Conversation analysis
What customers say.Not just how often they call.
AI phone conversation analysis turns customer calls into transcripts, summaries and topic trends, so the team can see why people call and where conversations get stuck. All with callers informed, restricted access and a defined retention period.
Calls hold answers nobody has time to listen to
Every call says something about the product, the procedures or what frustrates customers. But listening to them takes hours, so decisions are made on impressions, and recurring problems only come to light once they turn into complaints.
- There is no clear picture of why customers call
- Quality checks are done on a small, randomly chosen sample
- Information from calls does not reach the product or operations teams
- Call summaries are written by hand or not at all
Who it makes sense for
- Support and sales departments that already record calls lawfully
- Managers who want to know the main reasons for calls
- Quality teams that today listen manually to only a few calls a month
- Companies that use voice agents and want to check their conversations
What AI call analysis can do
- 01
Transcription
Turns recordings into searchable text, with speakers separated where audio quality allows.
- 02
Summary per call
Writes a few lines on the reason, what was discussed and what was agreed, attached to the customer record.
- 03
Topics and trends
Groups calls by reason and shows how these change from one week to the next.
- 04
Support for quality checks
Flags the calls worth a person's attention, based on criteria agreed together: complaints, escalations, long calls.
- 05
Masking sensitive data
Can automatically hide card numbers, CNPs (Romanian personal identification numbers) or other data that should not be kept in the transcript.
How it works
From recording to conclusions
The steps marked in yellow are approved by a person.
- Input
- AI
- Human in the loop
- Result
- 01
Input
Lawfully recorded call
Only calls where participants were informed about the recording.
- 02
AI
Transcription and masking
Speech becomes text, and sensitive data is hidden.
- 03
AI
Summary and topics
Each call gets a summary and its reason categories.
- 04
Human in the loop
The quality team checks
A person listens to the flagged calls and confirms the conclusions.
- 05
Result
Trend report
Managers see the main reasons and how they change over time.
What call analysis requires
- Lawfully obtained recordings, with participants informed and a stated purpose
- The list of topics or call reasons relevant to your company
- The quality criteria the team already uses when listening to calls
- Retention rules: how long recordings, transcripts and summaries are kept
- The access matrix: who can see full transcripts and who only aggregated reports
Possible integrations
- Cloud call centre platforms
- Virtual phone systems (cloud PBX)
- Twilio
- HubSpot
- Salesforce
- Zendesk
- Freshdesk
- Power BI
- Looker Studio
- Cloud call centre platforms
- Virtual phone systems (cloud PBX)
- Twilio
- HubSpot
- Salesforce
- Zendesk
- Freshdesk
- Power BI
- Looker Studio
Each integration's compatibility is checked during the analysis phase, for the systems and versions you use.
Control and transparency
Where a person decides
- Conclusions about the quality of a call are drawn by a person who listened to it, not by the automated analysis
- The analysis is not used for automated assessment or disciplining of employees
- The topic list and flagging criteria are set and reviewed by the team
- Access to full transcripts is approved by a designated person and logged
Limits and clear rules
- Transcription works best on clear audio; noise, accents and overlapping speech can cause errors
- Summaries may miss nuances or misread irony; in important cases, a person listens to the call
- We do not carry out covert monitoring of employees; employees are informed of the purpose and the way calls are analysed
- We do not analyse calls recorded without informing the participants
- Voice emotion analysis is not included by default; it would require a separate assessment of necessity and risks
How we check the quality of transcripts and summaries
We agree the indicators together before launch and track them afterwards. The values depend on the process, the data and the volume.
- Transcription accuracy
- A sample of calls transcribed manually and compared with the automated transcript
- Topic accuracy
- The quality team periodically checks whether calls were assigned to the correct reason
- Summary faithfulness
- Summaries compared with the call as heard, marked “correct”, “incomplete” or “wrong”
- Masking of sensitive data
- Checked on a test set with fictitious data, before launch and after every change
How we implement conversation analysis
- 01
Legal framework and purpose
We clarify the purpose of the analysis, how callers and employees are informed, the retention period and, where applicable, the need for an impact assessment.
- 02
Topics and criteria
With the team, we set the list of call reasons and what makes a call worth a person listening to it.
- 03
Test on real calls
We run the analysis on a limited set of calls and compare the results with the quality team's assessment.
- 04
Reports and access
We configure the reports and access rights, then periodically review the topics and transcription quality.
Use cases
The scenarios below are conceptual examples, built to illustrate what is possible. They do not describe implementations delivered by WEB-AZ.
- Energy supplier
Call reasons after a change to the bill
The report shows that many calls that week are about a new line on the bill, and the team decides to clarify the explanation sent to customers.
Conceptual example - Online electronics store
Recurring problems with a product
Calls about the same model are grouped automatically, and the product team receives the summaries to decide what to check with the supplier.
Conceptual example - Private clinic
Summaries attached to appointments
After each call, reception finds a short summary in the patient record, without medical data being included in the general reports.
Conceptual example
Frequently asked questions
What is AI phone conversation analysis?
It is the automated processing of lawfully recorded calls: transcription into searchable text, a summary for each call and grouping of calls by reason, so you can see trends from week to week. It does not answer the phone; it analyses calls handled by people or by voice agents, and the important conclusions are drawn by a person.
Do we have to inform callers and employees?
Yes. Callers must be told that the call is recorded and for what purpose, and, if they are talking to an automated assistant, that too. Employees must be informed that calls are analysed and why. We do not build covert monitoring systems.
How long are recordings and transcripts kept?
As long as necessary for the stated purpose, agreed together with you. We can configure automatic deletion and keep only summaries or aggregated data after a set period.
What happens when the analysis is not sure about a call's topic?
The call is marked “unclassified” or “uncertain” instead of being forced into a category, and the quality team can review and assign it manually.
Can it be used to assess employee performance?
The analysis can support coaching and training, but we do not recommend or build automated assessments or sanctions. Any conclusion about an employee must be drawn by a person, with the team informed in advance.
Who has access to transcripts?
Only the people set out in the access matrix. Managers may see only aggregated reports, while full transcripts are available to a small group, with access logged.
Can’t find the answer? Write to us and we’ll reply directly.
Want to find out why your customers really call?
Tell us how you record calls today and what you would like to learn from them. We start from the purpose and the privacy rules.
Related solutions
- AI voice agent for inbound callsThe first line for high volumes of support and order calls: caller identification, answers from approved sources, a ticket created and a transfer with context.
- AI evaluation and monitoringTest sets, checks at every change, human spot-check reviews and cost tracking, so you know how your AI performs over time.
- AI agent for data analysisYou ask a question about your company's data in natural language, and the agent writes the query, runs it on authorised sources and explains the result, showing how it got there.