camelAI reliably answers live PostgreSQL questions, keeps follow-up context, and refuses unsupported historical comparisons instead of guessing. For this use case, though, charts are request-driven rather than automatic, and the generated SQL is buried inside a JavaScript work trace instead of being shown and explained in a dedicated panel.
Screen recording of camelAI answering live database questions, generating a customer table, and creating an interactive visualization report.
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Feature-by-Feature Breakdown
Plain-English Live Database Q&A
▾
Test Summary
Feature tested: Plain-English Live Database Q&A
Result: Passed
Feature tested: Plain-English Live Database Q&A
Result: Passed
Expected behavior: Answers plain-English questions against the connected live PostgreSQL database and returns readable tables or direct summaries. It was exercised on new-customer acquisition, best-customer rankings, and current order-stage counts.
Test case: Text prompt → Image
Input type: Text prompt
Input used: Input artifact (Text prompt): Input
Observed output: Output artifact (Image): Answered the 90-day acquisition query with 0 new customers in the last 90 days versus 13 in the previous 90 days. — input1-answer-text-0-vs-13.png
Output artifact: Output artifact (Image): Answered the 90-day acquisition query with 0 new customers in the last 90 days versus 13 in the previous 90 days. — input1-answer-text-0-vs-13.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 separate frequency and spend rankings and named Rahul Sharma as the strongest balance of order count and spend. — input2-main-two-ranking-tables-and-best-overall.png
Output artifact: Output artifact (Image): Returned separate frequency and spend rankings and named Rahul Sharma as the strongest balance of order count and spend. — input2-main-two-ranking-tables-and-best-overall.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 the current order pipeline as 93 orders across seven stages. — input3-main-stage-breakdown-table.png
Output artifact: Output artifact (Image): Summarized the current order pipeline as 93 orders across seven stages. — input3-main-stage-breakdown-table.png
What changed: Text prompt transformed into Image
Why it matters / Conclusion: Strong at answering the actual database question with a clean table or summary, but the output stays terse and factual.
Answers plain-English questions against the connected live PostgreSQL database and returns readable tables or direct summaries. It was exercised on new-customer acquisition, best-customer rankings, and current order-stage counts.
INPUT
INPUT: Show all customers created in the last 90 days. How does new customer acquisition compare to the previous 90 days?
↓→
OUTPUT
Answered the 90-day acquisition query with 0 new customers in the last 90 days versus 13 in the previous 90 days.
INPUT
INPUT: Who are my best customers — the ones who order the most and spend the most?
↓→
OUTPUT
Returned separate frequency and spend rankings and named Rahul Sharma as the strongest balance of order count and spend.
INPUT
INPUT: How many orders do we have at each stage right now?
↓→
OUTPUT
Summarized the current order pipeline as 93 orders across seven stages.
Bottom Line
Strong at answering the actual database question with a clean table or summary, but the output stays terse and factual.
From our researchQuery Live Databases Using Plain English with AI
Conversational Follow-up Handling
▾
Test Summary
Feature tested: Conversational Follow-up Handling
Result: Passed
Feature tested: Conversational Follow-up Handling
Result: Passed
Expected behavior: Keeps later questions anchored to earlier results so follow-ups reuse the prior scope instead of starting over. It was tested across the best-customers chain and the order-pipeline chain.
Test case: Text prompt → Image
Input type: Text prompt
Input used: Input artifact (Text prompt): Input
Observed output: Output artifact (Image): Scoped the unpaid-order check to the top three highest-spending customers and found Rahul Sharma had one unpaid order. — input2-followup1-unpaid-orders-answer.png
Output artifact: Output artifact (Image): Scoped the unpaid-order check to the top three highest-spending customers and found Rahul Sharma had one unpaid order. — input2-followup1-unpaid-orders-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): Kept the same top-three context and summarized the observed payment-method pattern, while noting the small sample size. — input2-followup2-payment-methods-table.png
Output artifact: Output artifact (Image): Kept the same top-three context and summarized the observed payment-method pattern, while noting the small sample size. — input2-followup2-payment-methods-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): Computed the delivered-vs-cancelled percentage split from the current orders. — input3-followup1-delivered-vs-cancelled-percentages.png
Output artifact: Output artifact (Image): Computed the delivered-vs-cancelled percentage split from the current orders. — input3-followup1-delivered-vs-cancelled-percentages.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): Found two pending-but-paid orders and totaled them at $1,690. — input3-followup2-pending-but-paid-table.png
Output artifact: Output artifact (Image): Found two pending-but-paid orders and totaled them at $1,690. — input3-followup2-pending-but-paid-table.png
What changed: Text prompt transformed into Image
Why it matters / Conclusion: Very solid conversational continuity: each follow-up stayed tied to the previous result set and produced the right scoped answer.
Keeps later questions anchored to earlier results so follow-ups reuse the prior scope instead of starting over. It was tested across the best-customers chain and the order-pipeline chain.
INPUT
INPUT: For the top 3 from that list — do any of them have unpaid orders?
↓→
OUTPUT
Scoped the unpaid-order check to the top three highest-spending customers and found Rahul Sharma had one unpaid order.
