What challenges did KNST CO., LTD. face before AI implementation?

Frequent technical disruption from on-prem infrastructure Need to improve customer service efficiency for a high-volume call center Lacked a centralized data warehouse to speed analytics Needed faster handling of customer questions across diverse service topics

What was the impact of the AI solution?

Analytics requests that previously took up to a week can now be served immediately from the centralized warehouse Active users increased by nearly 20% Customer call handling time dropped from 15 minutes to seven minutes No major downtime since migration to Google Cloud

Source

GCP

SmartDaily (KNST) builds ML-powered customer service agent insights with Vertex AI, BigQuery, and Contact Center AI on Google Cloud

Use case typeCustomer service automationUpdated Jun 13, 2026

SmartDaily, a smart community services platform from KNST CO., LTD. in Taiwan, moved its platform to Google Cloud to improve stability, data security, and analytics performance. The company built a centralized BigQuery data warehouse aggregating Cloud SQL and Google Analytics data, then used BigQuery ML with FAQ tagging from Vertex AI and industry knowledge summarized by Gemini 1.5 Pro to help customer service agents answer questions faster. SmartDaily also plans to extend the solution with Contact Center AI, Dialogflow, Text-to-Speech AI, and Speech-to-Text AI for virtual customer service agents.

Organization: KNST CO., LTD.
Industry: Other
Location: Taiwan
Published: January 2024

Reported outcomes

+20%

active users increaseAdoption & scale

8 minutescall handling time reduction7 daysanalytics request turnaround

Strategic outcomes

Speed & agilityFaster customer service responsesBetter decisions & insightCentralized analytics warehouseNew product / capabilityPlanned virtual customer service agentsRisk & complianceImproved platform stability and security

Catalog median for adoption & scale deployments: +85.5% across 46 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 3

1Customer Service Automation
2Predictive Analytics
3Data Warehouse Modernization

Frequent technical disruption from on-prem infrastructure
Need to improve customer service efficiency for a high-volume call center
Lacked a centralized data warehouse to speed analytics
Needed faster handling of customer questions across diverse service topics

Migrated the smart community service platform from on-prem to Google Cloud
Built a centralized BigQuery warehouse aggregating Cloud SQL and Google Analytics data
Trained a BigQuery ML model using frequently asked questions tagged by Vertex AI and industry knowledge summarized by Gemini 1.5 Pro
Used predicted questions and answers to help customer service agents respond more quickly
Planned virtual customer service agents using Contact Center AI, Dialogflow, Text-to-Speech AI, and Speech-to-Text AI

Technologies

BigQuery BigQuery ML Vertex AI Gemini 1.5 Pro Contact Center AI Dialogflow Text-to-Speech AI Speech-to-Text AI Cloud SQL Google Kubernetes Engine Compute Engine Looker Studio Google Analytics

Analytics requests that previously took up to a week can now be served immediately from the centralized warehouse
Active users increased by nearly 20%
Customer call handling time dropped from 15 minutes to seven minutes
No major downtime since migration to Google Cloud

Architecture

SmartDaily migrated its smart community services platform from on-premises infrastructure to Google Cloud. It centralized user behavior and device data in BigQuery by aggregating Cloud SQL and Google Analytics sources, then applied BigQuery ML with FAQ tagging from Vertex AI and knowledge summarization from Gemini 1.5 Pro to generate predicted Q&A for customer service agents. The company also uses GKE and Cloud SQL for platform operations and plans to add Contact Center AI, Dialogflow, Text-to-Speech AI, and Speech-to-Text AI for virtual call-center agents.

Implementation partners1

CloudMile

Sources & evidence1

Groundedness: 5/5Type: Customer StoryPublished: Jan 1, 2024Publisher: Google CloudEvidence: PrimaryConfidence: High

Primary source

AI-generated summary. Verify important details with the linked sources before relying on this case.

Explore related AI use cases

Customer evidence