Databases Using Plain English with AI
Automatic Chart and Table Generation
▾
Test Summary
Feature tested: Automatic Chart and Table Generation
Result: Passed

Feature tested: Automatic Chart and Table Generation

Result: Passed

Expected behavior: Automatically renders charts and tabular summaries without user configuration, including bar charts, pie charts, comparison tables, and dashboard-style result cards. It also rendered an empty chart correctly when the result set was empty.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Rendered the answer with a chart area that correctly reflected the empty last-90-days result rather than inventing data. — input1-answer-and-diagnostic-chain.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Rendered the answer with a chart area that correctly reflected the empty last-90-days result rather than inventing data. — input1-answer-and-diagnostic-chain.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 customer comparison bar chart and a supporting table for spending and order metrics. — input2-main-bar-chart-id-tooltip.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Auto-generated a customer comparison bar chart and a supporting table for spending and order metrics. — input2-main-bar-chart-id-tooltip.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 pie chart and a table for the 93-order status breakdown. — input3-main-status-breakdown-table-and-pie.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Auto-generated a pie chart and a table for the 93-order status breakdown. — input3-main-status-breakdown-table-and-pie.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): Rendered a delivered-vs-cancelled comparison with both a table and a chart in the same result card. — input3-followup1-delivered-vs-cancelled-chart.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Rendered a delivered-vs-cancelled comparison with both a table and a chart in the same result card. — input3-followup1-delivered-vs-cancelled-chart.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Strong overall: charts and tables were generated automatically on every tested turn.

Automatically renders charts and tabular summaries without user configuration, including bar charts, pie charts, comparison tables, and dashboard-style result cards. It also rendered an empty chart correctly when the result set was empty.

INPUT
Show all customers created in the last 90 days. How does new customer acquisition compare to the previous 90 days?
↓
image
Output artifact for "Automatic Chart and Table Generation" test: Rendered the answer with a chart area that correctly reflected the empty last-90-days result rather than inventing data., input1-answer-and-diagnostic-chain.png
Rendered the answer with a chart area that correctly reflected the empty last-90-days result rather than inventing data.
INPUT
Who are my best customers — the ones who order the most and spend the most?
↓
image
Output artifact for "Automatic Chart and Table Generation" test: Auto-generated a customer comparison bar chart and a supporting table for spending and order metrics., input2-main-bar-chart-id-tooltip.png
Auto-generated a customer comparison bar chart and a supporting table for spending and order metrics.
INPUT
How many orders do we have at each stage right now?
↓
image
Output artifact for "Automatic Chart and Table Generation" test: Auto-generated a pie chart and a table for the 93-order status breakdown., input3-main-status-breakdown-table-and-pie.png
Auto-generated a pie chart and a table for the 93-order status breakdown.
INPUT
What percentage of our orders were successfully delivered vs cancelled?
↓
image
Output artifact for "Automatic Chart and Table Generation" test: Rendered a delivered-vs-cancelled comparison with both a table and a chart in the same result card., input3-followup1-delivered-vs-cancelled-chart.png
Rendered a delivered-vs-cancelled comparison with both a table and a chart in the same result card.
Bottom Line
Strong overall: charts and tables were generated automatically on every tested turn.
From our researchQuery Live Databases Using Plain English with AI
Empty-Data Diagnostics and Ambiguity Resolution
▾
Test Summary
Feature tested: Empty-Data Diagnostics and Ambiguity Resolution
Result: Passed

Feature tested: Empty-Data Diagnostics and Ambiguity Resolution

Result: Passed

Expected behavior: When a requested date window had no rows or a follow-up was ambiguous, it ran extra checks to confirm the data boundary and then either selected a valid comparison window or narrowed the scope instead of fabricating results.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Detected that the last-90-days window was empty and explained the boundary rather than reporting a misleading flat zero. — input1-answer-and-diagnostic-chain.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Detected that the last-90-days window was empty and explained the boundary rather than reporting a misleading flat zero. — input1-answer-and-diagnostic-chain.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): Diagnosed the requested comparison window, found that a direct June-to-August-style comparison would be empty, and substituted a valid April-vs-May comparison instead. — input3-followup3-full-breakdown-april-vs-may.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Diagnosed the requested comparison window, found that a direct June-to-August-style comparison would be empty, and substituted a valid April-vs-May comparison instead. — input3-followup3-full-breakdown-april-vs-may.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): Narrowed the ambiguous request to pending-and-paid only and confirmed there were no new pending-and-paid orders in April or May 2026. — input3-followup3-disambiguated-pending-paid-only.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Narrowed the ambiguous request to pending-and-paid only and confirmed there were no new pending-and-paid orders in April or May 2026. — input3-followup3-disambiguated-pending-paid-only.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Strong on the hard cases: it refused to invent data and either switched to a valid window or narrowed scope.

