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Databricks Telecommunications South Korea

LG U+

Customer Service Automation · Enterprise Knowledge Search

LG U+ needed to reduce its dependency on external LLM providers because latency, outages, and operating costs were directly affecting AI search and voice experiences for telecom customers.

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48% increase in throughput
90% lower failure rate
Average response time dropped from 18 seconds to 14
LG U+ Reduced LLM Vendor Dependency and Cut Costs 36% with Databricks Model Serving

Reported impact

Business outcome

LG U+ reported LG U+ Reduced LLM Vendor Dependency and Cut Costs 36% with Databricks Model Serving, 48% increase in throughput, and Transactions per second improved after adopting Databricks 22% faster responses, improving AI search and voice services with resilient model serving with measurable gains in cost reduction, customer experience, and scalability.

  • Cost Reduction
  • Customer Experience
  • Scalability

Solution

LG U+ uses Databricks Model Serving, AI Gateway, Genie, and Gemini 2.5 Flash to support AI search and voice services with resilient model serving. LG U+ needed to reduce its dependency on external LLM providers because latency, outages, and operating costs were directly affecting AI search and voice experiences for telecom customers. In practice, the implementation governs access and lineage with AI Gateway, runs transformation or AI logic on Gemini 2.5 Flash, and delivers outputs through Databricks Model Serving. Tickets, conversation history, knowledge articles, and operational context are consolidated into a governed cloud data layer; Gemini 2.5 Flash applies analytics or model logic; results are surfaced through Databricks Model Serving. The case study reports LG U+ Reduced LLM Vendor Dependency and Cut Costs 36% with Databricks Model Serving, 48% increase in throughput, and Transactions per second improved after adopting Databricks 22% faster responses, showing measurable...

Data flow

Tickets, conversation history, knowledge articles, and operational context are consolidated into a governed cloud data layer; Gemini 2.5 Flash applies analytics or model logic; results are surfaced through Databricks Model Serving.

Solution architecture

4 components · 3 layers
  1. Compute
    • Gemini 2.5 Flash Provides the language or embedding model used for reasoning, retrieval, or response generation
  2. Serving
    • Databricks Model Serving Hosts production inference endpoints that return summaries, predictions, or recommendations
    • Genie Gives business users natural-language access to curated analytics and metrics
  3. Governance
    • AI Gateway Routes and monitors model traffic so the team can manage cost, latency, and reliability

Architecture clues

  • Average response time dropped from 18 seconds to 14

Evidence from the source

"By adopting Databricks for LLM serving, we reduced our vendor dependence and actually improved performance at the same time," Cho said.
"We realized we needed a more fundamental fix, something that would structurally free us from depending on any single vendor," said Cho Kyung-hyun, Principal Manager of the ixi Agent Media Business Enablement Team.
48% increase in throughput
LG U+ Reduced LLM Vendor Dependency and Cut Costs 36% with Databricks Model Serving
Transactions per second improved after adopting Databricks 22% faster responses
Transparency Evidence & methodology 95/100 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 value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    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
    6 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. Databricks

    LG U+ customer story

    First-party
    https://www.databricks.com/customers/lg-u
    Source type
    Vendor-published customer story
    Published
    Aug 6, 2026
    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 Aug 6, 2026 · Databricks · 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.

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