Goal:
Enhance customer experience by replacing generic bots with domain-tuned, context-aware AI assistants that understand telecom-specific language and workflows.
Impact Keywords:
- LLM Fine-Tuning
- Customer Experience
- Multilingual AI
- Knowledge Automation
Approach:
LunarTech Lab created a secure, telecom-specialized conversational AI layer built atop a fine-tuned large language model.
- Domain Data Training:
- Curated anonymized support tickets, FAQs, and service scripts to fine-tune model weights for telecom context.
- Retrieval-Augmented Generation (RAG):
- Connected real-time product, outage, and plan databases for factual, up-to-date answers.
- Multichannel Integration:
- Deployed across WhatsApp, Telegram, web chat, and internal support dashboards for unified interactions.
- Agent Co-Pilot Mode:
- Provided internal support agents with AI-generated suggestions, next-best actions, and customer sentiment insights.
- Continuous Learning:
- Added feedback pipelines so internal teams could retrain and improve the AI over time.
Summary:
By combining generative AI, retrieval systems, and domain fine-tuning, LunarTech Lab helped telecom operators deliver faster, more accurate, and multilingual support — while empowering internal teams to own and evolve the models.
