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Nivision
Use case

Quality assurance on every call, not a sample.

Manual QA reviews a handful of calls a week and hopes they are representative. Nivision scores and audits every conversation, automatically - so quality and compliance stop being a sampling exercise.

Automated QA & compliance

Sampling misses what matters.

A typical QA team listens to two or three percent of calls. The problem call - the missed disclosure, the mishandled objection, the agent quietly drifting off-script - is almost always in the ninety-seven percent nobody hears. By the time a pattern surfaces, it is already a liability.

AI Call Analytics Platform | Conversation Analytics Software

Score and check every conversation.

Nivision runs every call through a classifier built for your QA criteria. Each call gets a structured score, extracted custom fields and an automatic compliance check against the disclosures and steps you require. Alerts fire the moment a call fails a rule, and aggregate reports show where quality is trending - across the whole floor, not a sample of it.

01

Classifier-driven scorecards

Define your QA criteria once as a classifier - every matching call is scored automatically against it.

02

Compliance checks on 100% of calls

Required disclosures and process steps are verified on every conversation, not a weekly sample.

03

Instant fail alerts

A missed disclosure or a low score raises an in-system alert with full context the moment it happens.

04

Audit-ready records

Every call carries its transcript, score and extracted fields - a defensible record with no extra work.

Automated QA & compliance

The outcome

  • Compliance verified on 100% of calls, not 2-3%
  • Risk caught in hours, before it becomes a pattern
  • QA teams freed from manual review to focus on coaching
Automated QA & compliance

FAQ

How many of our calls does the QA actually cover?

100% of them, automatically. Instead of a manual sample of a few percent, every call is checked against the scorecard your team defines - so a violation doesn't wait for the monthly review.

Who defines the scoring criteria?

You do. Each call type gets its own scorecard - opening, discovery, mandatory statements, empathy, closing - and the system scores every call against it, consistently and objectively.

What happens when a violation is detected?

A real-time alert goes to whoever you define - shift manager or compliance owner - with a link to the call and the relevant transcript segment. Daily or weekly digest reports are available too.

Does this fit insurance and finance compliance requirements?

Yes. Reviewing 100% of calls with a full audit trail is exactly what regulators expect - automatic detection of disclosure gaps and an audit-ready record for every call.

What is automated call QA software?

Automated call QA software scores calls against a fixed set of criteria without a person listening to each one. You define what a passing call looks like per call type — disclosures made, script followed, objection handled — and every call is checked against that, with the evidence quoted from the transcript.

How does AI call quality assurance compare with manual QA?

On coverage and consistency, not on judgement. Manual review reaches a sample; AI call quality assurance reaches every call, and applies the same classifier each time rather than whichever reviewer heard it. Judgement calls, disputed scores and the coaching conversation still need a person.

Is this AI call quality assurance software or a QA service?

Software. You configure the criteria per call type and the platform scores against them; nobody at Nivision listens to your calls. That is the difference from outsourced QA — the coverage is 100% and the cost does not scale with how many calls you want reviewed.

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