Al-tech support

Expertise:

Backend

Frontend

DevOps

Tech Stack:

PostgreSQL

MongoDB

Python

Node.js

Kubernetes

Docker

Client: Сloud provider

A major cloud provider offering data storage, computing power, and IT infrastructure services for businesses. The client faced high support loads, particularly during peak hours. The lack of prompt responses to customer inquiries led to decreased customer satisfaction and potential financial losses.

About the Client

Automating technical support with an AI assistant integrated into existing systems significantly reduced the workload on first-line operators, sped up request processing, and improved customer support availability. The project involved developing an intelligent AI-powered system capable of recognizing and resolving common user issues without human intervention.

Project Overview

Challenges & Objectives

30% of customer calls went unanswered during peak hours, leading to frustration and dissatisfaction.
High workload on first-line operators resulted in inefficiencies and burnout.
Long average response times (up to 10 minutes) negatively impacted customer experience.
Inefficient resource allocation within the support team hindered operational effectiveness.
The primary goal was to implement an AI assistant capable of handling common queries autonomously.
The AI system needed to ensure 24/7 support availability without increasing operational costs.
Seamless integration with the existing IT infrastructure was essential for smooth adoption.
Reducing response times and allowing human operators to focus on complex inquiries was a key objective

Implementation

01

Development and integration of the AI assistant into the support system.

02

Training the model on historical customer inquiry data, including analysis of common issues and resolution methods.

03

Configuring request routing algorithms between AI and human operators to ensure a seamless transition from automated responses to human intervention when needed.

04

Implementing machine learning to continuously improve model accuracy.

05

Optimizing system performance based on user feedback.

Outcomes & Business Impact

60% of common queries are resolved without human involvement, reducing operator workload.
24/7 customer support availability, improving customer satisfaction.
Average response time decreased from 10 minutes to 2 minutes, significantly enhancing user experience.
Reduced operator workload allows them to focus on complex cases requiring expert intervention.
Increased support team efficiency led to lower operational costs and higher customer satisfaction.

By implementing the AI assistant, the client significantly improved the efficiency of their support service, optimized workflows, and ensured high-quality service without increasing personnel costs.

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