Telegram Bot for Sales and Support Automation

Automated processing of 2000+ requests per month and reduced customer response time from 2 hours to 30 seconds for a service center network.

Industry
Tech Service & Repair
Format
B2C
Duration
6 weeks
Stack
Python, aiogram, PostgreSQL, Redis

About the Client

A network of service centers for home appliance and electronics repair with 12 locations in Moscow region. Over 2000 inquiries per month through various channels.

Highly competitive market where response speed directly affects conversion. Customers leave for competitors if they don't receive a response within an hour.

Medium business, 50+ employees

Challenge & Problems

  • Managers couldn't process all requests — up to 30% of leads were lost
  • Average response time was 2+ hours during business hours
  • Night and weekend requests remained unanswered until Monday morning
  • No unified inquiry database — history was lost in chat threads
  • Impossible to track repair status without calling a manager
  • High call center load due to routine questions

Why standard solutions didn't work

Off-the-shelf bot builders didn't provide the needed integration with the client's internal CRM and didn't support complex request routing logic by device type and geolocation.

Project Goals

Reduce first response time

to 1 minute (was 2+ hours)

Automate request intake

100% of requests through bot

Reduce call center load

by 40%

Enable status tracking

without manager involvement

Integrate with existing CRM

real-time synchronization

Our Solution

Developed a Telegram bot with multi-level request processing, intelligent routing, and full integration with the client's CRM. The bot operates 24/7, automatically classifies device type, determines the nearest service center, and creates a request in CRM.

Request Intake Module

Step-by-step form with validation: device type, problem description, photo, contacts

Intelligent Routing

Automatic service center selection based on device type and customer geolocation

CRM Connector

Two-way synchronization with client's internal system via REST API

Notification System

Push notifications about repair status, readiness, pickup reminders

Admin Panel

Web interface for operators with request queue and analytics

Architecture

Microservice architecture on Python with asynchronous message processing. Main bot on aiogram 3.x, separate service for CRM integration, Redis for caching and queues.

Integrations

REST API for CRM communication, webhook for status updates, Yandex.Maps API integration for geolocation.

Security

Personal data encryption, rate limiting for spam protection, audit log of all actions, GDPR compliance.

Scalability

Horizontal scaling through Docker Swarm, ready for loads up to 10,000 messages per minute.

Development Process

1

Analysis and Design

Studied business processes, interviewed managers and operators, designed user flow and architecture. 1 week.

2

Prototyping

Created clickable dialog prototype, approved all scenarios with client. 3 days.

3

MVP Development

Implemented basic functionality: request intake, CRM integration, notifications. 2 weeks.

4

Testing

Functional testing, load testing at 1000 RPS, bug fixes. 1 week.

5

Launch

Phased launch at 3 locations, monitoring, feedback collection, adjustments. 3 days.

6

Scaling

Rollout to all 12 locations, staff training, documentation handover. 1 week.

Technology Stack

Backend

Python 3.11
aiogram 3.x
FastAPI
Pydantic

Databases

PostgreSQL 15
Redis 7
SQLAlchemy

Integrations

Telegram Bot API
CRM REST API
Yandex.Maps API

Infrastructure

Docker
Docker Swarm
Nginx
Let's Encrypt

Monitoring

Prometheus
Grafana
Sentry

Results

Measurable Results

30 seconds

First response time

was 2+ hours

+35%

Request conversion

due to instant response

-45%

Call center load reduction

routine questions handled by bot

100%

24/7 request processing

night requests are not lost

+22 points

Customer satisfaction (NPS)

based on survey results

Qualitative Improvements

  • Unified history of each customer's inquiries
  • Transparent repair status for customers
  • Reduced stress for managers
  • Ability to analyze common issues
  • Foundation for further automation

Business Value

Bot investment paid off in 2.5 months through conversion growth and call center expansion savings. Monthly savings amount to ~$2,000 on operator payroll.

Current Usage

The bot processes 2500+ requests per month and is the main customer communication channel. 78% of customers prefer the bot over calling.

Scaling Opportunities

WhatsApp and VK integration planned, adding AI classifier for automatic fault type detection from photos.

Challenges & Learnings

Complex Routing Logic

Problem

Different service centers specialize in different device types, while customers don't always know exactly what's broken.

Solution

Implemented two-level system: bot first clarifies device category through visual menu with icons, then applies routing matrix considering center workload.

Learning

Visual selection works better than text — conversion at category selection stage increased by 25%.

Historical Data Migration

Problem

Old CRM had 50,000+ records with varying data quality, some without Telegram contacts.

Solution

Normalized data, created mechanism for linking customers without Telegram on first bot interaction.

Learning

Important to allocate time for data migration in such projects — it took an additional week.

Want the Same Results for Your Business?

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