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camelAI

A self-checking AI data agent for live database Q&A, follow-ups, and charts on request.

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Live PostgreSQLFollow-up contextRequest-driven chartsSchema-aware refusal
TL;DR — our verdictUpdated August 2026 · 16 test artifacts

Strong on trust, weaker on presentation

Where it wins
  • 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.
Main limitation
  • You need charts to appear automatically on every answer.
Pricing (verified plans)
Free $0/moStarter $10/mo + usage after creditsPro $40/mo + usage after credits
Strongest test artifacts
Plain-English Live Database Q&A →Conversational Follow-up Handling →On-Demand Chart Generation →

Our take

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.

Download video: Demo videoWatch video (streaming): Demo video0:00 — Introduction to Camila1:41 — Connecting to Databases and Tools3:04 — Executing Natural Language Queries5:49 — Visualization and Auto-Correction8:19 — Advanced Features and Final Demo
Screen recording of camelAI answering live database questions, generating a customer table, and creating an interactive visualization report.

In-Depth Review

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

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AI Demos Team
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Verified Review

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

Input artifact: Input artifact (Text prompt): Input

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

Input artifact: Input artifact (Text prompt): Input

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

Input artifact: Input artifact (Text prompt): Input

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
Output artifact for "Plain-English Live Database Q&A" test: 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
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
Output artifact for "Plain-English Live Database Q&A" test: 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
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
Output artifact for "Plain-English Live Database Q&A" test: Summarized the current order pipeline as 93 orders across seven stages., input3-main-stage-breakdown-table.png
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

Input artifact: Input artifact (Text prompt): Input

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

Input artifact: Input artifact (Text prompt): Input

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

Input artifact: Input artifact (Text prompt): Input

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

Input artifact: Input artifact (Text prompt): Input

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
Output artifact for "Conversational Follow-up Handling" test: 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
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
Output artifact for "Conversational Follow-up Handling" test: 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
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
Output artifact for "Conversational Follow-up Handling" test: Computed the delivered-vs-cancelled percentage split from the current orders., input3-followup1-delivered-vs-cancelled-percentages.png
Computed the delivered-vs-cancelled percentage split from the current orders.
INPUT
INPUT: Are there any orders that are pending but already paid?
↓
OUTPUT
Output artifact for "Conversational Follow-up Handling" test: Found two pending-but-paid orders and totaled them at $1,690., input3-followup2-pending-but-paid-table.png
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

Input artifact: Input artifact (Text prompt): Input

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

Input artifact: Input artifact (Text prompt): Input

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

Input artifact: Input artifact (Text prompt): Input

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
Output artifact for "On-Demand Chart Generation" test: Created a bar chart comparing 13 prior-90-day customers to 0 in the last 90 days., input1-visualization-png-bar-chart.png
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
Output artifact for "On-Demand Chart Generation" test: Created a dual-panel chart showing payment-method counts and spend for the top three customers., input2-followup2-visualization-png-dual-panel.png
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
Output artifact for "On-Demand Chart Generation" test: Created a side-by-side comparison chart for current versus previous-month pending-but-paid orders., input3-followup3-visualization-png-current-vs-previous.png
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

Input artifact: Input artifact (Text prompt): Input

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

Input artifact: Input artifact (Text prompt): Input

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

Input artifact: Input artifact (Text prompt): Input

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
Output artifact for "Schema Validation and Safe Refusal" test: 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
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
Output artifact for "Schema Validation and Safe Refusal" test: 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
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
Output artifact for "Schema Validation and Safe Refusal" test: Hit a JavaScript parse error, diagnosed the template-literal issue, and reran the visualization successfully., image-1786777101484.png
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

Input artifact: Input artifact (Text prompt): Input

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

Input artifact: Input artifact (Text prompt): Input

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

Input artifact: Input artifact (Text prompt): Input

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
Output artifact for "Inspectable Execution Trace" test: Show-work trace for schema inspection and SQL generation on the acquisition query., input1-show-work-schema-and-sql-steps.png
Show-work trace for schema inspection and SQL generation on the acquisition query.
INPUT
INPUT: show work for the customer ranking analysis
↓
OUTPUT
Output artifact for "Inspectable Execution Trace" test: Show-work trace for schema inspection and ranking-query generation., input2-main-show-work-schema-and-ranking-sql.png
Show-work trace for schema inspection and ranking-query generation.
INPUT
INPUT: show work for the current order-stage breakdown query
↓
OUTPUT
Output artifact for "Inspectable Execution Trace" test: Show-work trace for the status-grouping query against the live orders table., input3-main-show-work-groupby-sql.png
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.
●You need fast, low-latency responses.
business-marketingother-business-marketingtextOther
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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