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NASA

Enterprise Knowledge Search · Real-time Analytics · Risk Management

NASA had decades of scientific and operational data spread across open repositories, academic sources, engineering reviews, safety analyses, and mission systems, making it difficult for scientists, planners, and flight controllers to find, connect, and act on the right information quickly.

More than 500 employees signed up
The AI tools did that work in seconds – I mean, literally seconds
Traditionally, it would take experts weeks
a single mission that was only 10 days in duration

Reported impact

Business outcome

NASA reduced some mission-planning analysis from weeks to seconds, expanded non-expert access to complex Earth and hydrology data through natural-language interfaces, and is building AI-assisted decision support for flight operations and faster disaster-response analysis.

  • Productivity Improvement
  • Risk Reduction

Solution

NASA is using a set of AI assistants, developed partly with Microsoft, to turn scattered scientific and operational repositories into usable decision support. Inputs include academic literature, open NASA datasets, safety analyses, engineering reviews, Artemis II mission records, and satellite, observational, and modeling data drawn from at least 10 sources. Microsoft Copilot helps employees sift large volumes of literature and routine information. Earth Copilot and Hydrology Copilot add a conversational access layer over Earth science and water datasets, letting users ask natural-language questions while the system performs calculations and analyses on variables such as precipitation and soil moisture. NASA keeps its existing open-data repositories, mission systems, and public databases as the storage foundation, while AI handles retrieval, synthesis, and response delivery. A separate SPARTAN agent prototype is being designed as a virtual backroom teammate for Johnson Space Center flight controllers, returning real-time answers from mission information to support operational decisions.

Data flow

NASA draws safety analyses, engineering reviews, mission records, academic literature, and Earth science datasets from open repositories and internal mission systems. Microsoft Copilot, Earth Copilot, and Hydrology Copilot interpret those sources through natural-language prompts and automated analysis, then return synthesized answers to scientists, planners, communities, and agencies. The SPARTAN agent prototype is intended to deliver similar mission-information support directly into Mission Control workflows for real-time operational use.

Solution architecture

4 components · 1 layer
  1. Serving
    • Microsoft Copilot Used by NASA employees to sift through large volumes of academic literature and streamline daily tasks.
    • Earth Copilot Provides natural-language interaction with complex Earth science datasets so users can explore NASA data more easily.
    • Hydrology Copilot Lets users ask questions about water data and automatically runs calculations and analyses on variables such as precipitation and soil moisture.
    • SPARTAN agent prototype Acts as a virtual Mission Control support teammate for flight controllers by surfacing answers from mission information for real-time decisions.

Architecture clues

  • Hydrology Copilot automatically runs calculations and analyses over precipitation and soil-moisture variables.
  • NASA is exploring edge processing of satellite data so flood maps can be generated close to where the data is created or used.
  • NASA retains existing open-data repositories, mission systems, and public databases as the source-of-record data stores.
  • Natural-language prompting is the main access pattern for Earth Copilot and Hydrology Copilot.
  • The SPARTAN agent prototype is aimed at Johnson Space Center Mission Control support for ISS power and thermal-control operations.

Evidence from the source

"Employees use AI tools including Microsoft Copilot to sift through massive amounts of academic literature and streamline daily tasks"
"If you’re able to process the flood map on the edge and give you that information directly, that’s a matter of minutes."
"It automatically performs calculations and analyses that would otherwise require time-consuming manual work."
"NASA collaborated with Microsoft to develop Earth Copilot and Hydrology Copilot, AI-powered tools designed to help users interact with complex data through natural language."
"NASA is increasingly turning to AI to extract insights from decades of observations, simulations and mission data."
"The AI tools did that work in seconds – I mean, literally seconds,"
"teams across the organization generated streams of information – safety analyses, engineering reviews and mission data scattered across at least 10 separate sources."
Transparency Evidence & methodology 90/100 reference value · 94% 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 value90/100
  • 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 impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence94%
  • 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 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. NASA

    NASA customer story

    First-party
    https://news.microsoft.com/source/features/digital-transformation/at-nasa-ai-is-helping-scientists-unlock-discoveries-hidden-in-decades-of-data/
    Source type
    Customer-published story
    Published
    Sep 28, 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 Sep 28, 2026 · Microsoft · 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.