AI call analytics means automatically transcribing your phone conversations and extracting structured insight from them: why people called, what they wanted, how they felt, how the call ended and where it went wrong. Instead of listening to a handful of random calls, you get a full picture of what customers ask for and how well your flows answer. Voxivra builds these solutions to fit your existing telephony, not the other way around.
What call analytics gives you
Most organizations know their call volumes and wait times, but far less about why people call. AI analytics fills that gap:
- Real call reasons, classified automatically, rather than wrap-up codes picked by hand.
- Sentiment and friction points: moments where tone drops, frustration keywords, repeated requests.
- Compliance checks: confirming required disclosures were read, or that sensitive data wasn’t spoken where it shouldn’t be.
- Automatic summaries pushed to the CRM, cutting after-call work.
- Automation candidates: the analysis often shows which requests an AI voicebot could handle first.
Use cases
Contact centers
Track call reasons by day and queue, find the root causes of repeat calls, and coach agents from real examples instead of tiny samples. For the specific challenges of these environments, see our contact centers page.
Customer service and e-commerce
Measure how a delivery incident or a returns policy change affects call volume and tone, then adjust flows accordingly.
Measuring voicebot and IVR performance
Review unresolved conversations, transfers and hang-ups to keep improving recognition and flows. Our article on call automation KPIs covers which metrics matter. This kind of tracking is what made it possible to measure the 80% call automation rate of the healthcare appointment voicebot in our case studies.
How Voxivra builds it
Audio capture
We pick up audio wherever it lives: Asterisk or FreePBX recordings, real-time streams through Asterisk ARI (external media or snoop channels), or files exported by your contact center platform. Where possible, each party is captured on a separate track for more reliable transcription.
Transcription and understanding
- STT using Whisper-style or commercial engines, benchmarked on your recordings, with diarization (who spoke when) and custom vocabulary.
- NLU or LLM to classify call reasons, extract entities (order number, product, date), write a summary and score sentiment.
- Redaction of sensitive data (card numbers, identifiers) before storage.
The technology choices are covered in more depth on our STT, TTS and NLP expertise page.
Data and reporting
Results are stored in a structured database, then exposed through an API, written to your CRM or BI tool, and surfaced in dashboards built for managers and supervisors. Alerts can flag a sudden spike in a given call reason.
| Mode | Typical use | Complexity |
|---|---|---|
| Post-call (batch) | Reporting, trends, quality review | Low to medium |
| Near real time | CRM summary as soon as the call ends | Medium |
| Real time | Agent assist, supervisor alerts | Higher |
Metrics worth tracking
Analytics only pays off if it answers concrete questions. These are the indicators we most often set up:
- Call reason distribution over time, by queue, line or region, with drill-down to example calls.
- Repeat contact rate: callers who call back within a few days about the same issue, a strong signal of unresolved problems.
- First-contact resolution, estimated from the conversation outcome and repeat calls.
- Sentiment trajectory: how tone evolves from the start to the end of the call, rather than a single score.
- Hold and silence time inside the call, often a sign that agents are searching for information.
- Transfer and escalation reasons, especially useful for tuning an IVR or voicebot.
- Talk-over and interruptions, which can point to coaching needs or confusing scripts.
Each metric comes with its definition and known limits, so managers know how far they can trust it. We prefer a small set of reliable indicators reviewed every week over a dashboard with fifty tiles nobody opens. The analysis also makes it easier to build a business case: once you know the share of calls about order status or appointment changes, you can estimate what automation would actually free up.
How a project runs
- Audit: available audio sources, recording quality, volumes, target systems and privacy requirements.
- Design: call-reason taxonomy, metrics, redaction and retention rules, dashboard mockups.
- Build: capture pipeline, transcription, analysis and integrations.
- Test and monitor: comparison against a hand-labeled sample, category tuning, then regular quality reviews.
What to expect and prerequisites
The first few weeks usually go into refining classification: the call reasons you expect up front don’t always match reality. Automated analysis also carries a margin of error, which is why it should be validated against a sample.
Useful prerequisites:
- access to recordings or the live audio stream;
- a caller notification and data retention policy, reviewed with legal counsel in light of Law 25, PIPEDA or GDPR as applicable;
- a list of decisions the analysis should inform (training, automation, quality).
A first batch of recordings is usually enough to produce an initial map of call reasons and set priorities.