AI Support Automation

Support AI Agent:
tech support automation

An intelligent n8n-powered agent closes 70% of tickets without an operator — from the attachment detector to the final Gemini decision.

AgentACTIVE

Tickets today342

Auto-resolved239

otrs-ai-agent · live

$ otrs-agent --watch --queue=support

Initializing... connected to OTRS API

RAG index loaded: 512 rules · Qdrant Cloud ✓

[10:42:18] Ticket #48391 "Где мой заказ?"

→ tier-1 lang=RU | attach=false

→ pre-filter pass

→ rag match=0.94 "order-status-faq"

→ gemini action=AUTO_REPLY conf=98%

→ ✓ CLOSED in 27s · article created

[10:42:51] Ticket #48392 "System error 500"

→ tier-1 attach=true (screenshot.png)

→ ⚡ ESCALATED → agent queue

[10:43:04] Ticket #48393 "Password reset"

→ rag match=0.89 "auth-faq" (RAG Match)

→ ✓ CLOSED in 31s

ROI

×36

€1,800 saved
at €50/mo on AI

Before → after

30 sec

Instead of 2–4 hours
waiting for a reply

Auto-resolution

70%

Tickets closed
without an operator

NPS uplift

+26

From 42 → 68
in 2 months

The Task

Problem statement

Companies with a heavy inbound ticket flow — fintech, e-commerce, SaaS — receive tickets that are 70% repetitive questions. Operators handle them by hand.

OTRS is already in place as the ticketing system, but it works as a logging tool — not an automation engine. Average response time sits at 2–4 hours. The support headcount scales linearly with load, and so do costs.

"We deliberately build this solution on the low-code n8n stack — the client gets a transparent tool, not a black box. You edit the prompts yourself, extend the knowledge base with new rules, and change escalation logic without involving developers. The agent grows with your support team: today it closes FAQs, tomorrow it handles refunds and billing."
— Yevhen Katkov, CTO · Aibot.pro

Market Potential

Customer Service Automation Market

$47.8Bby 2030

CAGR 27.8% · from $8.7B in 2023

More than 60% of companies running support at 500+ tickets/month rank automation as a top priority for 2025–2026.

Tech stack

n8n Google Gemini Qdrant Nomic Embed OTRS API JavaScript

Target audience

Fintech and payment services

High volume of requests around blocks and transactions. Response speed and compliance are critical. Immediate escalation of critical cases plus full audit trail.

Compliance tagsAudit Trail

E-commerce · 300+ tickets/day

60–80% of inbounds are order status and payment questions. Auto-reply pays for itself in one month with no complex setup.

ROI · 1 mo

SaaS and technical services

Repetitive FAQs about features and access close through RAG search with no operator involvement.

RAG FAQ

Companies on OTRS · teams of 3+ operators

Drop-in integration with no infrastructure replacement. Launches without migration — the agent plugs into the existing OTRS.

No migration

What happens to every ticket

manage_search

TIER −1

Attachment detector

Detects language (EN/RU/UA) from the email body. Spots attachments → immediate escalation. No LLM call.

filter_alt

PRE-FILTER

Baseline cases

Thank-yous → closed. Security threats → escalated. Saves 30–40% of LLM calls.

storage

RAG + QDRANT

Knowledge base

Vector search across 500+ rules. Semantic match score 0.7–1.0. Only relevant data is passed to the agent.

smart_toy

AI AGENT

Google Gemini

Final decision: context analysis → structured output: action + reply + reason. Four outcome types.

Tickets without an operator

0 sec

Average response time

×0

ROI in the first month

Operators

4 → 2

same ticket volume

Savings/mo

€1,800

at €50 on AI

Tickets/day

300+

handled automatically

Operating costs

−45%

on tech support

The system in action

01 Dashboard

€1,800/mo saved · 40% auto-replies · 30 sec. A live ticket feed with AI confidence scores and an AI vs. Human handling chart.

02 Processing Flow

The 4-tier pipeline schematic: Tier-1 → Pre-Filter → RAG+Qdrant → AI Agent. Three outcomes: Auto-Reply, Escalate, Close.

03 Knowledge Base

Qdrant vector search across 500+ rules with relevance scoring. Add records, re-index, and monitor service status.

dashboard.ui — Support AI Agent

processing-flow.ui — Support AI Agent

knowledge-base.ui — Support AI Agent

Support AI Agent knowledge base management

Solution architecture

Language Detection · 95%+ accuracy

Smart multilingual handling

The agent detects language from the email body by analyzing unique alphabet characters. Technical headers are often in English while the customer writes in their native language. The system never gets confused and always replies in the correct language.