INPUT
INPUT: What payment methods do these top 3 usually use?
↓→
OUTPUT
Kept the same top-three context and summarized the observed payment-method pattern, while noting the small sample size.
INPUT
INPUT: What percentage of our orders were successfully delivered vs cancelled?
↓→
OUTPUT
Computed the delivered-vs-cancelled percentage split from the current orders.
INPUT
INPUT: Are there any orders that are pending but already paid?
↓→
OUTPUT
Found two pending-but-paid orders and totaled them at $1,690.
Bottom Line
Very solid conversational continuity: each follow-up stayed tied to the previous result set and produced the right scoped answer.
From our researchQuery Live Databases Using Plain English with AI
On-Demand Chart Generation
▾
Test Summary
Feature tested: On-Demand Chart Generation
Result: Passed
Feature tested: On-Demand Chart Generation
Result: Passed
Expected behavior: Turns a completed answer into a downloadable visualization when the user explicitly asks for one. It was tested on a 90-day customer comparison, a dual-panel payment-method chart, and a current-vs-previous pending-paid comparison.
Test case: Text prompt → Image
Input type: Text prompt
Input used: Input artifact (Text prompt): Input
Observed output: Output artifact (Image): Created a bar chart comparing 13 prior-90-day customers to 0 in the last 90 days. — input1-visualization-png-bar-chart.png
Output artifact: Output artifact (Image): Created a bar chart comparing 13 prior-90-day customers to 0 in the last 90 days. — input1-visualization-png-bar-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 dual-panel chart showing payment-method counts and spend for the top three customers. — input2-followup2-visualization-png-dual-panel.png
Output artifact: Output artifact (Image): Created a dual-panel chart showing payment-method counts and spend for the top three customers. — input2-followup2-visualization-png-dual-panel.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 side-by-side comparison chart for current versus previous-month pending-but-paid orders. — input3-followup3-visualization-png-current-vs-previous.png
Output artifact: Output artifact (Image): Created a side-by-side comparison chart for current versus previous-month pending-but-paid orders. — input3-followup3-visualization-png-current-vs-previous.png
What changed: Text prompt transformed into Image
Why it matters / Conclusion: The charting itself is good, but it is request-driven: no chart appears until the user explicitly asks for one.
Turns a completed answer into a downloadable visualization when the user explicitly asks for one. It was tested on a 90-day customer comparison, a dual-panel payment-method chart, and a current-vs-previous pending-paid comparison.
INPUT
INPUT: can you visualize it? (after the 90-day customer acquisition comparison)
↓→
OUTPUT
Created a bar chart comparing 13 prior-90-day customers to 0 in the last 90 days.
INPUT
INPUT: can you visualize it? (after the top-3 payment-method answer)
↓→
OUTPUT
Created a dual-panel chart showing payment-method counts and spend for the top three customers.
INPUT
INPUT: can you visualize it? (after the pending-but-paid comparison)
↓→
OUTPUT
Created a side-by-side comparison chart for current versus previous-month pending-but-paid orders.
Bottom Line
The charting itself is good, but it is request-driven: no chart appears until the user explicitly asks for one.
From our researchQuery Live Databases Using Plain English with AI
Schema Validation and Safe Refusal
▾
Test Summary
Feature tested: Schema Validation and Safe Refusal
Result: Passed
Feature tested: Schema Validation and Safe Refusal
Result: Passed
Expected behavior: Checks the underlying schema before answering and refuses to fabricate comparisons the database cannot support. It also recovered from a JavaScript parse error by debugging and rerunning the task.
Test case: Text prompt → Image
Input type: Text prompt
Input used: Input artifact (Text prompt): Input
Observed output: Output artifact (Image): Refused to make a reliable point-in-time comparison because payment_status has no history and only the current value is stored. — input3-followup3-refusal-and-caveated-answer.png
Output artifact: Output artifact (Image): Refused to make a reliable point-in-time comparison because payment_status has no history and only the current value is stored. — input3-followup3-refusal-and-caveated-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): Listed the columns in order_status_history and confirmed that it tracks order-status changes only, not payment_status history. — input3-schema-check-order-status-history-columns.png
Output artifact: Output artifact (Image): Listed the columns in order_status_history and confirmed that it tracks order-status changes only, not payment_status history. — input3-schema-check-order-status-history-columns.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): Hit a JavaScript parse error, diagnosed the template-literal issue, and reran the visualization successfully. — image-1786777101484.png
Output artifact: Output artifact (Image): Hit a JavaScript parse error, diagnosed the template-literal issue, and reran the visualization successfully. — image-1786777101484.png
What changed: Text prompt transformed into Image
Why it matters / Conclusion: This is the tool's strongest trust behavior: it validates the schema, declines unsupported comparisons, and can self-correct when the code runner fails.
Checks the underlying schema before answering and refuses to fabricate comparisons the database cannot support. It also recovered from a JavaScript parse error by debugging and rerunning the task.
INPUT
INPUT: Compare that to last month — same breakdown, I want to see if things have improved or got worse.
