Did the agent or AI follow the policy?
Review whether the conversation followed the business rule, process, or scorecard definition that applies to that work type.
Salted Quality Intelligence
Review. Improve.
Salted brings AI Quality Management into customer operations: review conversations at scale, check them against expected behavior and approved knowledge, and show whether the fix belongs in coaching, policy, knowledge, workflow, or automation.
Coverage
At scale
Move beyond manual samples and inspect more of the real operation.
Evidence
Source-linked
Review findings against policy, scorecard, knowledge, or expected answer.
Action
Confirm or dispute
Human review keeps noisy AI findings from becoming operational truth.
Feedback
Improve
Confirmed issues point to coaching, policy, knowledge, workflow, or automation.
The problem
Sampling tells you how a few conversations went. It does not show the operation.
Traditional quality review is often a small manual sample. It catches some agent behavior, but it can miss the problems that hurt customers at scale: stale policies, inconsistent answers, missing knowledge, broken workflows, and automation behavior that no longer matches the business.
Quality Intelligence should answer a more operational question: did the customer get the right experience, what evidence supports the finding, and what source should change if they did not?
Quality is no longer only a scorecard. It is how the operation learns where to
coach, clarify, or change the workflow.
What it reviews
Measure conversations against the work your team actually expects.
Salted can support classic quality criteria and operational checks: tone, process adherence, resolution quality, compliance, language quality, and whether the customer-facing answer matched approved knowledge.
Did the customer get the right answer?
Compare what the customer was told with approved source material, then show the evidence behind the finding.
What should the team learn from it?
Turn patterns into feedback for agents, team leads, knowledge owners, workflow owners, and automation owners.
Improvement loop
The review is only useful if it changes the operation.
A quality finding should not die in a dashboard. Salted turns review outcomes into a practical path for confirming the issue and fixing the source.
Review at scale
AI reviews conversations where behavior, policy, knowledge, or outcome may matter.
Show the evidence
The reviewer sees the conversation context, the expected behavior, and the source.
Confirm or dispute
Human review separates real misses from noisy or uncheckable findings.
Fix the source
Update coaching, policy, knowledge, workflow, scorecards, or automation behavior.
Where it fits
Start with quality intelligence even if your contact center stays where it is.
Some teams want visibility, quality review, and coaching before they move agent work or automation. Salted can work with existing conversation data, then expand when the team is ready.
Other teams run Agent Desktop or AI agents in Salted. In that case, quality uses the same conversation state, actions, knowledge, routing, and customer feedback that powered the interaction.
1
Existing contact center
Conversations are ingested and reviewed without moving the live operation first.
Start
2
Agent Desktop
Quality reads the same state, notes, routing, and customer context.
Expand
3
AI Agents
Automation behavior becomes reviewable and improvable inside the same loop.
Automate
Why it is different
Quality is stronger when it shares the same context as the conversation.
Separate review tools can score transcripts. Salted can connect review to the operating system: the conversation, source material, agent desktop, automation behavior, customer feedback, and next improvement.
Show the policy or knowledge behind the finding.
Reviewers should not have to trust a black-box score. The source behind the review should be visible enough to inspect.
Use people where judgment still matters.
Team leads can confirm, dispute, calibrate, and guide the system instead of accepting every automated finding.
Point to the next fix, not only the score.
A confirmed miss should tell the team whether to fix coaching, policy, knowledge, workflow, or automation.
Buyer questions
What Quality Intelligence should answer quickly.
Is this just automated quality scoring?
No. Scoring is one part. The larger value is connecting findings to the expected behavior, source material, human review, coaching, and operational improvement.
Can it work without Agent Desktop?
Yes. Quality Intelligence can start with conversation data from an existing contact center stack, then expand into Agent Desktop or automation later.
What can be reviewed?
Tone, process, resolution quality, compliance, language quality, knowledge accuracy, expected answers, customer feedback, and automation behavior.
How does it reduce false alarms?
Strong review design should show evidence, let humans confirm or dispute, and route only findings that are worth attention.
Who uses it?
Quality teams, team leads, support operations, knowledge owners, automation owners, and executives who need to understand customer experience at scale.
What happens after a miss is confirmed?
The next step should be specific: update knowledge, change a workflow, tune automation, coach an agent, or clarify a policy.
See what your support operation is missing.
Review real conversation patterns and decide where quality findings should turn into coaching, knowledge, workflow, or automation improvements.
Book a live demo