It correctly recognizes a bot-avoidance request as escalation intent and provides the full human-support channel list, including email, phone, and ticket portal.

✓ Worked🧾 artifact-verifiedinput + output shownTest date not recordedWonderchat
What was measured
Response Completeness

Addresses all parts of the user’s support request instead of leaving key questions unanswered.

decisive for this rankingtransformation

A support bot that leaves key questions unanswered is not solving the customer’s issue. (3 of 3 judges)

What was given, what came back

Test input: Customer-support handoff request · text
Input — what we sent
The exact prompt
I received the wrong item in my order. I've already checked the order details and this is clearly a mistake on your end. I don't want any more back and forth — can you please connect me to a customer support agent or raise a ticket for this?
a84d1484038e4965ab4d0ce43abd63ef.pdf?v=1
a84d1484038e4965ab4d0ce43abd63ef.pdf

A frustrated support-escalation request after receiving the wrong item, asking to be connected to a support agent or have a ticket raised.

Why this input is hard
  • · Human handoff reliability
  • · Ticket creation workflow
  • · Escalation contextual awareness
  • · Professional tone under frustration
Output — unretouched
image
Provenance
Observation
9e93e0bf-2bf4-431c-82d4-0027fab38b30
Evidence run
2645dc92-49df-478a-b809-21dfd09f06a7
Study
Automate customer support using an AI chatbot
Research task
86b9jm3ev
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: "wonderchat"
})
MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 5 other tools
measured on Response Completeness
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com