Reported impact
Business outcome
Across Engage and Mentor, FPT reports 3x lower data processing, 4x faster speech-synthesis processing, 100x richer speech-model output, 55 percent better agent knowledge quality, 80 percent lower training-resource requirements, 30x faster LLM training, and production-scale customer-service deployments that lifted productivity by 50 percent while cutting operating expense by 67 percent.
- Cost Reduction
- Customer Experience
- Productivity Improvement
- Revenue Growth
Solution
FPT Smart Cloud built two NVIDIA-accelerated application stacks: FPT AI Engage for customer-service virtual assistants and FPT AI Mentor for workforce development. FPT AI Engage combines LLMs, NLP, ASR, and TTS with NVIDIA software pipelines to automate inbound and outbound calling, IVR, telesales, reminders, and FAQ resolution. The deployment uses NVIDIA GPU Operator and MIG support in Kubernetes for multi-tenant scale, then optimizes inference with NVIDIA TensorRT and NVIDIA Triton Inference Server using dynamic batching. For model training, FPT moved speech-synthesis and LLM workloads from A100-based systems to NVIDIA H100 and DGX H100 nodes, alongside NVIDIA NGC, PyTorch, and NVIDIA-powered RAG workflows. That stack lets FPT build more human-like voices in 18 languages, personalize microlearning through AI Mentor, generate situational Q&A from enterprise knowledge bases, and deliver both call-center automation and staff training from a common accelerated AI foundation.
Data flow
Customer calls and enterprise knowledge enter FPT AI Engage and Mentor, speech, language, and retrieval models run on Kubernetes-managed GPU infrastructure, TensorRT and Triton optimize the serving path, and the systems return virtual-agent responses, quizzes, summaries, or recommendations back into customer-service and workforce workflows.