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Collect and score sales chats with response metrics and evidence-based LLM reviews.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "sales-chat-quality" yet — see the docs or source repo.
Collect sales chat conversations from the past week through the CDP, calculate average response time per conversation, and generate quality scores and evidence-linked review comments.
Returns per-conversation quality scores, response-time metrics, and LLM review results with linked evidence.
Identify sales chat conversations with long response times and generate evidence-linked quality review results for them.
Provides a list of slow-response conversations along with quality scores and evidence-based review notes.
Score a batch of sales chat conversations, compute response latency, and generate quality review reports traceable to conversation evidence.
Produces batch scoring results, response-time statistics, and QA reports with linked supporting evidence.
Sales or support managers can use it to collect and score chat conversations in bulk, quickly identifying low-quality interactions. It also provides evidence-linked reviews for easier validation.
Data analysts can use it to compute response times in sales chats and locate conversations with delayed replies. They can then compare timing with quality scores to assess service performance.
When reviewing sales communication performance, teams can examine LLM-generated quality assessments with linked evidence. This makes it easier to trace specific chat excerpts and discuss improvements.
It collects sales chat conversations through a CDP and scores them. It also computes response times and generates evidence-linked LLM quality reviews.
The description indicates that it relies on a CDP to collect sales chat conversations. For the exact integration method and prerequisites, see the source repository.
It does more than output scores; it also generates LLM reviews linked to supporting evidence. This helps users connect quality judgments back to specific conversation content.
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