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Databricks Telecommunications United States

Lumen Technologies

Customer Service Automation · IT Operations Automation

Lumen needed to join diagnostic, performance, and ticketing data because siloed systems and outdated logic were slowing issue resolution and technician onboarding for enterprise customers.

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Reported impact

Business outcome

Lumen Technologies improved GenAI ticket summarization and diagnostics for field technicians and created measurable gains in productivity improvement, customer experience, scalability, and cost reduction through the new platform.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

Lumen Technologies uses Azure Databricks Data + AI Platform, Databricks Model Serving, Llama 3.3, and Agent Bricks AI Playground to support GenAI ticket summarization and diagnostics for field technicians. Lumen needed to join diagnostic, performance, and ticketing data because siloed systems and outdated logic were slowing issue resolution and technician onboarding for enterprise customers. In practice, the implementation governs access and lineage with Unity Catalog, coordinates repeatable workflows with Agent Bricks AI Playground, runs transformation or AI logic on Azure Databricks Data + AI Platform, and delivers outputs through Databricks Model Serving. Tickets, conversation history, knowledge articles, and operational context are consolidated into a governed cloud data layer; pipeline execution is coordinated with Agent Bricks AI Playground; Azure Databricks Data + AI Platform applies analytics or model logic; results are surfaced through Databricks Model Serving.

Data flow

Tickets, conversation history, knowledge articles, and operational context are consolidated into a governed cloud data layer; pipeline execution is coordinated with Agent Bricks AI Playground; Azure Databricks Data + AI Platform applies analytics or model logic; results are surfaced through Databricks Model Serving.

Solution architecture

5 components · 4 layers
  1. Compute
    • Azure Databricks Data + AI Platform Unifies engineering, analytics, and ai processing on one governed platform
    • Llama 3.3 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
  3. Orchestration
    • Agent Bricks AI Playground Coordinates agentic or multi-step conversational workflows for the use case
  4. Governance
    • Unity Catalog Enforces access controls, lineage, and governance across the data and models used in this workflow

Architecture clues

  • Centralized governance and lineage are enforced through Unity Catalog.
  • The platform is designed for near-real-time or streaming analysis rather than batch-only reporting.

Evidence from the source

Empowering technicians with instant answers and faster fixes
Hours saved annually across GenAI-assisted transactions
Lumen leverages Databricks to enhance enterprise support and optimize first-call resolution
Reworking legacy logic causing innovation barriers
Support calls resolved in Lumen’s automated service and network diagnostics tool
Transparency Evidence & methodology 70/100 reference value · 87% 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 value70/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • 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 specificity8/10
    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

    Lumen Technologies customer story

    First-party
    https://www.databricks.com/customers/lumen-technologies
    Source type
    Vendor-published customer story
    Published
    Jul 18, 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 Jul 18, 2025 · 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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