of cancelling patients retained
The manual scoring department was closed, every call is scored against a 30-point checklist, and cancellations turn into rescheduled appointments.
The Expert clinic networkSpeech Analytics · 2026
Every customer conversation under control
Speech Analytics reviews every call and chat in Uzbek and Russian, not a two per cent sample.
We will show the review on your own recordings
How the system reviews a single call
Next to the recording: a transcript by speaker, checklist marks, customer emotion and the topics the system found itself.
What the system hears in a conversation
The same conversation is reviewed from both sides: how the agent spoke and what the customer said.
- Script compliance: greeted, introduced themselves, verified identity, said goodbye
- Courtesy and tone: interrupted, raised their voice, argued
- Handling objections: heard the doubt and what they answered
- Best practice: how the highest converters phrase things
- Policy breaches: promised something the company does not do
- Why they call: the real reasons, not the topic in the record
- What they complain about: timelines, fees, quality, repeat enquiries
- Who they compare you with: competitor mentions and their terms
- How they react to offers: what lands and what passes them by
- Who is about to leave: direct and indirect signs of churn
Build a checklist for your business
Criteria are picked from a ready library. The score follows the checklist you built — no developer needed.
Ready-made criteria
- Introduced themselves to standard
- Used the customer’s name
- Clarified what the customer needed
- Verified identity
- Handled the objection
- Offered a credit product
- Offered insurance
- Did not interrupt the customer
- Stated the terms and timelines
- Closed to standard
A criterion is set up by keywords or by a language model: it grasps the meaning even when the words differ.
Everyone gets their own screen
The reviews are the same, the screen is not: the agent sees their own calls, the manager sees team reports.
I see my calls reviewed and I understand why the score went down.
My calls this shift
Call 4821387
Call 4819874
Call 4817691
Weak spot this week: handling the rate objection
I check everything, not a sample, and it takes minutes instead of hours.
Review queue
Need attention12
Have remarks58
All to standard930
The system itself moved up what is worth listening to again
I see who is falling behind this shift and on exactly which criterion.
Shift, average score
Sardor Kh.68
Nilufar A.84
Aziz K.93
The rate objection is slipping: three agents out of seven
I look at where the money leaks, not at who greeted whom.
Reasons for enquiries this week
Questions about the rate31%
Card blocked24%
Card delivery times18%
Competitor mentions rose by 9% over the week
- More than 30 chart templates: a report is assembled from ready blocks
- A workspace per role: agent, supervisor, quality controller, manager
- Role-based access: who hears which calls and sees which reports
- The system adapts to your processes, not the other way round
Ask your own calls
Describe the situation in plain words: the search goes by meaning. The answer comes with a quote and a timestamp.
What people ask most often
- All calls where the customer mentioned the car loan rate
- Where the agent stayed silent for more than 10 seconds
- Who mentioned competitors last week
- Calls with a threat to switch to another bank
The search covers every conversation at once — calls and chats alike.
Uzbek and Russian in one model
A customer may switch language mid-sentence — the transcript does not fall apart because of it.
Call transcriptUzbekRussian
CustomerKartam bloklanib qoldi, что делать? Оплатить не могу.
AgentHozir tekshiramiz. Назовите последние четыре цифры карты.
CustomerOxirgi raqamlar — 4417. Вчера в магазине отказ был.
AgentKoʻrdim, blok avtomatik qoʻyilgan. Снимаю ограничение, попробуйте через пару минут.
Around 95% accuracy
On telephone recordings, where line quality and background are far from a studio.
Trained on your vocabulary
The model learns product names and internal abbreviations from your own recordings.
20+ industry dictionaries
Banking, telecom, healthcare, public services: terms come as a ready set.
What this gives the business
The same reviews solve different jobs — depending on who opens them.
up to +15%conversion of calls into target actions
up to +50%compliance with service standards
up to −20%operating cost of quality control
up to −20%customer churn
- You can see at which step the deal breaks and which phrase saves it.
- Wording from the best conversations spreads across the team.
- Competitor mentions are collected separately, with what is said about them.
- Every conversation is scored, not the controller’s sample.
- A complaint shows in tone and wording before the customer writes it.
- One set of criteria for everyone: the recording settles any dispute.
- The weak spot shows as a specific criterion, not as “try harder”.
- Failed conversations go to the AI Trainer as an exercise.
- A new hire reaches the line with their first calls already reviewed.
- Reasons for enquiries collect themselves: you see what breaks in the process.
- Decisions rest on every conversation, not on a shift report.
- Reports go where people look at them: your BI or an export.
How it is rolled out
We start with your recordings rather than an install: result first, system second.
Reviewing your recordings
You send an export of calls — we show a report: scores, topics, emotion.
A checklist for your standard
We build criteria from your policy: ready ones from the library, your own without a developer.
Connection and pilot
We pull recordings from your telephony and return results to the CRM. The pilot runs on part of the team.
Rollout to everyone
We refine the criteria after the pilot and roll out across the whole contact centre.
It fits into what you already have
We take recordings from your telephony and return results where people look at them.
Telephony and chats
- Asterisk
- Avaya
- Naumen
- Telegram
CRM and BI
- Bitrix24
- 1С
- Power BI
- Metabase
Export
- XLSX
- CSV
- JSON
The same technology works for meetings
Meeting Minutes runs on the same recognition engine: a transcript with speakers separated, decisions and action items from the conversation.
Related case studies
Read the case study: The Expert clinic network: 100% of calls instead of 1%, and patients retained
to sales over the phone
100% of inbound calls against consistent criteria, real-time alerts about fraud, compliance with the sales stages up from 41% to 94%.
Lemana PROSpeech Analytics · 2026
of outbound calls under control
Feedback to agents in real time instead of once a week; trainers work from real calls rather than blanket training.
Renaissance LifeSpeech Analytics · 2026
Still have questions?
How many calls a day can the system handle?
The limit is server capacity, not architecture: processing runs in parallel and scales by adding nodes. We will calculate the figure for your load during the pilot.
How soon does a score appear after a call?
In the normal mode the review is ready a few minutes after the conversation ends. If you need it in real time, that is what the AI Prompter does: it works during the call itself.
Can we analyse chats, not just calls?
Yes. Conversations from chats and messengers go through the same checklist as calls and land in the same reports.
Where are the recordings and transcripts stored?
Wherever you decide. On-premise, everything stays inside your network and never leaves it. In the cloud option the data sits in a TIER III data centre.
How much does it cost?
The price depends on the number of agents, the volume of recordings and the deployment option. We will work it out after a short conversation about your task.
Send us 100 calls and we will show what is in them
We will review your recordings on our side and show a report: scores, topics, emotion and what you had not heard.
