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

Accepts a multi-step operational question in plain English across several follow-ups without requiring SQL from the user.

✓ Worked🧾 artifact-verifiedinput + output shownTest date not recordedQuerio →
What was measured
Plain English Query Handling

Can the tool understand business questions without SQL?

transformation

What was given, what came back

Test input: Order pipeline breakdown with paid-pending edge case and last-month comparison · text · group: ecommerce-nl2sql-benchmark
Input — what we sent
The exact prompt
How many orders do we have at each stage right now?

Follow-up 1: What percentage of our orders were successfully delivered vs cancelled?

Follow-up 2: Are there any orders that are pending but already paid?

Follow-up 3: Compare that to last month — same breakdown, I want to see if things have improved or got worse.

A deeper operational analysis of current order stages, delivery-versus-cancellation rates, pending-but-paid edge cases, and a month-over-month comparison of the same breakdown.

Why this input is hard
  • · Order pipeline analysis
  • · Percentage calculation
  • · Edge-case detection
  • · Payment/order status joins
  • · Multi-turn context retention
  • · Month-over-month comparison
  • · Ambiguity handling for 'same breakdown'
Output — unretouched
Output 1
Output 1
Output 2
Output 2
Output 3
Output 3
Output 4
Output 4
Also checked on this input — same tool, 5 other criteria
Business Insight✓ WorkedExplains the comparison rather than just reporting numbers, including that the month-to-date view is an early signal and should not be treated as a stable trend yet.Chart / Visualization Support✓ WorkedAutomatically generates chart views for the order-stage breakdown and the delivered-vs-cancelled comparison, including the month-over-month share chart.Follow-Up Context⚠ StruggledKeeps the conversation going across multiple turns, but on the final comparison it reuses the delivered-versus-cancelled thread instead of the pending-paid edge-case context, so context selection breaks on one follow-up.Result Readability✓ WorkedPresents the operational breakdown in compact tables with counts and percentages, making the current-stage and month-over-month results easy to scan.SQL Visibility✓ WorkedExposes the generated SQL directly in the chat, including the explicit filter for pending-but-paid orders.
Provenance
Observation
e7daaa8d-fa7d-45f4-89f5-34917965ab66
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/e7daaa8d-fa7d-45f4-89f5-34917965ab66
Free with attribution.
Same input, same check — 1 other tool
measured on Plain English Query Handling
AskYourDatabase✓ WorkedAccepted a four-step operational analysis in plain English across the full follow-up chain without needing the user to write SQL.
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