"How is a support agent different from a regular chatbot?"

"A chatbot answers by script and hits its limit on the second non-standard question. An agent classifies the request, finds the answer in your knowledge base, reaches into the CRM when needed and escalates to a human by an explicit rule. It closes the request rather than just replying."

"Where does the agent get answers?"

"From your corporate knowledge base via retrieval (RAG): normalized documents, policies, ticket history. The answer is grounded in the retrieved fragment, not the model's memory — which reduces hallucinations and lets it cite the source."

"What happens with hard cases?"

"The agent is designed with an explicit human-handoff point: low confidence, a money matter above threshold, an emotional customer or two misunderstandings in a row trigger a soft escalation to an operator with a short dialog summary."

"Which channels does it work in?"

"Telegram, WhatsApp, a site widget, email — the same agent with different input channels. The logic and knowledge base are shared; only the channel changes."

Support & comms

First-line AI Support Agent

AI support agent: classifies tickets, answers from the corporate knowledge base, opens tickets and escalates to a human by SLA. Removes first-line routine without losing hard cases.

Corporate Chatbots & Operational AI Agents

First-line support is the most common automation candidate: up to 80% of requests are routine and repeat. The support agent takes that flow — it classifies the request, answers from the corporate knowledge base and opens a ticket, while routing hard and risky cases to a human by an explicit rule.

What it does

It receives a request in any channel, detects type and urgency, finds the answer in your knowledge base and grounds it in a source. If the request needs an action, it pulls data from the CRM, files a request, checks status. If confidence is low or the cost of error is high, it escalates to an operator with ready context, so the human doesn’t start from scratch.

Where the line is

The agent should not “push through” where the outcome is unpredictable. We design the handoff scenarios up front — that’s the agent’s contract, not a weakness. Good AI first-line support isn’t “a bot instead of people” but the removal of repetitive routine, so operators handle what genuinely needs a human.

The knowledge retrieval inside the agent is a full RAG system, and the agent itself is a case of an operational AI agent. It’s assembled for your process from ready blocks with cost control.

How the chain works

01
Ticket classification · light model
Detects request type, language and urgency. A cheap step on every message — filters spam and routes.
02
Knowledge-base answer · RAG + mid model
Retrieves relevant fragments from the corporate KB, composes an answer grounded in the source, not the model's memory.
03
Escalation decision · rule + model
On low confidence, money matters or an upset customer — hands off to a human with ready context.

Integrations

OpenAI

YandexGPT

+ any external API

Cost calculator

Requests per day 200

Sub-models in the chain 3

Model

Tokens, ₽/mo

—

Development, ₽

—

Support, ₽/mo

—

Estimate at a blended per-token rate (input+output). Exact cost depends on context length, number of calls and the share of manual review — we scope it to your process.

related cases

AI Systems

AI Customer Interaction & Recommendation Assistant

Omnichannel

First-line AI Support Agent

What it does

Where the line is

AI Customer Interaction & Recommendation Assistant

AI Communication Monitoring System

Straight answers

Let's design an AI-native automation layer for your operations.