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BlazeSQL

Plain-English database analytics with SQL transparency, auto-charts, and self-correcting follow-ups.

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TL;DR — our verdictUpdated August 2026 · 20 test artifacts

Strong analyst-like behavior, with caveats

Where it wins
  • You want a no-code analyst over a live SQL database.
  • You want the generated SQL and a plain-English query explanation visible on demand.
  • You care about self-correction when a first answer is skewed or incomplete.
Main limitation
  • You need query cost, latency, or execution logs visible by default.
Pricing (verified plans)
Free FreePro $150/monthAdvanced $250/monthTeam $400/month
Strongest test artifacts
Natural-Language Live Database Querying →SQL Transparency and Plain-English Query Explanation →Automatic Chart and Table Rendering →

Our take

BlazeSQL is a strong fit for non-technical users who want live database answers in plain English: it returns tables and charts, shows SQL on demand, keeps context across follow-ups, and often corrects its own first pass. The main cautions are inconsistent auto-charting, no visible cost or latency, and a Training workflow needed to lock down business-rule definitions.

Download video: Demo videoWatch video (streaming): Demo video0:00 — Introduction to SQLBot AI0:41 — Core Features and Interface1:41 — Database Connection and Setup2:42 — AI Training and Feedback Loop4:13 — Advanced Features: Dashboards and Automation5:55 — Dual-Mode Query Demonstration8:39 — API Integration and MCP Server10:12 — Interactive Refinement and Final Recap
Browser walkthrough from onboarding into the analytics chat, showing a new-customer query, generated tables and charting, and a data-gap diagnosis.

In-Depth Review

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

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AI Demos Team
Expert Reviewer
Verified Review

Feature-by-Feature Breakdown

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

Feature tested: Natural-Language Live Database Querying

Result: Passed

Verdict: Strong

Expected behavior: Converts business questions into live database queries and returns readable answers with tables and narrative. In testing, it handled customer acquisition, best-customer ranking, and order-status breakdowns without requiring SQL from the user.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): Returned a readable customer list from the connected database for the last-90-days query, showing that BlazeSQL can answer a plain-English request with a live result table. — input1-customer-list-table.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): Returned a readable customer list from the connected database for the last-90-days query, showing that BlazeSQL can answer a plain-English request with a live result table. — input1-customer-list-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): Returned the best-customer answer as a ranked analytical result with narrative summary after correcting the ranking to use valid paid orders only. — input2-main-self-corrected-paid-only-chart.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): Returned the best-customer answer as a ranked analytical result with narrative summary after correcting the ranking to use valid paid orders only. — input2-main-self-corrected-paid-only-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): Returned an order-stage breakdown table with counts per status, showing that BlazeSQL can handle a straightforward operational question against the live database. — input3-main-stage-breakdown-table.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): Returned an order-stage breakdown table with counts per status, showing that BlazeSQL can handle a straightforward operational question against the live database. — input3-main-stage-breakdown-table.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Strong for normal business questions; it answers from live data in a form a non-technical user can read.

Converts business questions into live database queries and returns readable answers with tables and narrative. In testing, it handled customer acquisition, best-customer ranking, and order-status breakdowns without requiring SQL from the user.

INPUT
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 Live Database Querying" test: Returned a readable customer list from the connected database for the last-90-days query, showing that BlazeSQL can answer a plain-English request with a live result table., input1-customer-list-table.png
Returned a readable customer list from the connected database for the last-90-days query, showing that BlazeSQL can answer a plain-English request with a live result table.
INPUT
INPUT: Who are my best customers — the ones who order the most and spend the most?
↓
image
Output artifact for "Natural-Language Live Database Querying" test: Returned the best-customer answer as a ranked analytical result with narrative summary after correcting the ranking to use valid paid orders only., input2-main-self-corrected-paid-only-chart.png
Returned the best-customer answer as a ranked analytical result with narrative summary after correcting the ranking to use valid paid orders only.
INPUT
INPUT: How many orders do we have at each stage right now?
↓
image
Output artifact for "Natural-Language Live Database Querying" test: Returned an order-stage breakdown table with counts per status, showing that BlazeSQL can handle a straightforward operational question against the live database., input3-main-stage-breakdown-table.png
Returned an order-stage breakdown table with counts per status, showing that BlazeSQL can handle a straightforward operational question against the live database.
Bottom Line
Strong for normal business questions; it answers from live data in a form a non-technical user can read.
From our researchQuery Live Databases Using Plain English with AI
SQL Transparency and Plain-English Query Explanation
Strong when Technical Mode is on
▾
Test Summary
Feature tested: SQL Transparency and Plain-English Query Explanation
Result: Passed — Strong when Technical Mode is on

