Evidence · first-party tested/Query Live Databases Using Plain English with AI

Shows the generated SQL inline so users can inspect the aggregation and ordering logic, including the GROUP BY and ORDER BY clauses.

✓ Worked🧾 artifact-verifiedinput + output shownTest date not recordedQuerio →
What was measured
SQL Visibility

Can users inspect or copy the generated SQL?

capability

What was given, what came back

Test input: Best customers with unpaid-order and payment-method follow-ups · text · group: ecommerce-nl2sql-benchmark
Input — what we sent
The exact prompt
Who are my best customers — the ones who order the most and spend the most?

Follow-up 1: For the top 3 from that list — do any of them have unpaid orders?

Follow-up 2: What payment methods do these top 3 usually use?

A conversational multi-table customer analysis that identifies best customers by both order volume and spend, then drills into unpaid orders and payment methods for the top 3.

Why this input is hard
  • · Ambiguous business-term interpretation
  • · Multi-table joins across customers orders and payments
  • · Aggregation and ranking
  • · Follow-up context retention
  • · Scoped drill-down to the top 3 customers
  • · Readable customer-level output
Output — unretouched
Output 1
Output 1
Output 2
Output 2
Also checked on this input — same tool, 5 other criteria
Ambiguity Handling✓ WorkedWhen the phrase "best customers" was ambiguous, it did not guess a single meaning; it split the request into separate order-count and spend rankings.Chart / Visualization Support✓ WorkedSupports visualization on the customer-analysis thread by generating bar-chart views for both spend ranking and order-count ranking.Follow-Up Context✓ WorkedCarries the top-3 customer identities forward across follow-ups and regenerates fresh SQL on each turn, preserving context correctly across at least two drill-down questions.Plain English Query Handling✓ WorkedHandles an informal conversational request about top customers without needing the user to translate it into SQL.Result Readability◐ MixedThe tables are structurally clear, but using customer UUIDs instead of customer names makes the best-customer results less immediately readable for nontechnical users.
Provenance
Observation
71e8765d-9dfb-4578-92cf-fdc93700ad3e
Evidence run
db2bb5d5-0e0e-4cb3-8d76-3555c45c23cd
Study
Query Live Databases Using Plain English with AI
Research task
86b9y6c99
Tested at
not recorded
Source
first-party
Evidence state
verified
Proof shown
input + output shown
Cost / latency
not captured
Repeat run
not captured
Tester
not captured

The last three rows are honest blanks, not placeholders — our capture has no field for them yet.

Query this
get_evidence({
  tool: "querio",
  scenario: "ecommerce-nl2sql-benchmark"
})
MCP · mcp.aidemos.com/api/mcp
JSON · ai.aidemos.com/v1/observations/71e8765d-9dfb-4578-92cf-fdc93700ad3e
Free with attribution.
Same input, same check — 0 other tools
measured on SQL Visibility

No other tool was measured on this criterion for this input.

This evidence is published in
Query Live Databases Using Plain English with AI →
From the same study (page rebuilt from a later run)
Best AI Tools to Query Live Databases Using Plain English →
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com