95%+

Accuracy

Languages

Misroutes

EN · RU · UK

charset analysis

Production-grade safeguards

Protection and compliance

01 Deduplication — every ticket is processed exactly once

02 A retry policy handles HTTP failures without dropping tickets

03 Graceful degradation — hands off to operators when AI is unavailable

04 Full audit trail with timestamps — ready for compliance and external audits

Implementation results

report_problem

Before implementation

groupOperators spend 70% of their time on repetitive questions
scheduleCustomer response time: 2–4 hours
support_agentSupport headcount: 4 operators for 300+ tickets/day
sentiment_dissatisfiedNPS: 42 — customers unhappy with response speed
money_offPayroll costs: €3,600/mo

verified

After implementation

smart_toy70% of tickets — fully automated, no human involvement
boltResponse time: 30 seconds
groupsHeadcount: 2 operators + bot — same volume
trending_upNPS: 68 (+26 in 2 months)
savingsSavings: €1,800/mo at €50/mo on AI

This solution fits if you:

requirements-check · running

✓Already run OTRS and handle 100+ tickets a day

✓Your operators burn time on repetitive questions every day

✓Want to reduce headcount or reassign people to complex work

✓First response time is critical — customers complain about the wait

✓You serve a multilingual audience (EN/RU/UA)

✓Your industry demands compliance — you need an audit trail of every decision

✓You want analytics: which questions come in most often and where the knowledge base has gaps

schedule Development and launch take from 3 weeks. The agent adapts to your knowledge base, OTRS queue structure, and business escalation rules.

Frequently asked questions

Q1 What does Support AI Agent do?

Support AI Agent is an intelligent agent built on n8n, Qdrant, and Google Gemini that plugs directly into an existing OTRS installation. It automatically processes incoming tickets: detects language, classifies the request, searches the knowledge base via RAG, and forms a resolution — auto-reply, escalation, or closure. 70% of tickets close without operator involvement, and the average response time is 30 seconds instead of 2–4 hours.

Q2 What percentage of tickets does the agent automate?

70% of incoming tickets close without operator involvement. The remaining 30% escalate to humans — complex cases, attachments that require manual review, and compliance situations. The agent hands them off with full context: language, category, RAG search results, and confidence score.

Q3 What ROI does deploying Support AI Agent deliver?

ROI ×36 in the first month at a cost of €50/month for AI infrastructure: €1,800/month saved on payroll. Headcount drops from 4 to 2 operators at the same 300+ tickets/day volume. Operating expenses on tech support fall by 45%.

Q4 What technology stack does the agent run on?

The core is the low-code n8n platform with 30+ workflow nodes. Vector search over the knowledge base runs on Qdrant Cloud with Nomic Embed. The final decision is generated by Google Gemini via structured output. OTRS integration is handled through the OTRS REST API. Around 2,000 lines of JavaScript logic handle edge cases and safety.

Q5 Which languages does Support AI Agent support?

The agent supports English, Russian, and Ukrainian with 95%+ language-detection accuracy. Detection runs on charset analysis of the email body — which matters because technical headers are often in English while the customer writes in their native language. The system does not get confused and always responds in the correct language.

Q6 How long does it take to deploy Support AI Agent?

Development and launch take from 3 weeks. The agent adapts to your existing knowledge base, OTRS queue structure, and business escalation rules. No infrastructure migration is required — the agent runs on top of your existing stack. Starting price is €1,500.

Q7 What is RAG in the context of Support AI Agent?

RAG (Retrieval-Augmented Generation) is a vector search across the knowledge base before the LLM generates an answer. In Support AI Agent, Qdrant stores 500+ rules and FAQ answers with embeddings from Nomic Embed. Before answering a ticket, the agent retrieves the top-N most relevant records with a score of 0.7–1.0 and passes them to Gemini as context. This guarantees answer accuracy and eliminates LLM hallucinations.

Q8 Does Support AI Agent include compliance and audit trail?

Yes. The agent keeps a full audit trail with timestamps for every action: language detection, pre-filter checks, RAG queries, the Gemini decision, and the final output. It supports deduplication (every ticket is processed exactly once), a retry policy for HTTP failures, and graceful degradation to human operators when AI is unavailable. This makes the system ready for fintech, e-commerce, and industries with compliance requirements.

Support AI Agent: tech support automation