Feature tested: SQL Transparency and Plain-English Query Explanation

Result: Passed

Verdict: Strong when Technical Mode is on

Expected behavior: Shows the exact SQL it ran alongside a numbered plain-English explanation of the query logic. The report also notes that Technical Mode off hides the SQL while preserving the explanation for non-technical users.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): Technical Mode exposes the exact SQL for the 90-day customer-list query and shows a numbered plain-English query-logic summary above it. — input1-technical-mode-sql-list-query.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): Technical Mode exposes the exact SQL for the 90-day customer-list query and shows a numbered plain-English query-logic summary above it. — input1-technical-mode-sql-list-query.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): Technical Mode exposes the exact SQL for the last-90-days vs previous-90-days comparison query and explains the logic in plain English. — input1-technical-mode-sql-comparison-query.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): Technical Mode exposes the exact SQL for the last-90-days vs previous-90-days comparison query and explains the logic in plain English. — input1-technical-mode-sql-comparison-query.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 refined best-customers analysis shows the generated SQL and a numbered logic breakdown, making the paid-only ranking auditable from the screen. — input2-main-technical-mode-sql-refined.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): The refined best-customers analysis shows the generated SQL and a numbered logic breakdown, making the paid-only ranking auditable from the screen. — input2-main-technical-mode-sql-refined.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Technical Mode makes the SQL transparent and understandable; the off state keeps the same reasoning but hides the code for non-technical users.

Shows the exact SQL it ran alongside a numbered plain-English explanation of the query logic. The report also notes that Technical Mode off hides the SQL while preserving the explanation for non-technical users.

INPUT
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 "SQL Transparency and Plain-English Query Explanation" test: Technical Mode exposes the exact SQL for the 90-day customer-list query and shows a numbered plain-English query-logic summary above it., input1-technical-mode-sql-list-query.png
Technical Mode exposes the exact SQL for the 90-day customer-list query and shows a numbered plain-English query-logic summary above it.
INPUT
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 "SQL Transparency and Plain-English Query Explanation" test: Technical Mode exposes the exact SQL for the last-90-days vs previous-90-days comparison query and explains the logic in plain English., input1-technical-mode-sql-comparison-query.png
Technical Mode exposes the exact SQL for the last-90-days vs previous-90-days comparison query and explains the logic in plain English.
INPUT
INPUT: Who are my best customers — the ones who order the most and spend the most?
↓
image
Output artifact for "SQL Transparency and Plain-English Query Explanation" test: The refined best-customers analysis shows the generated SQL and a numbered logic breakdown, making the paid-only ranking auditable from the screen., input2-main-technical-mode-sql-refined.png
The refined best-customers analysis shows the generated SQL and a numbered logic breakdown, making the paid-only ranking auditable from the screen.
Bottom Line
Technical Mode makes the SQL transparent and understandable; the off state keeps the same reasoning but hides the code for non-technical users.
From our researchQuery Live Databases Using Plain English with AI
Automatic Chart and Table Rendering
Useful but not fully consistent
▾
Test Summary
Feature tested: Automatic Chart and Table Rendering
Result: Partial — Useful but not fully consistent