When a requested date window had no rows or a follow-up was ambiguous, it ran extra checks to confirm the data boundary and then either selected a valid comparison window or narrowed the scope instead of fabricating results.

INPUT
Show all customers created in the last 90 days. How does new customer acquisition compare to the previous 90 days?
↓
image
Output artifact for "Empty-Data Diagnostics and Ambiguity Resolution" test: Detected that the last-90-days window was empty and explained the boundary rather than reporting a misleading flat zero., input1-answer-and-diagnostic-chain.png
Detected that the last-90-days window was empty and explained the boundary rather than reporting a misleading flat zero.
INPUT
Follow-up 3: Compare that to last month — same breakdown, I want to see if things have improved or got worse.
↓
image
Output artifact for "Empty-Data Diagnostics and Ambiguity Resolution" test: Diagnosed the requested comparison window, found that a direct June-to-August-style comparison would be empty, and substituted a valid April-vs-May comparison instead., input3-followup3-full-breakdown-april-vs-may.png
Diagnosed the requested comparison window, found that a direct June-to-August-style comparison would be empty, and substituted a valid April-vs-May comparison instead.
INPUT
Follow-up 3, disambiguated: I want to know about pending and paid orders only compare that.
↓
image
Output artifact for "Empty-Data Diagnostics and Ambiguity Resolution" test: Narrowed the ambiguous request to pending-and-paid only and confirmed there were no new pending-and-paid orders in April or May 2026., input3-followup3-disambiguated-pending-paid-only.png
Narrowed the ambiguous request to pending-and-paid only and confirmed there were no new pending-and-paid orders in April or May 2026.
Bottom Line
Strong on the hard cases: it refused to invent data and either switched to a valid window or narrowed scope.
From our researchQuery Live Databases Using Plain English with AI
Model and Cost Transparency
▾
Test Summary
Feature tested: Model and Cost Transparency
Result: Passed

Feature tested: Model and Cost Transparency

Result: Passed

Expected behavior: Surfaces the active model, token count, and cost inline on each answer, and the Billing page confirms the tested account stayed on the free tier with zero aggregate spend.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): The Billing page shows the Free plan selected, $0.00 credits remaining, 1 seat, 3 workflows max on free, and usage by member with $0.0000 cost. — billing-dashboard-free-plan-usage.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): The Billing page shows the Free plan selected, $0.00 credits remaining, 1 seat, 3 workflows max on free, and usage by member with $0.0000 cost. — billing-dashboard-free-plan-usage.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Very strong and unusually transparent; the free plan held at $0.0000 in Billing.

Surfaces the active model, token count, and cost inline on each answer, and the Billing page confirms the tested account stayed on the free tier with zero aggregate spend.

INPUT
Open the Billing tab after the test session and verify usage for the free plan.
↓
image
Output artifact for "Model and Cost Transparency" test: The Billing page shows the Free plan selected, $0.00 credits remaining, 1 seat, 3 workflows max on free, and usage by member with $0.0000 cost., billing-dashboard-free-plan-usage.png
The Billing page shows the Free plan selected, $0.00 credits remaining, 1 seat, 3 workflows max on free, and usage by member with $0.0000 cost.
Bottom Line
Very strong and unusually transparent; the free plan held at $0.0000 in Billing.
From our researchQuery Live Databases Using Plain English with AI

How it scored on the research's own criteria

The 11 evaluation dimensions from our hands-on research on AI for Database, each judged from recorded runs on 3 test inputs — the same verdicts the ranking page ranks on.

