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Lockheed Martin

Document Processing · Enterprise Knowledge Search

Lockheed Martin needed a secure way to scale generative AI across its U.S.-based workforce for code generation, data analysis, post-mission analytics, and question answering over production documentation without moving sensitive enterprise data off premises.

more than 8,000 engineers and developers using AI Factory

Reported impact

Business outcome

Lockheed Martin reports that the deployment increases quality and efficiency across the enterprise, accelerates movement from AI prototype to production, and enables secure, repeatable AI use in high-assurance business and customer-facing applications.

  • Productivity Improvement
  • Risk Reduction
  • Scalability
  • Speed to Market

Solution

Lockheed Martin deployed LMText Navigator as an internal generative AI tool for its U.S.-based workforce, using NVIDIA DGX SuperPOD as the AI infrastructure that powers model execution. The application sits inside Lockheed Martin’s broader AI Factory platform, which the company describes as a centralized environment for secure AI hosting across centralized, classified, and edge deployments. Within that platform, enterprise content and work artifacts such as production line documentation, post-mission analytics inputs, and software development tasks are processed on premises so employees can generate code, test software, analyze data, and retrieve answers securely. AI Factory also operationalizes the solution by scaling model-development automation and enforcing configuration management, model and dataset provenance, and repeatable ML pipelines. The 1LMX transformation team partnered on delivery to help embed the system into enterprise business processes.

Data flow

Enterprise inputs such as production line documentation, post-mission analytics work, and software development tasks are submitted by Lockheed Martin employees into LMText Navigator. The workloads run on NVIDIA DGX SuperPOD within the internal AI Factory environment, where data remains on premises and the resulting answers, analytics, and generated code are returned to the workforce for operational use.

Solution architecture

3 components · 3 layers
  1. Compute
    • NVIDIA DGX SuperPOD Provides the AI infrastructure that powers LMText Navigator.
  2. Serving
    • LMText Navigator Internal generative AI tool used by employees for code generation, data analysis, post-mission analytics, and answering questions from production documentation.
  3. Orchestration
    • AI Factory Hosts and operationalizes secure AI deployments, model-development automation, and repeatable ML pipelines across the enterprise.

Architecture clues

  • AI Factory supports centralized, classified and edge deployments.
  • Lockheed Martin uses a hub and spoke talent development model to spread AI capabilities across engineering teams.
  • The implementation emphasizes configuration management, model and dataset provenance, and repeatable ML pipelines.
  • The platform is maintained on premises for data security.

Evidence from the source

"A fully internal GenAI platform developed by Lockheed Martin accelerates our ability to propel AI-enabled solutions from prototype to production rapidly and at scale, while maintaining data security on premises."
"Ensuring trustworthy AI solutions for high-assurance applications through configuration management, model and dataset provenance, and repeatable ML pipelines"
"LMText Navigator is powered by NVIDIA DGX SuperPOD AI infrastructure"
"Lockheed Martin introduced its latest innovation in generative artificial intelligence (AI): Lockheed Martin Text Navigator (LMText Navigator), to its U.S.-based workforce."
"Providing a premier AI hosting platform for centralized, classified and edge deployments"
"Specific use cases for LMText Navigator include: Accelerating post-mission analytics, Generating and testing software code, Extracting answers from extensive production line documentation."
"With more than 8,000 engineers and developers using AI Factory, Lockheed Martin is pushing the frontiers of our AI solutions"
Transparency Evidence & methodology 75/100 reference value · 92% 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 value75/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence92%
  • Source authority25/25
    The deployment is described by the adopting organization or a government source.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    7 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. Lockheed Martin

    Lockheed Martin customer story

    First-party
    https://www.lockheedmartin.com/en-us/news/features/2024/empowering-innovation-with-secure-generative-ai-across-enterprise.html
    Source type
    Customer-published story
    Published
    Oct 8, 2024
    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 Oct 8, 2024 · Lockheed Martin · 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.