Speech analytics: AI hears every conversation.
Speech analytics, or sales call analysis, is the automatic review of every conversation with a customer: the recording is transcribed and scored against a checklist. AI listens to every call and reads every chat, rates them by one standard and shows exactly where the manager lost the customer. Every conversation is checked, with no sample of a dozen recordings a month.
A quality control team that gets through everything.
What we do
We transcribe every call and every chat, score them against your criteria and put them into a report: who performed how, what was said, what wasn't, and at which point the conversation went wrong.
What we don't do
We don't score by gut feeling and we don't collect dirt. Every point is tied to a checklist item and to a quote from the conversation. A score without a quote just becomes a reason to take offence.
The quality control that isn't there.
Three calls out of a thousand get listened to
There is no time for more. Conclusions come from a random sample, while the problem may sit in exactly the conversations nobody opened.
The score depends on the mood
Different people score the same call differently. The salesperson feels it and stops taking the score seriously.
Nobody looks at the chats
Calls get listened to, but half the selling happens in messengers. That is also where half the clients are lost — silently.
The reason for losing is always «too expensive»
Everyone puts the same reason into the CRM because it closes the card faster. The real objections stay inside the conversations and never reach the manager.
Coaching in general terms
«Handle objections better» is not an instruction. Without a specific moment in a specific conversation, the salesperson doesn't know what to change.
The script lives apart from real life
The playbook was written a year ago and conversations go differently. Nobody knows which part of the script works and which part everyone has long been skipping.
What exactly the AI checks.
A checklist built for your process
The criteria come from your own playbook and your successful conversations: the opening, uncovering the need, handling price, the next step. Not a universal template.
Calls and chats
Everything is scored: telephony, Telegram, WhatsApp, website chat. A chat conversation is reviewed against the same criteria as a call.
Leaks and weak spots
Where the client asked a question and got no answer, where no next step was agreed, where the salesperson simply agreed with the objection. With a quote and a timestamp.
Salesperson scoring
A score for every criterion and the trend by week. You see not only who is weaker, but what exactly that person struggles with.
The real reasons for losing
The AI extracts from the conversations why people actually didn't buy and builds a frequency list. It usually differs from what is written in the CRM.
Automatic report to the head of sales
A daily and a weekly summary: what dropped, who to review, which three conversations to listen to. No manual exports and no reminders.
What call review looks like in practice.
Screens of the GetGate sales platform. AI reviews every completed call and puts the review into the deal card, suggesting the next step alongside. The manager edits it and sets the task with one button. Names and phone numbers are hidden.
Every morning the sales manager gets a summary for each rep: what was done yesterday, where no next step was set and which important clients nobody touched.
Call evaluation checklist: what AI checks and what the head of sales sees.
This is the base we start from. The wording is then rewritten for your process, but the key questions to ask of a conversation are the same in most sales teams.
| Criterion | What AI checks | What the head of sales sees |
|---|---|---|
| Opening | Whether the salesperson introduced themselves and named the company and the reason for the call | Who loses the customer in the very first sentences |
| Identifying the need | Whether they asked about the task, timing and volume before talking about the product | Conversations where the offer was made blind |
| Qualification | Did they ask about the budget and who makes the decision | Deals where the manager spends time on someone who isn't buying |
| Who talks more | Did the manager let the customer describe their need, or talk almost the whole time themselves | Managers who deliver a monologue instead of asking questions |
| Proposal | Whether the offer is tied to what the customer said | Where the salesperson reads out the price list instead of answering the question |
| Handling objections | Whether they answered the objection on its merits or agreed and backed off | Frequent objections and the quotes where the conversation broke down |
| Next step | Whether a specific action with a date was agreed | Deals that are about to stall |
| Promises to the customer | What the salesperson promised — a call-back, a deadline, a discount — and whether it is recorded in the CRM | Promises only the salesperson knows about |
| Tone of the conversation | Whether they interrupted, argued or pressured the customer | Conversations worth listening to yourself first |
| Wrap-up | Did the manager repeat at the end what was agreed | Where the customer and the manager left with different ideas of the agreement |
| Reply speed in chats | How long the customer waited for the first reply in the messenger and for replies during the conversation | Chats where the customer went cold while waiting |
AI speech analytics vs classic: what's the difference.
