NVIDIA Software / SaaS Vietnam

FPT Smart Cloud

Customer Service Automation · Sales Automation

FPT Smart Cloud needed more natural, multilingual virtual assistants and more scalable employee upskilling systems, but high speech-synthesis training costs, long processing times, and the need for personalized knowledge delivery limited how far those AI services could scale.

100X improvement in speech synthesis model output in dimension and aptitude across 18 languages
16 million end users served
30X faster LLM training than the previous generation
3X reduction in data processing for FPT AI Engage

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.

Solution architecture

12 components · 4 layers
  1. Compute
    • NVIDIA H100 Tensor Core GPUs Accelerate advanced speech-synthesis and LLM training for Engage and Mentor.
    • NVIDIA A100 Tensor Core GPUs Supported the earlier generation of speech and training workloads before the H100 upgrade.
    • NVIDIA DGX H100 Hosts the LLM infrastructure behind AI Mentor’s content generation workflows.
    • PyTorch Supports training of Mentor’s large language models on DGX H100.
  2. Serving
    • FPT AI Engage Provides virtual assistants for call-center automation, IVR, outbound calls, and customer support.
    • FPT AI Mentor Delivers personalized learning content, quizzes, and knowledge graphs for workforce development.
    • NVIDIA TensorRT Optimizes inference for virtual-assistant workloads.
    • NVIDIA Triton Inference Server Serves models with dynamic batching to lower compute usage at production scale.
  3. Orchestration
    • NVIDIA GPU Operator Helps FPT deploy and manage GPU resources at cloud scale.
    • Multi-Instance GPU (MIG) Enables efficient multi-tenant GPU partitioning for large-scale virtual-assistant deployment.
    • Kubernetes Orchestrates the cloud-native deployment of FPT’s AI assistants.
  4. Governance
    • NVIDIA NGC Supplies container and software support for FPT’s model development workflows.

Architecture clues

  • AI Mentor uses NVIDIA DGX H100, NVIDIA NGC, PyTorch, and NVIDIA-powered RAG workflows to generate personalized training content and knowledge graphs.
  • FPT uses NVIDIA GPU Operator and MIG support in Kubernetes to scale virtual assistants.
  • Inference is optimized with NVIDIA TensorRT and served through NVIDIA Triton Inference Server with dynamic batching.

Evidence from the source

FPT Smart Cloud, an NVIDIA Cloud Partner and leading AI provider in Vietnam, is using FPT AI Engage to accelerate their speech synthesis models by 4X.
The H100 can speed up LLM training by 30X over the previous generation.
The deployment is optimized using NVIDIA® TensorRT™ and served in NVIDIA Triton™ Inference Server with dynamic batching, saving up to 20 percent of high-performance computing resources for the same quality of model output.
This advanced approach has improved knowledge quality for the workforce by 55 percent while reducing required training resources by 80 percent.
Trusted by more than 100 enterprises in 15 countries, FPT AI Engage has enabled, on average, a 50 percent increase in productivity and a 67 percent reduction in operating expenses.
Transparency Evidence & methodology 100/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    8 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. NVIDIA

    FPT Smart Cloud customer story

    First-party
    https://www.nvidia.com/en-us/case-studies/fpt-smart-cloud-levels-up-customer-service-operations/
    Source type
    Vendor-published customer story
    Published
    Jan 17, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Jan 17, 2025 · NVIDIA · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.