Review every conversation, not a 2% sample
Automated contact centre quality assurance: every AI and human conversation scored against your own published rubric, with evidence, coaching notes and calibration instead of a manual sample.
The problem
- Manual quality review can only ever cover a small sample of conversations, so most of what customers actually experience is never inspected.
- Different reviewers score the same conversation differently, and by the time a score lands the coaching moment has passed.
- When a regulator, client or executive asks why a conversation was judged compliant, the evidence is scattered across spreadsheets.
How Quantara Flow AI does it
Your rubric, published and versioned
Quality rubrics are configured per workspace with weighted attributes, pass thresholds, channel, language and AI/human/mixed scope. Only a published version can score, and each score records which version judged it.
Every eligible conversation queued automatically
Voice calls and chat sessions enter the audit queue when they end, so coverage is complete by default rather than dependent on who had time to review.
Attribute-level scoring with evidence
Each attribute is scored with the transcript evidence behind it, so a score can be inspected, disputed and calibrated instead of taken on trust.
Honest incomplete states
If the evidence a rubric needs is missing — for example a human agent's audio was not captured — the audit is marked incomplete rather than being given a pass or fail it cannot justify.
Coaching that reaches the agent
Reviewers attach coaching notes to the exact conversation and attribute, and agents and team leaders see them in their own workspace.
What your operation gains
- Complete quality coverage across AI-handled and human-handled conversations
- One consistent scoring standard per channel, language and interaction type
- Auditable evidence for every score, including who or what produced it
- Coaching tied to specific conversations instead of general feedback
What you can measure
Measured in your own workspace. We publish no benchmark figures we cannot evidence.
- Share of conversations audited, by channel and by AI versus human handling
- Average and distribution of rubric scores over time
- Attribute-level failure patterns across teams and queues
- Volume of audits marked incomplete, and why
- Coaching notes issued and acknowledged
Governance and data handling
- Rubrics, scores and coaching notes are scoped to your workspace and enforced with row-level security.
- Scores never overwrite history: rubric version, scorer and evidence are retained.
- AI-produced scores are labelled as AI-produced and can be reviewed or invalidated by a human.
Questions
Does it replace my QA team?
▾
No. It removes the sampling problem so your reviewers spend their time on calibration, disputes and coaching rather than on choosing which conversations to open.
Can we use our existing scorecard?
▾
Yes. Rubrics are configured per workspace with your own attributes, weights and pass thresholds, and can differ by channel and language.
What happens if the AI is not confident?
▾
The audit is returned as incomplete or routed for human review rather than being given a pass or fail.