Feature tested: Automatic Chart and Table Rendering

Result: Partial

Verdict: Useful but not fully consistent

Expected behavior: Automatically renders charts for some answers, including bar, dual-axis, and pie charts, while other turns remain table-only or prompt the user about charting. The tested behavior was helpful but not uniform across every query.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): BlazeSQL auto-generated a monthly customer-acquisition bar chart after the initial table answers, using the live database result without requiring chart setup. — input1-monthly-breakdown-chart.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): BlazeSQL auto-generated a monthly customer-acquisition bar chart after the initial table answers, using the live database result without requiring chart setup. — input1-monthly-breakdown-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): The best-customers answer rendered as a chart plus summary, combining total spend and order frequency into a single visual. — input2-main-self-corrected-paid-only-chart.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): The best-customers answer rendered as a chart plus summary, combining total spend and order frequency into a single visual. — input2-main-self-corrected-paid-only-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): The successful-vs-cancelled follow-up produced a pie chart after BlazeSQL corrected the success definition to include completed orders. — input3-followup1-successful-vs-cancelled-pie.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): The successful-vs-cancelled follow-up produced a pie chart after BlazeSQL corrected the success definition to include completed orders. — input3-followup1-successful-vs-cancelled-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): The final order-health comparison rendered as a chart after BlazeSQL diagnosed the date-range issue and switched to the most recent months with real data. — Screenshot 2026-08-11 123155.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): The final order-health comparison rendered as a chart after BlazeSQL diagnosed the date-range issue and switched to the most recent months with real data. — Screenshot 2026-08-11 123155.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Charts are valuable and often automatic, but BlazeSQL does not chart every turn in the same way.

Automatically renders charts for some answers, including bar, dual-axis, and pie charts, while other turns remain table-only or prompt the user about charting. The tested behavior was helpful but not uniform across every query.

INPUT
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 Rendering" test: BlazeSQL auto-generated a monthly customer-acquisition bar chart after the initial table answers, using the live database result without requiring chart setup., input1-monthly-breakdown-chart.png
BlazeSQL auto-generated a monthly customer-acquisition bar chart after the initial table answers, using the live database result without requiring chart setup.
INPUT
INPUT: Who are my best customers — the ones who order the most and spend the most?
↓
image
Output artifact for "Automatic Chart and Table Rendering" test: The best-customers answer rendered as a chart plus summary, combining total spend and order frequency into a single visual., input2-main-self-corrected-paid-only-chart.png
The best-customers answer rendered as a chart plus summary, combining total spend and order frequency into a single visual.
INPUT
INPUT: What percentage of our orders were successfully delivered vs cancelled?
↓
image
Output artifact for "Automatic Chart and Table Rendering" test: The successful-vs-cancelled follow-up produced a pie chart after BlazeSQL corrected the success definition to include completed orders., input3-followup1-successful-vs-cancelled-pie.png
The successful-vs-cancelled follow-up produced a pie chart after BlazeSQL corrected the success definition to include completed orders.
INPUT
INPUT: Compare that to last month — same breakdown, I want to see if things have improved or got worse.
↓
image
Output artifact for "Automatic Chart and Table Rendering" test: The final order-health comparison rendered as a chart after BlazeSQL diagnosed the date-range issue and switched to the most recent months with real data., Screenshot 2026-08-11 123155.png
The final order-health comparison rendered as a chart after BlazeSQL diagnosed the date-range issue and switched to the most recent months with real data.
Bottom Line
Charts are valuable and often automatic, but BlazeSQL does not chart every turn in the same way.
From our researchQuery Live Databases Using Plain English with AI
Conversational Follow-Up Context
Strong
▾
Test Summary
Feature tested: Conversational Follow-Up Context
Result: Passed — Strong