↓→
OUTPUT
Refused to make a reliable point-in-time comparison because payment_status has no history and only the current value is stored.
INPUT
INPUT: What columns does the order status history table have? Does it track payment_status changes over time, or only the order status field?
↓→
OUTPUT
Listed the columns in order_status_history and confirmed that it tracks order-status changes only, not payment_status history.
INPUT
INPUT: Create a live comparison chart for current pending-paid orders versus the previous calendar month’s observed orders.
↓→
image
Hit a JavaScript parse error, diagnosed the template-literal issue, and reran the visualization successfully.
Bottom Line
This is the tool's strongest trust behavior: it validates the schema, declines unsupported comparisons, and can self-correct when the code runner fails.
From our researchQuery Live Databases Using Plain English with AI
Inspectable Execution Trace
▾
Test Summary
Feature tested: Inspectable Execution Trace
Result: Passed
Feature tested: Inspectable Execution Trace
Result: Passed
Expected behavior: Exposes the agent's working steps, including schema inspection and the generated query path, under a show-work view. The trace was visible, though the SQL was embedded inside JavaScript rather than shown in a dedicated SQL panel.
Test case: Text prompt → Image
Input type: Text prompt
Input used: Input artifact (Text prompt): Input
Observed output: Output artifact (Image): Show-work trace for schema inspection and SQL generation on the acquisition query. — input1-show-work-schema-and-sql-steps.png
Output artifact: Output artifact (Image): Show-work trace for schema inspection and SQL generation on the acquisition query. — input1-show-work-schema-and-sql-steps.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): Show-work trace for schema inspection and ranking-query generation. — input2-main-show-work-schema-and-ranking-sql.png
Output artifact: Output artifact (Image): Show-work trace for schema inspection and ranking-query generation. — input2-main-show-work-schema-and-ranking-sql.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): Show-work trace for the status-grouping query against the live orders table. — input3-main-show-work-groupby-sql.png
Output artifact: Output artifact (Image): Show-work trace for the status-grouping query against the live orders table. — input3-main-show-work-groupby-sql.png
What changed: Text prompt transformed into Image
Why it matters / Conclusion: Useful for auditability, but not ideal for non-technical users because the SQL is buried inside code-mode output.
Exposes the agent's working steps, including schema inspection and the generated query path, under a show-work view. The trace was visible, though the SQL was embedded inside JavaScript rather than shown in a dedicated SQL panel.
INPUT
INPUT: show work for the 90-day customer acquisition analysis
↓→
OUTPUT
Show-work trace for schema inspection and SQL generation on the acquisition query.
INPUT
INPUT: show work for the customer ranking analysis
↓→
OUTPUT
Show-work trace for schema inspection and ranking-query generation.
INPUT
INPUT: show work for the current order-stage breakdown query
↓→
OUTPUT
Show-work trace for the status-grouping query against the live orders table.
Bottom Line
Useful for auditability, but not ideal for non-technical users because the SQL is buried inside code-mode output.
From our researchQuery Live Databases Using Plain English with AI
Choose your plan
Observed on the pricing and billing screens.
TESTED
Free
$0/mo
Observed in Billing as 'Free plan — No subscription. Buy credits before hosted usage.'
Starter
$10/mo + usage after credits
Solo builders; the pricing page lists $10 of model credits/mo, 5x daily web search and research allowances, priority over free traffic on camelCode, 30 deployed apps, 10 custom domains, 1 automated task hourly, and 50 GB storage.
Pro
$40/mo + usage after credits
Power users; marked 'Most popular.' The pricing page lists $40 of model credits/mo, 20x daily web search and research allowances, priority over free traffic on camelCode, unlimited deployed apps and custom domains, automations every 5 minutes, and 100 GB storage.
Testing completed successfully on the Free plan with no API key supplied, even though the pricing page says Free has no model credits / bring your own API key. The reason for that gap was not established.
✓ Use This If
●You want to ask a live database questions in plain English without writing SQL.
●You are comfortable asking for charts as a follow-up instead of expecting them automatically.
●You value a tool that will refuse unsupported comparisons instead of guessing.
✕ Skip This If
●You need charts to appear automatically on every answer.
●You need SQL in a dedicated readable panel with a plain-English explanation.
It was tested against a live PostgreSQL connection called @order_status. The tool executed queries, returned answers and tables, and validated the results against the underlying database.
No. In this test, charts only appeared after an explicit follow-up asking to visualize the result. The charts themselves were good, but they were request-driven rather than automatic.
Yes. It correctly kept the scope of the top-3 customers across follow-up questions, and it kept the order-pipeline context across the multi-turn chain.
It does show its working, but the SQL is buried inside a JavaScript code-execution trace rather than exposed in a dedicated SQL panel or explained in plain English.
It refused to guess. When asked to compare a current payment-status state to last month, it explained that payment_status has no history in the schema and only the current value is stored.
The tool was tested on the Free plan with no API key, and the billing screen showed no subscription. The pricing page lists Starter at $10/mo and Pro at $40/mo, and also says Free has no model credits / bring your own API key.
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