Read what customers and agents actually said.
Explore transcripts, conversation summaries, channels, brands, reasons, queues, teams, and time windows.
Salted Conversation Intelligence
Ask the operation.
Salted lets CX, support, quality, and operations teams ask natural-language questions across conversations, customer feedback, quality findings, and operational signals. The answer should not be another chart to interpret. It should point to evidence and the next useful action.
?
Why did damaged delivery contacts increase?
Filtered to brand, channel, date range, and customer segment.
Question
1
Evidence found
Conversation themes, survey comments, and quality findings point to two repeat causes.
Evidence
2
Suggested next step
Review packaging guidance, escalation rules, and the top agent coaching pattern.
Action
The problem
Dashboards answer the questions you already knew to ask.
Customer operations leaders need to ask new questions every week: why one contact reason changed, what customers complain about after a policy update, which quality misses repeat, where automation escalates, and what teams should fix first.
Conversation Intelligence should reduce the distance between a business question and the evidence behind the answer. It is not a replacement for every dashboard. It is the layer that helps teams investigate, explain, and decide.
The goal is not more charts. It is faster movement from question to evidence to
operational action.
Conversation analytics + voice of customer
Ask across the signals that already describe the customer operation.
Salted can analyze different source types, each with clear meaning. A customer quote is not the same thing as an AI reviewer observation, and a transcript is not the same thing as a summary.
Separate customer verbatim from internal interpretation.
Use survey comments and ratings when the question is about what customers said directly.
Track patterns without losing the conversation behind them.
Explore contact reasons, channels, brands, teams, outcomes, customer sentiment, and repeated themes alongside the conversations that produced them.
Ask what the review system is seeing.
Analyze low-scoring themes, repeated quality misses, knowledge issues, coaching patterns, and automation behavior.
Workflow
Move from one-off questions to reusable operating views.
The first question should lead somewhere useful. Teams can narrow the source, inspect the evidence, compare segments, save a useful selection, and repeat the analysis as the operation changes.
This is where Conversation Intelligence connects back to the rest of Salted: a finding can become a coaching topic, a knowledge update, an automation adjustment, or a product/customer operations discussion.
Ask
Start with a business question in natural language.
Filter
Choose date, brand, channel, reason, team, customer group, or saved selection.
Verify
Inspect the source material behind the answer.
Act
Turn the finding into coaching, knowledge, workflow, or automation change.
Connected to the platform
Conversation Intelligence is stronger when it sits near the work.
The answers are more useful when they can read the same conversations, feedback, quality findings, knowledge, and automation outcomes that the operation already uses.
Explain the patterns behind the scores.
Ask why certain teams, channels, policies, or contact reasons produce more review findings.
Understand what happens in live work.
Analyze queues, escalation points, agent actions, customer context, and follow-up work.
Measure automation beyond containment.
Review completion patterns, guidance requests, customer feedback, and where automation should ask for help.
Find gaps between guidance and reality.
Surface policies that confuse customers, answers that drift, and knowledge updates that should be made explicit.
Buyer questions
What Conversation Intelligence should answer quickly.
Is this a dashboard or a chat interface?
It is an analysis layer over operational data. Dashboards are still useful for fixed metrics; Conversation Intelligence helps teams investigate new questions as they appear.
What data can it analyze?
It can work across conversations, summaries, customer feedback, quality findings, and operational attributes such as date range, channel, brand, team, reason, queue, and customer group.
Can we quote customers from the answers?
Only when the source is customer verbatim, such as transcript turns or survey comments. AI-written summaries and review observations should be treated as analyst notes, not customer quotes.
How is it different from quality reporting?
Quality reporting shows what the review system found. Conversation Intelligence lets teams ask follow-up questions across reviews, customer comments, conversations, and operating context.
Can it support executive questions?
Yes. It is useful for contact drivers, customer sentiment, policy friction, quality themes, automation performance, and recurring operational issues.
Does it replace human analysis?
No. It helps analysts and leaders get to evidence faster. Important findings should still be reviewed, scoped, and turned into a concrete operational change.
See what your conversations already know.
Ask better questions across conversations, customer feedback, quality findings, and the operational context behind them.
Book a live demo