per customer record in CRM
The agent puts the phone down and the record is already written: topic, resolution, next step. Half as many errors.
A contact centreAI Agent Factory · 2025Client under NDA
A shift where every agent performs like your best one
The trainer before the line, the prompter during the call, the analytics afterwards — across one shift.
A shift in which every agent performs like your best one
- 9:00
A newcomer does a practice run
Before going on the line — two scenarios with an AI customer: an angry subscriber and an objection about price. Score 84, with the weak spot flagged.
AI Trainer - 9:30
Biometrics on every inbound call
Customers are recognised by voice in the first seconds. Code words and security questions have gone from the line.
Voice Biometrics - 11:40
The prompter on a difficult call
The customer is calling for the second time about a blocked card. The prompt with the clause of the regulations arrives while they are still explaining.
AI Prompter - 13:00
The bot takes the lunchtime peak
Statuses, balances and bookings go to the bot. The agents are left with the difficult cases and the queue does not grow.
Voice and Chat Bots - 15:30
The assistant answers from the regulations
An agent asks about a new fee — and gets the answer with a link to the clause, without walking over to a supervisor.
AI Agent Factory - 17:45
The evening analytics report
Every call of the shift has been checked. The manager sees each agent’s score and what is worth listening to again.
Speech Analytics
For outsourced contact centres
Each client gets their own checklist and their own report — from one system.
Consistent criteria per client
A bank’s script and checklist differ from a retailer’s. The analytics checks each project against its own criteria.
A report for the client
The client sees the scores for their own calls and the trend, with no access to anyone else’s projects.
Rotating agents
A newcomer on a new project practises on its scenarios before their first live call.
For in-house contact centres
We connect to what you already run: telephony, CRM, knowledge base, dashboards.
- Asterisk, Avaya, Naumen and others over SIP and MRCPv2
- Bitrix24, 1C and any CRM over API
- Power BI, Metabase and exports
- Your own knowledge base for the prompter and the assistant
The economics of a shift
How many calls the analytics checks and how many the bot takes is counted on your own numbers.
- Calls checked instead of a controller’s sample
- 100%
- Routine contacts taken by the bot the share depends on the industry
- 30 %
- Time per CRM record instead of 5–7 minutes
- 5–10 s
What this affects
up to −50%agent errors
up to −30%repeat contacts
up to +20–30%to NPS
up to −25%in costs
Still have questions?
Can we start with one product?
Yes, and most people start with Speech Analytics: it shows where the problems are. Then the prompter and the trainer are added to close them, and a bot to take the routine off the line.
How is data from different clients kept apart?
Projects are isolated: their own checklists, their own reports, their own access. A client only sees their own calls.
What about agents who only speak Uzbek?
Everything works the same: recognition, prompts and the trainer work in Uzbek, Russian and the two mixed. The checklist is the same and the language of the call does not matter.
Let us look at one of your shifts
Send us a day of recordings. We will return a review for every agent and show what the bot would have taken.