held up  partial  failed  not exercised by this input

CriterionVerdictWhat the runs showedPer inputProof
Ambiguity HandlingStrong5/5When a business term could mean more than one thing, it does not just guess and move on. It either surfaces both plausible readings or asks/works through the scope until the result is specific enough to trust.open proof ↗
Business InsightStrong5/5It does more than restate counts: it turns results into operational meaning and next steps. The tool repeatedly points to what the numbers suggest a team should do next.open proof ↗
Chart / Visualization SupportMixed3/5It reliably generates charts, but the visuals are only partly useful. Some are fine, while others hide the most important label or mix scales in a way that makes the chart harder to read than the table.open proof ↗
Dashboard WorkflowStrong5/5The tool clearly supports turning answers into reusable dashboard items. The dashboard path is not just implied by the product marketing; it shows up in the live result screens too.open proof ↗
Export / ReuseStrong4/5There is clear evidence that at least one result can be exported for reuse, which is a real strength. The score stays below top marks because the observed export support is limited to one confirmed case rather than broad, repeated reuse behavior.open proof ↗
Follow-Up ContextMixed3/5It does remember earlier answers and can carry details forward across turns, but not perfectly. One follow-up made a claim that wasn’t backed by the displayed rows, while later turns correctly reused prior customer and order details.open proof ↗
FS Learning ValueStrong5/5This tool shows a very useful improvement path for FS NL2SQL Agent: the natural-language summary layer should verify its own math and make sure every claim is grounded in the rows it shows. That is the clearest lesson from the failures observed here.open proof ↗
Plain English Query HandlingStrong5/5It understood straightforward business questions without SQL and returned the right database-backed answers on the first pass. The hard part was not parsing the wording, but checking the data behind it.open proof ↗
Result ReadabilityStrong5/5The answers are packaged for non-technical reading: clear headings, compact tables, and short narrative takeaways. Even when the numbers are complex, the presentation stays easy to scan.open proof ↗
SQL GenerationStrong5/5Across all three tested scenarios, it built sensible database queries and traces that matched the questions being asked. The SQL was not just present; it was structured to check the right things before answering.open proof ↗
SQL VisibilityStrong5/5Users can inspect the SQL directly in the answer view, so the tool does not hide its reasoning behind a separate step. That makes it easy to copy, verify, or debug what it ran.open proof ↗

Verdicts come verbatim from the study's recorded observations, never re-derived at render; a criterion with no recorded run shows Not exercised — this section cannot invent a score.

Free plan tested; paid tiers documented

Hands-on testing used the Free plan only. The report also documents Pro and Enterprise pricing.

TESTED
Free
$0 / month
Free models included at no cost. 1 seat. 3 workflows max on free. No credit card required. This is the plan used in testing.
Pro
$19 / month
Premium AI models, unlimited workflows, webhook integrations, team usage tracking, and priority support.
Enterprise
Custom
Self-hosted deployment, unlimited connections, SSO, bring-your-own models/API keys, and dedicated support.

The tested account stayed at $0.0000 total cost across 12 queries and 186,345 tokens.

✓ Use This If
●You want to query a live database in plain English with no SQL.
●You need inline SQL plus charts/tables for business stakeholders.
●You want follow-up questions to stay in context.
●You care about seeing per-query cost and model usage on every answer.
✕ Skip This If
●You need the prose summary to be perfect without checking the underlying table.
●You need charts that always label entities by name instead of IDs.
●You need business-rule definitions to stay perfectly consistent across turns without drift.
developer-toolsother-developer-toolstextOther
Yes. In this test it answered plain-English questions against a live PostgreSQL database and returned natural-language summaries with tables and charts.
Yes. Every tested turn displayed the generated SQL inline, along with a step-by-step trace of how the answer was built.
Yes. It reused prior context across follow-ups, including a best-customer thread and a multi-turn order-pipeline thread with a later disambiguation.
It generated bar charts, pie charts, comparison tables, and dashboard-style answer cards without any chart setup from the user.
Yes. The report tested the Free plan, and the Billing page showed $0.00 credits remaining, 1 seat, 3 workflows max on free, and $0.0000 total cost across 12 queries and 186,345 tokens.
The underlying SQL and tables were correct, but the prose layer had verified mistakes: one percentage sentence was wrong, one unpaid-order claim had no supporting row in the displayed result, and some charts were hard to read or labeled by ID instead of name.

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