Scenario / 01
Typical question
The system finds a confirmed answer, shows it to the customer, and suggests the next step.
1Question2Classify3Knowledge4Answer
Customer service Support teams with recurring questions, multiple channels, and queues
Typical questions close faster; hard cases reach specialists
We remove repetitive work while keeping humans where context, ownership, or non-standard judgment is required.
01Ticket
02Category
03Answer
04Agent
OutputResolution or escalation
The agent receives history, category, and matched materials.
Practical outcome
A request is classified, answered from the knowledge base, or sent to a specialist with full context
Problem
Tickets are routed by hand, stock answers repeat, and context is lost on handoff
Primary route
customer question → knowledge search → answer → escalate to agent
Engagement trigger
Where support loses speed
First-response time grows while agents spend effort on classification instead of hard cases
- 01Agents answer the same questions
- 02Tickets bounce between teams
- 03Customers re-explain the problem
- 04There is no clear view of request causes
Working scenarios
From question to answer or agent
Demonstration routes, not client results. Exact logic depends on your rules, data, and systems.
Scenario / 02
Complex ticket
ID, description, and history are collected, then the task reaches the right specialist without re-asking.
1Dialog2Context3Route4Agent
Scenario / 03
Feedback loop
After resolution the system checks outcome and links a poor rating to the original ticket.
1Resolve2Check3Score4Review
A good fit for
Which tickets we take off the queue
Support teams with recurring questions, multiple channels, and queues
First line
Answers to typical questions, data capture, and knowledge checks before an agent joins.
Routing
Topic, urgency, and customer classification; queue selection; full context handoff.
Quality control
Analysis of request causes, missed steps, and repeats — without invented scores.
How we launch
From a real process to a working loop
Tools are chosen after we verify inputs, exceptions, and the success criterion.
- 01
We analyze the stream
Real topics, channels, repeat causes, and delay points.
- 02
We prepare knowledge
Answer owners, freshness windows, and usage rules.
- 03
We design routes
Categories, priorities, queues, and mandatory human handoff rules.
- 04
We connect channels
Chat, Telegram, email, or work UI into one handling loop.
- 05
We set control
Classification errors, repeats, and unhandled exceptions.
Solution scope
Routing and quality control
- Topic and route map
- Knowledge base and access rules
- First-line automation
- Context handoff to agents
- Reporting and logging
Data and systems in the loopTelegramEmailLLM APIREST APIKnowledge baseTicketing systems
Fit / boundaries
What must not go unchecked
Risky or ambiguous cases are not auto-closed without review
01We do not automate answers where error risk is high without human review
02Knowledge needs owners and regular updates
03Routing quality must be measured on real tickets
Will automation replace agents?+
The goal is to remove repetitive work and gather context. Complex, conflict, and high-stakes cases stay with people.
Can we use an existing knowledge base?+
Yes after checking structure, freshness, duplicates, and access. Sometimes materials need cleanup first.
Which channels can be unified?+
Telegram, site chat, email, and others when a safe exchange interface exists.
How does handoff to a human work?+
It creates a ticket with topic, priority, short summary, history, and already collected fields.
How do we know automation helps?+
Compare repeats, routing time, correctly closed typical questions, and escalation causes.
First step
Map the support flow
We'll map inputs, exceptions, and constraints. You leave with a priority scenario and a next step — no obligation to start a project.
Map the support flowService: Support