AI for Customer Service

In Türkiye between 2018-2020, chatbot was just a 'rule-based menu tree' acting as a 'call router'; the customer hit 'speak to agent' after 5 menus. GPT-3.5-powered generative chatbot broke this paradigm in 2022 — customer asks in free language, bot finds the right answer in the knowledge base. By 2026 the maturity level is: RAG-based assistant, voice-based natural conversation, and human-in-the-loop handoff for complex cases.

Key lesson: AI chatbot doesn't 'replace' the human agent, it 'augments' them. The best architecture is hybrid: bot handles easy cases, supports the agent in 'assistant' mode (MH-04 Agent Assist), escalates complex or empathy-needing cases to humans. Typical impact in this hybrid: call load down 40-65%; average resolution time down 30-50%; first-contact resolution from 60% to 85%+.

Whatever the chatbot knows, the assistant can help with. So 70% of customer service AI is 'knowledge base design', not 'model selection'. RAG (Retrieval-Augmented Generation) architecture — embedding-based semantic search + LLM answer generation — makes the knowledge base a live, version-controlled, auditable asset. Without this infrastructure, the chatbot either gives wrong answers (hallucination) or says 'I don't know'.

Additional challenge in the Türkiye context: Turkish NLP models run 10-15 points below English performance. Solution: BERTurk or similar Turkish fine-tuned embedding models; two-layer retrieval (semantic + keyword); and continuous evaluation set built from real user queries. With these three practices, Turkish chatbot accuracy reaches 85-95% — global benchmark level.

The high-compliance-risk area of customer service AI: call recording transcription and sentiment analysis. Under KVKK Articles 5 and 11, every call needs explicit customer consent, retention period (typically 6-12 months), and clear deletion policy. Sentiment results cannot be used as performance penalty against an individual agent — protected by both KVKK and labor law.

Practical design: auto-notice at call start ('This call may be recorded for training and quality'), encrypted storage, role-based access, and monthly audit. Sentiment analysis is aggregated at team level; individual use is for coaching, not penalty. With this framework, risk is managed in both KVKK audit and call center union dialogue.