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Business automation → Support automation

Support automation

AI support automation: request classification and routing, conversation summaries, agent suggestions and SLA control. Implemented in Kazakhstan in Russian and Kazakh.

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Overview

Support automation: what the service covers

Gotomarket automates customer support with AI in Kazakhstan. The system classifies and routes requests, prepares conversation summaries and agent suggestions, and helps you keep SLAs under control. The result: a lighter load on first-line support, faster replies and transparent quality in Russian and Kazakh.

Launch
2–4 weeks
Languages
Russian and Kazakh
Control
SLA and quality
Channels
website, WhatsApp and email
In plain words

What it is and why it matters

What it is
AI helps your support team: it sorts customer requests by topic, routes them to the right people, summarizes conversations and drafts replies for agents.
Who it is for
IT products and services with a large flow of similar requests, where support agents are buried in manual sorting and miss agreed response times.
What you get
A map of request types and causes, rules for sorting and handing complex cases to a person, a connected answer base, agent suggestions, response-time tracking and trial-run metrics.
Where to start
Tell us which type of request comes in most often and how long it takes to handle. We will suggest starting the trial run there.

Solution architecture

How we automate support using the 5D methodology

We start with the most frequent request types and expand automation only after quality is verified and handoff to an agent works safely.

01

Discover

Analyze the flow

We measure request types, handling time, repeat requests and first-line bottlenecks.

02

Design

Set the rules

We design classification, routing, SLAs and the conditions for handing off to an agent.

03

Deploy

Connect the systems

We connect channels, the knowledge base and the helpdesk, and set up suggestions and summaries.

04

Drive

Launch under control

We switch on automation for part of the flow and monitor speed and quality.

05

Dominate

Expand coverage

We add standard replies and scenarios based on statistics and feedback.

Unit economics and ROI

How automation pays off

The economics rest on reducing manual workload and repeat requests. In a hypothetical example with 2,000 requests a month and 6 minutes of handling each, first-line support spends about 200 hours a month.

If AI cuts manual workload by 30–50%, that frees up 60–100 hours. The actual effect depends on the share of routine requests, the quality of the knowledge base and the current cost of handling.

Example flow
2,000 requests per month
Load reduction
30–50%
Time freed up
60–100 hours per month
First response
faster

Result

What you get

The pilot covers one measurable area of support and leaves you control rules for further expansion.

  • Map of request types and causes
  • Classification and routing rules
  • Scenarios for handing complex cases to an agent
  • Knowledge base and channel integration
  • Conversation summaries and agent suggestions
  • SLA and KPI setup
  • Answer quality test set
  • Pilot metrics and scaling plan

Comparison of approaches

Auto-reply or agent assistance

We choose the level of automation based on the risk of error and how repetitive the requests are.

ApproachRole of AILimitationWhen it fits
RoutingIdentifies the topic and queueDoesn't resolve the customer's issueMultiple support tiers
Agent suggestionDrafts the reply and summaryA person stays in every conversationComplex or sensitive requests
Auto-replyResolves a routine requestRequires a solid knowledge base and escalationHigh volume of repetitive requests

Limitations and risks

When automation is not needed

Automation pays off when there is a steady flow of repetitive requests and clear rules for answering them.

Low volume

With a few dozen requests a month, manual support can be cheaper and more flexible.

Every case is unique

If there are almost no routine scenarios, classification and auto-replies will have little effect.

No knowledge base

Without verified answers, you first need to collect and maintain the sources.

A good fit

When this service is a good fit

  • You have a large flow of repetitive requests
  • First-line support is overloaded with manual sorting
  • You need to keep response speed and SLAs under control
  • Support runs in Russian and Kazakh

Not a fit

When to choose another approach

  • Request volume is low
  • Most cases are unique and sensitive
  • No verified knowledge base
  • Helpdesk and routing are not configured yet

Terms in plain words

SLA
the agreed time for a first response or resolution
Routing
sending a request to the right queue or specialist
Escalation
handing a complex case over to a person
Conversation summary
a short description of what the request is about and what has already been done
First-line support
the staff who receive requests first
Helpdesk
The system where support receives, assigns and closes customer requests; automation is usually built into it rather than replacing it.
Agent suggestion
A draft reply and short summary that AI prepares for a support agent; the decision and sending stay with the person.
Knowledge base
A verified set of answers and instructions that AI relies on; without it, automatic replies and suggestions will be inaccurate.

Frequently asked questions

Support automation: FAQ

Will AI reply instead of agents?

Only in agreed routine scenarios. Complex and sensitive cases are handed to an agent with full context.

How is quality controlled?

Through a test set of questions, SLA rules, a reply log, escalation and analytics on request causes.

Does the system work in Kazakh?

Yes. We check the quality of the Kazakh version with native speakers and on real requests.

Where should support automation start?

With one frequent request type that has a clear handling cost, not with all of support at once.

How much does AI support automation cost?

The cost depends on the volume and structure of the ticket flow, the number of channels, the state of the knowledge base and the integrations with the helpdesk and CRM. We start with a pilot on the most frequent request type with a clear handling cost. We provide an estimate after analyzing the flow and current first-line metrics.

Do we need to change our helpdesk to automate support?

Usually not. We connect to your current helpdesk, CRM and channels via API and build classification, summaries and suggestions into the interface agents already use. If the system cannot support the required scenario, we discuss options before the start rather than during implementation.

How can support pass bugs to development through Jira or Plane?

For IT products, AI detects that a request looks like a bug, prepares a summary with reproduction steps and customer data, and creates a task in Jira or Plane. The task status flows back to the ticket so the agent can tell the customer about the fix. Rules for creating tasks and merging similar requests are agreed in advance.

How does support automation connect to Twenty or amoCRM?

Tickets are linked to the customer record in the CRM, and conversation summaries are saved to its history. Account managers see customer issues before a renewal or upsell, and support sees deal terms and status. Sync is set up only for the agreed fields.

Which metrics show the impact of support automation?

First response and resolution time, SLA compliance, the share of tickets closed without an agent, the escalation rate, repeat contacts and answer quality scores. We record baseline values before launch to compare the pilot fairly with the original process. The decision to expand is based on this data.

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