Feature tested: Conversational Follow-Up Context

Result: Passed

Verdict: Strong

Expected behavior: Keeps prior results in context so users can ask follow-up questions without starting over. In testing, the tool carried corrected rankings and multi-turn order-pipeline threads across subsequent questions.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): BlazeSQL kept the corrected top-3 customer set in context and answered the unpaid-orders follow-up with the matching customer and order details. — input2-followup1-unpaid-orders-table.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): BlazeSQL kept the corrected top-3 customer set in context and answered the unpaid-orders follow-up with the matching customer and order details. — input2-followup1-unpaid-orders-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): BlazeSQL preserved the same customer context and returned payment-method usage for the corrected top 3, not a fresh unrelated ranking. — input2-followup2-payment-methods-table.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): BlazeSQL preserved the same customer context and returned payment-method usage for the corrected top 3, not a fresh unrelated ranking. — 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): The order-pipeline thread stayed coherent across follow-ups, with BlazeSQL keeping the discussion focused on the same live orders dataset. — input3-followup2-pending-but-paid-table.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): The order-pipeline thread stayed coherent across follow-ups, with BlazeSQL keeping the discussion focused on the same live orders dataset. — input3-followup2-pending-but-paid-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): BlazeSQL kept the order-health thread alive across the diagnostic sequence and returned the final month-over-month comparison after correcting the time window. — input3-followup3-april-vs-may-result.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): BlazeSQL kept the order-health thread alive across the diagnostic sequence and returned the final month-over-month comparison after correcting the time window. — input3-followup3-april-vs-may-result.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Strong context retention across both short and longer analytical threads.

Keeps prior results in context so users can ask follow-up questions without starting over. In testing, the tool carried corrected rankings and multi-turn order-pipeline threads across subsequent questions.

INPUT
INPUT: For the top 3 from that list — do any of them have unpaid orders?
↓
image
Output artifact for "Conversational Follow-Up Context" test: BlazeSQL kept the corrected top-3 customer set in context and answered the unpaid-orders follow-up with the matching customer and order details., input2-followup1-unpaid-orders-table.png
BlazeSQL kept the corrected top-3 customer set in context and answered the unpaid-orders follow-up with the matching customer and order details.
INPUT
INPUT: What payment methods do these top 3 usually use?
↓
image
Output artifact for "Conversational Follow-Up Context" test: BlazeSQL preserved the same customer context and returned payment-method usage for the corrected top 3, not a fresh unrelated ranking., input2-followup2-payment-methods-table.png
BlazeSQL preserved the same customer context and returned payment-method usage for the corrected top 3, not a fresh unrelated ranking.
INPUT
INPUT: Are there any orders that are pending but already paid?
↓
image
Output artifact for "Conversational Follow-Up Context" test: The order-pipeline thread stayed coherent across follow-ups, with BlazeSQL keeping the discussion focused on the same live orders dataset., input3-followup2-pending-but-paid-table.png
The order-pipeline thread stayed coherent across follow-ups, with BlazeSQL keeping the discussion focused on the same live orders dataset.
INPUT
INPUT: Compare that to last month — same breakdown, I want to see if things have improved or got worse.
↓
image
Output artifact for "Conversational Follow-Up Context" test: BlazeSQL kept the order-health thread alive across the diagnostic sequence and returned the final month-over-month comparison after correcting the time window., input3-followup3-april-vs-may-result.png
BlazeSQL kept the order-health thread alive across the diagnostic sequence and returned the final month-over-month comparison after correcting the time window.
Bottom Line
Strong context retention across both short and longer analytical threads.
From our researchQuery Live Databases Using Plain English with AI
Self-Correction and Data Diagnostics
Strong and differentiated
▾
Test Summary
Feature tested: Self-Correction and Data Diagnostics
Result: Passed — Strong and differentiated

