Review how well every finished support conversation was handled
Reads a closed chat, email thread or call and says whether the agent answered the request, left a clear next step, contradicted themselves or made the customer repeat, so team leads review the right ones.
Try it on this example
Channel (email, web chat, chat app, SMS, call): Email
The whole conversation, oldest message first
- Does the conversation contain a reply from an agent of the company?Yes98%
- Did the agent address the customer's main request?Yes90%
- Did the agent deal with every separate question or request the customer made?All answered93%
- Did the agent make clear what happens next, when anything was left to do?Clear100%
- Did an agent say something that contradicts a fact stated earlier in the conversation?No75%
- Did the customer have to repeat a request or detail because an agent missed it?Yes96%
- Were the agent replies respectful throughout?Yes89%
- How completely did the agent replies, taken together, answer the customer's request?Fully65%
- How did the agent respond to the customer's situation?Acknowledged92%
These are real answers stored from one run on this example.
The prism behind it
Review how well every finished support conversation was handled
Fields
- Channel (email, web chat, chat app, SMS, call)
- The whole conversation, oldest message first
Context
The text is one support conversation between a customer and the company, oldest message first. It may carry sender names and times, or no speaker labels at all, as in a call transcription; the customer is the person who contacted support. Judge only what the conversation shows. How the answers are used: they choose which conversations a team leader reads, and team leaders coach from rates over many conversations. No single answer is a verdict on an agent. Judge what the agent did, not their spelling, grammar or accent.
Questions
Does the conversation contain a reply from an agent of the company? Yes / No
The other questions mean something only when the answer is yes. Yes: At least one substantive reply written or spoken by an agent is present. No: Only customer messages appear, or the only company content is an automated acknowledgement.
Did the agent address the customer's main request? Yes / No
A refusal counts when the agent gives a specific reason. Yes: The agent replies answer or act on the main request, or decline it with a specific reason. No: The agent replies ignore the main request, answer a different question, or refuse without a reason.
Did the agent deal with every separate question or request the customer made? Choice
Count each distinct question or request the customer made anywhere in the conversation.
Did the agent make clear what happens next, when anything was left to do? Choice
Judge the position at the end of the conversation.
Did an agent say something that contradicts a fact stated earlier in the conversation? Yes / No
Judge only against the conversation itself, not against outside knowledge. Yes: An agent message conflicts with an earlier fact given by the customer or by an agent. No: No such conflict appears.
Did the customer have to repeat a request or detail because an agent missed it? Yes / No
Yes: The customer restates a request or detail they already gave, after an agent reply that missed it. No: The customer never had to repeat themselves, or repeated only to add something new.
Were the agent replies respectful throughout? Yes / No
Yes: Every agent reply was courteous. No: At least one agent reply was dismissive, sarcastic, blaming or insulting.
How completely did the agent replies, taken together, answer the customer's request? Scale
How did the agent respond to the customer's situation? Scale
Judge acknowledgement of what the customer described, not politeness formulas, and not whether the outcome was good. A routine request needs none; rate it as neutral.
Lens columns
has_agent_reply, has_agent_reply_probability, request_addressed, request_addressed_probability, every_part_answered, every_part_answered_probability, next_step, next_step_probability, agent_contradiction, agent_contradiction_probability, customer_repeated, customer_repeated_probability, respectful, respectful_probability, completeness, completeness_average, empathy, empathy_average
Run it on your own text
Add this prism in the app, change any question, and test it on a file of your own.