Industry solution

Conversational AI for debt collection and past-due accounts

Manual dialing on past-due accounts is expensive, agents burn out on monotonous reminders, and one harsh phrase turns into a complaint and a compliance risk. The platform hands first-stage, respectful payment reminders to an AI agent, captures promises to pay, and analyzes 100% of calls into wording metrics — improper language and disclosure of personal data are flagged automatically. Collection becomes controlled and considerate, not sampled.

Start free Gentle payment reminders with a voice AI agent

Collection pains

Where past-due work breaks down on calls

Calling is expensive

Early-stage past-due work needs the same respectful call to hundreds of debtors. There aren't enough live agents for the whole volume, and every hour of their time is money.

Agents burn out

Monotonous reminders and tense conversations wear people down fast. Turnover rises, and with it both the quality and the tone of customer contact fall.

Compliance risk in wording

One improper phrase, a threat, or disclosing a debt to a third party means a complaint, a fine and a reputational hit. Wording gets checked, at best, on a couple of calls.

Promises to pay are lost

A customer named a payment date — and it stayed in the agent's head or in a hurried note. Promises never come together into one picture, so the next contact is made blind.

What the platform gives you

Every pain is closed by a specific pillar

Respectful reminders on autopilot

The AI agent calls early-stage past-due accounts itself, calmly and politely reminds about the payment, checks the reason for the delay and captures when the customer is ready to pay. Live agents are left only the hard cases.

A gentle, even tone on every call — no pressure, no improvisation.
The same early past-due volume is worked without added headcount.
The promised payment date and the reason for delay flow into record fields.

AI outbound agent

Wording control on 100% of calls

Every conversation — the agent's and the live operator's — is analyzed into compliance metrics: detection of profanity and personal data, tone and sentiment. The whole stream is checked, not a sample.

Metrics for improper language and personal-data mentions on every call.
Tone and sentiment of the conversation — you see where an agent slipped into pressure.
An automation catches a risky call and sends an alert to email or a webhook.

Call analysis

Analytics on promises and reachability

Promises to pay, connect rates and the outcome of every call add up into dashboards. You see reachability by time of day and by agent, and semantic search finds the conversations you need by meaning.

Captured promises to pay and their dates — in one place, not in notebooks.
Reachability and outcome by time of day — when people actually pick up.
Dashboards and semantic search across the whole archive of past-due calls.

Advanced analytics

Scenarios

How it works in collection

1
A gentle reminder with a captured promise

On early past-due accounts the AI agent calls and calmly, respectfully reminds about the payment: it states the amount and due date, checks that everything is fine, and asks when it's convenient for the customer to pay. It's a reminder, not pressure — and the tone is the same on every call.
If the customer names a date, the agent captures the promise to pay into the record fields, and the outcome is visible in analytics right away. Hard and disputed cases are handed to a live agent along with the transcript, so they enter the conversation prepared.
2
100% compliance control of wording

Every call is analyzed automatically into compliance metrics: profanity and mentions of personal data are detected and tone is scored. The whole stream of conversations is checked — the agent's and live operators' — rather than a couple of random recordings on spot-review.
An automation rule catches a risky call — a harsh tone, improper wording, or disclosure of the debt — and immediately sends an alert to a manager by email or to an external system via webhook. A violation is visible the same day, not after a complaint.
3
Analytics on promises and reachability by time of day

All promises to pay, connect rates and outcomes add up into dashboards: how many promises were captured, which dates were named, how reachability differs by agent and by time of day. You see the hours when debtors actually pick up, and when a calling wave goes to waste.
The copilot assembles any set with a plain-language request — for example, every call where a promise to pay was given but the payment never came through — so you can plan the next contact precisely, not blindly.

Result

Past-due work becomes considerate and controlled: routine reminders go to the AI agent, wording is checked on every call, and promises to pay come together into one picture.

Respectful reminders across the entire early past-due volume, with no added load on agents.
Wording control on 100% of calls instead of spot-review sampling.
Promises to pay and their dates are captured automatically and visible in analytics.
Risky conversations are flagged with alerts the same day, not after a complaint.

Key facts

Where to start: Upload your call archive or connect telephony — integrations aren't required for wording control and analytics.
Tone: The AI agent runs a respectful payment reminder, not debt-shaming: an even, calm tone on every call.
Compliance: Every call scores metrics for profanity and personal-data mentions, and an automation rule alerts on risky conversations.
Promises: The promised payment date is captured into record fields and feeds analytics on reachability and outcome.
Data: Collected fields and transcripts flow into your CRM (Bitrix24, amoCRM/Kommo, RetailCRM, Megaplan) or an external system via webhook.

FAQ

Frequently asked questions

Won't the AI agent pressure the debtor?

No. The agent's scenario is a respectful payment reminder: it states the amount and due date, calmly checks the reason for the delay and asks when it's convenient for the customer to pay. You set the tone, and it stays equally even on every call — no improvisation, no pressure.

How is improper wording controlled?

Every call — the agent's and the live operator's — is analyzed automatically into compliance metrics: detection of profanity, personal-data mentions, tone and sentiment. An automation rule catches a risky conversation and immediately sends an alert to a manager by email or to an external system via webhook.

Where are promises to pay captured?

When a customer names a payment date, it's saved into the record fields and feeds analytics. Dashboards show how many promises were captured and which of them didn't lead to a payment, and the copilot assembles the set you need with a plain-language request for precise follow-up.

Do I need a telephony integration to start?

No. For wording control and analytics it's enough to upload your archive of recordings. Telephony (MANGO OFFICE, Telfin, Sipuni, Zadarma, UIS and others) and the calling agent are connected later, when you want to automate the payment reminders themselves.

Do audio and data stay under our control?

Yes. Accounts are fully isolated, and access to recordings is limited to you and those you share with. For sensitive data a self-hosted worker is available: transcription and diarization run inside your perimeter, and audio never leaves it. We don't claim any certifications.

Industry solution

Use cases

Gentle payment reminders with a voice AI agent Script and standards control on every conversation Quality control on 100% of conversations, not a sample

Solutions

Sales & call centers Customer support Healthcare & clinics Real estate

Ready to start?

Turn every conversation into data, knowledge and action

Start by analyzing your conversations — no risk, no bots required. The platform turns your archive into data and a knowledge base, and voice agents plug in when you're ready.

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