Feature tested: Self-Correction and Data Diagnostics

Result: Passed

Verdict: Strong and differentiated

Expected behavior: Detects when an answer is misleading, skewed, or under-specified, then reruns the analysis with a better filter or diagnostic query. The report observed multiple self-check behaviors across different analytical situations.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): BlazeSQL self-diagnosed that the first customer-acquisition answer was misleading because the last-90-days bucket sat on a date boundary, then expanded the analysis to a monthly breakdown to explain the gap. — Screenshot 2026-08-11 123333.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): BlazeSQL self-diagnosed that the first customer-acquisition answer was misleading because the last-90-days bucket sat on a date boundary, then expanded the analysis to a monthly breakdown to explain the gap. — Screenshot 2026-08-11 123333.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): BlazeSQL noticed the initial best-customers ranking included cancelled and failed orders, then re-ran the query to count only paid, non-cancelled orders. — input2-main-self-corrected-paid-only-chart.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): BlazeSQL noticed the initial best-customers ranking included cancelled and failed orders, then re-ran the query to count only paid, non-cancelled orders. — input2-main-self-corrected-paid-only-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): When the month-over-month comparison returned no results, BlazeSQL checked the orders date range, found the data ended in May 2026, and switched to a valid April-vs-May comparison. — input3-followup3-date-range-diagnostic.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): When the month-over-month comparison returned no results, BlazeSQL checked the orders date range, found the data ended in May 2026, and switched to a valid April-vs-May comparison. — input3-followup3-date-range-diagnostic.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: This is the tool's most trust-building behavior: it corrects itself instead of bluffing.

Detects when an answer is misleading, skewed, or under-specified, then reruns the analysis with a better filter or diagnostic query. The report observed multiple self-check behaviors across different analytical situations.

INPUT
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 "Self-Correction and Data Diagnostics" test: BlazeSQL self-diagnosed that the first customer-acquisition answer was misleading because the last-90-days bucket sat on a date boundary, then expanded the analysis to a monthly breakdown to explain the gap., Screenshot 2026-08-11 123333.png
BlazeSQL self-diagnosed that the first customer-acquisition answer was misleading because the last-90-days bucket sat on a date boundary, then expanded the analysis to a monthly breakdown to explain the gap.
INPUT
INPUT: Who are my best customers — the ones who order the most and spend the most?
↓
image
Output artifact for "Self-Correction and Data Diagnostics" test: BlazeSQL noticed the initial best-customers ranking included cancelled and failed orders, then re-ran the query to count only paid, non-cancelled orders., input2-main-self-corrected-paid-only-chart.png
BlazeSQL noticed the initial best-customers ranking included cancelled and failed orders, then re-ran the query to count only paid, non-cancelled orders.
INPUT
INPUT: Compare that to last month — same breakdown, I want to see if things have improved or got worse.
↓
image
Output artifact for "Self-Correction and Data Diagnostics" test: When the month-over-month comparison returned no results, BlazeSQL checked the orders date range, found the data ended in May 2026, and switched to a valid April-vs-May comparison., input3-followup3-date-range-diagnostic.png
When the month-over-month comparison returned no results, BlazeSQL checked the orders date range, found the data ended in May 2026, and switched to a valid April-vs-May comparison.
Bottom Line
This is the tool's most trust-building behavior: it corrects itself instead of bluffing.
From our researchQuery Live Databases Using Plain English with AI
Feedback-Guided Business-Rule Learning
Strong
▾
Test Summary
Feature tested: Feedback-Guided Business-Rule Learning
Result: Partial — Strong

Feature tested: Feedback-Guided Business-Rule Learning

Result: Partial

Verdict: Strong

Expected behavior: Provides a review workflow where BlazeSQL asks whether generated SQL is correct, accepts a yes/no judgment, and turns corrections into knowledge-base notes. The report treats this as a way to encode company-specific definitions and business rules.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): The training screen presents the generated SQL, the highest-spending customer table, and an explicit review prompt so the user can validate the answer. — training-highest-spending-please-review.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): The training screen presents the generated SQL, the highest-spending customer table, and an explicit review prompt so the user can validate the answer. — training-highest-spending-please-review.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): Training mode turns the answer into a formal yes/no correctness check instead of an informal chat response. — training-yes-no-correct-prompt.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): Training mode turns the answer into a formal yes/no correctness check instead of an informal chat response. — training-yes-no-correct-prompt.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): A negative review opens an open-ended feedback box, allowing the user to explain the correction that should be learned and saved. — training-what-should-i-have-done-differently.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): A negative review opens an open-ended feedback box, allowing the user to explain the correction that should be learned and saved. — training-what-should-i-have-done-differently.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Useful for locking down business definitions, but it is a deliberate review workflow rather than lightweight in-chat feedback.