Classic speech analytics searches the transcript for words and phrases from dictionaries. Review by a language model reads the whole conversation and answers the checklist questions by meaning. Each approach has its own strength.
| What we compare | Classic speech analytics | AI review |
|---|---|---|
| How it finds a problem | By dictionaries of keywords and stop words: whether a phrase was said or not | By meaning: did the manager find out the need, did they answer the customer's question |
| Setup | An analyst builds dictionaries and rules and maintains them as products and wording change | Criteria are written in plain sentences from your own sales rules |
| A script phrase said "just to tick the box" | Counts, since the words were said | Counts only if it actually gave the customer something in the conversation |
| Reasons for refusal | Tags based on predefined words | The reason in plain words with a quote, including one nobody anticipated |
| Messenger chats | Depends on the system; often only calls are reviewed | Reviewed against the same checklist as calls |
| What the head of sales gets | Shares of calls with matched words and charts of them | A score for each item, a timestamped quote and what should have been said |
| Where it is stronger | Silence, interruptions, speech rate and search across a huge call archive | Judging what the conversation meant and a review you can use to coach the manager |
The approaches don’t exclude each other. If you already have a classic system, AI review can be set up alongside it for checklist scoring. How such review works on a live flow can be seen in the live GetGate project: the figures are below.
Three things you notice at once.
Not a sample and not just the loudest cases. A problem that is rare but expensive also makes it into the report.
The head of sales opens the summary and immediately sees the three conversations worth listening to, instead of hunting for them blindly.
The manager gets a quote from their own conversation and what they should have said instead. A general "do better" gives them nothing.
This is a coaching tool, not surveillance.
If it is turned into supervision
Salespeople will start talking for the report rather than for the client: reciting checklist items out loud and moving into messengers where they «can't be heard». Control that people hide from does not improve sales.
So this is what we advise
The criteria are open, the salesperson sees their own score, and the review is about the conversation rather than the person. The best moves from strong conversations are shared with the whole team — and then the system works for people instead of against them.
Where to start reviewing calls.
One signal, one action
Take a week of conversations and look at one thing only: how many have a date for the next step. Conversations without a date go back to the manager the same day. Other signals are added one at a time, and a signal nobody acts on is removed.
An excuse or a real objection
A customer with a real reason to say no repeats it in the same words. Someone who finds it awkward to say “no” gives a new reason each time: too expensive, need to consult someone. The difference only shows at volume, and these cases need different handling.
Value for the whole company
Customers’ frequent questions show what is missing on the website, in the commercial offer and in onboarding new staff. An objection that comes up for every manager at once points to the product or the price, and the people are not the issue.
A manager should still listen to a couple of conversations a week personally, or they lose the feel of how their team sounds.
First we count the losses, then the price.
The price depends on the volume of conversations, the number of channels and how complex the checklist is. The review and the estimate are free: we'll take a few of your real conversations, show what the AI sees in them, and you decide whether that is worth the money.
If it turns out that there are few conversations and the manager keeps up with them personally, we'll say so.
Related reads.
How to choose a speech analytics service
How transcription differs from meaning-level analysis, what to check in a speech analytics service before you buy, and how to run a pilot on your own calls.
DKDmitry Kuzminov
Sales team audit: a step-by-step checklist
How to run a sales team audit yourself: what to check in the pipeline, CRM, calls and procedures, where money is lost, plus a ready-made checklist.
AEAlexey ErshovFrequently asked questions.
What is speech analytics for sales calls?
It is the automatic review of every customer conversation instead of spot-check listening. Each call is turned into text and checked against a checklist: did the manager find out the need, name the next step, answer the objection. The result goes into a report for each employee that shows at which step and what exactly went wrong.
How is AI review different from classic speech analytics?
A classic system searches the transcript for words and phrases from dictionaries. AI looks at meaning: was the customer's need found out, was the question answered, was the next step set. So a script phrase said just to tick the box won't fool the score. The point-by-point comparison is in the table above.
Which checklist should be used to score a call?
The one you already use to score conversations — we carry it over. If there is no checklist, we build it from your own successful calls: we take conversations that ended in a deal and look at what they had in common. The base we start from is in the table above. A checklist invented from the outside scores someone else's work.
How closely do AI scores match human ones?
At the start we compare them with yours on real conversations and adjust the wording of the criteria until disagreement becomes rare. Disputed cases always open with a quote, so the decision stays with a person.
Some recordings transcribe worse — usually because of noise, a bad line or people talking over each other. Such passages are marked as unparsed and are not scored at random.
Do managers and customers need to be warned, and where are recordings stored?
Managers need to be told, and not only because of the law. Hidden monitoring gives a one-off effect and lasting distrust. Open criteria work better: people understand what they are assessed on and can influence it.
Customers too: recording a conversation is processing of personal data, so the notice is given at the start of the call or included in the terms on the website. Recordings and transcripts are stored on a server in Russia. This is personal data, so access to it is restricted, and what is stored and for how long is described in the personal data processing policy.
Will speech analytics replace the head of sales?
No. It takes listening and counting off their plate and leaves what they are needed for: talking to people, deciding on deals and changing the process.
Other AIBP products.
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