Provides a review workflow where BlazeSQL asks whether generated SQL is correct, accepts a yes/no judgment, and turns corrections into knowledge-base notes. The report treats this as a way to encode company-specific definitions and business rules.

INPUT
INPUT: What are our highest spending customers, and how many orders has each of them placed?
↓
image
Output artifact for "Feedback-Guided Business-Rule Learning" test: The training screen presents the generated SQL, the highest-spending customer table, and an explicit review prompt so the user can validate the answer., training-highest-spending-please-review.png
The training screen presents the generated SQL, the highest-spending customer table, and an explicit review prompt so the user can validate the answer.
INPUT
INPUT: Is the above SQL code correct?
↓
image
Output artifact for "Feedback-Guided Business-Rule Learning" test: Training mode turns the answer into a formal yes/no correctness check instead of an informal chat response., training-yes-no-correct-prompt.png
Training mode turns the answer into a formal yes/no correctness check instead of an informal chat response.
INPUT
INPUT: What should I have done differently?
↓
image
Output artifact for "Feedback-Guided Business-Rule Learning" test: A negative review opens an open-ended feedback box, allowing the user to explain the correction that should be learned and saved., training-what-should-i-have-done-differently.png
A negative review opens an open-ended feedback box, allowing the user to explain the correction that should be learned and saved.
Bottom Line
Useful for locking down business definitions, but it is a deliberate review workflow rather than lightweight in-chat feedback.
From our researchQuery Live Databases Using Plain English with AI

Vendor-reported plans

Hands-on testing used Team Advanced on a 14-day vendor-granted trial.

Free
Free
Connect one live database; direct in-app querying is limited.
Pro
$150/month
AI data analyst, graphs and dashboards, desktop app with private database connection, knowledge base.
Advanced
$250/month
Pro features plus AI-generated reporting emails, advanced analytics AI that can run Python, public and embeddable dashboards, priority support.
Team
$400/month
3 users included; shared dashboards, specialized support, no-code chatbot.
TESTED
Team Advanced
$800/month
3 users included; $75 per extra user; dynamic integration, forecasting/statistical analysis AI, computer agent for deep research and PDF generation, unlimited agent reports, API and MCP, white-labeling.
Enterprise
custom
SSO, unlimited access groups, self-hosted query-result storage, SLAs, bulk seat pricing.

Free plan connects one live database; direct in-app querying was limited in the report, while the vendor-stated MCP path via Claude or ChatGPT was not hands-on tested.

✓ Use This If
●You want a no-code analyst over a live SQL database.
●You want the generated SQL and a plain-English query explanation visible on demand.
●You care about self-correction when a first answer is skewed or incomplete.
●You are willing to invest in Training to lock down business definitions.
✕ Skip This If
●You need query cost, latency, or execution logs visible by default.
●You want perfectly consistent auto-charting on every turn.
●You need stable out-of-the-box business-rule definitions without training.
productivityother-productivitytextOther
Yes. With Technical Mode on, the report shows the full generated SQL plus a numbered plain-English query-logic explanation. With Technical Mode off, the explanation remains visible and the raw SQL is hidden.
Yes. It retained context across the best-customers chain and the order-pipeline chain, including the corrected result set from the earlier turn.
Often, yes. The report observed a bar chart for monthly customer acquisition, a dual-axis spend-and-order chart for best customers, a pie chart for successful vs cancelled orders, and a chart for the final April-vs-May comparison. Some turns stayed table-only or asked before charting.
BlazeSQL did not bluff a result. It ran the comparison, got no rows, checked the actual date range in the table, found the data ended in May 2026, and then switched to a valid April-vs-May comparison.
Yes. The report shows multiple self-corrections: it flagged a customer-acquisition boundary artifact, removed cancelled and failed orders from a best-customers ranking, and updated a success-rate calculation to count completed orders as successful.
Yes. BlazeSQL has a structured Training mode where the user reviews the generated SQL, marks it correct or incorrect, and can explain what should have been done differently so the correction becomes knowledge-base notes.
No. The report says it did not surface per-query cost, timing, model information, or an execution log in the tested interface.

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