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75th U.S. Army Reserve Innovation Command

The 75th U.S. Army Reserve Innovation Command wanted to modernize administrative and planning work and give military police teams better ways to collect and analyze checkpoint footage and other surveillance, while proving that technical Soldiers could deliver useful operational AI capabilities faster than traditional acquisition processes.

15 highly motivated individuals
May 3-16, 2026
eight months of intensive planning and coordination
five-day development sprint by just four of our AAG Soldiers

Reported impact

Business outcome

The command produced several working AI prototypes, demonstrated that Soldiers could build and operationalize advanced AI models quickly, and showed leaders how AI tools could improve efficiency, accuracy, readiness, and surveillance analysis in Army operations.

  • Productivity Improvement
  • Risk Reduction
  • Speed to Market

Solution

The 75th U.S. Army Reserve Innovation Command, through its Army Applications Group, ran a two-week code-a-thon to build in-house AI applications inside a secure, open-internet development environment with access to commercial-grade AI models. The team developed and rehearsed several prototypes tied to concrete Army workflows: MP Sentinel-Video RAG, described as a novel multimodal Video RAG engine for military police tactical operations; a TA-50 computer vision application to reduce the inventory burden; and a Soldier Passport system to streamline administrative readiness. The implementation paired software engineers, product managers, data scientists, and military domain experts in an agile sprint, then moved the prototypes from development into operational demonstration during Operation Sentinel Justice 26, where senior leaders reviewed how the tools could support security analysis, readiness, and mission execution.

Data flow

For MP Sentinel-Video RAG, checkpoint footage and other critical site surveillance are taken from operational sources, processed by the multimodal Video RAG prototype for retrieval and AI-assisted analysis, and then surfaced to military police security teams and senior leaders as operational insight during OSJ 26 demonstrations. Parallel prototypes feed inventory and readiness workflows for TA-50 and Soldier Passport use cases.

Solution architecture

3 components · 1 layer
  1. Uncategorized
    • MP Sentinel-Video RAG AAG's multimodal retrieval-augmented generation prototype analyzes checkpoint footage and other surveillance to support military police tactical operations.
    • TA-50 computer vision application This prototype applies computer vision to TA-50 inventory workflows to reduce inventory burden and system overload.
    • Soldier Passport system This system streamlines administrative readiness tasks for Soldiers and unit operations.

Architecture clues

  • Built as in-house technological solutions by the Army Applications Group.
  • Development used a secure, open-internet development environment and infrastructure.
  • MP Sentinel-Video RAG is described as a novel multimodal Video RAG engine.
  • Operational demo took place during Operation Sentinel Justice 26 at Camp Shelby, Mississippi.
  • Teams were given direct access to best-in-class, commercial-grade AI models.

Evidence from the source

"Army Reserve Soldiers leveraged cutting-edge technology to modernize administrative and planning tasks, demonstrating new artificial intelligence applications during a collaborative code-a-thon"
"Coordinate and establish a secure, open-internet development environment and infrastructure that grants AAG teams direct access to best-in-class, commercial-grade AI models."
"During the event, Soldiers produced several advanced prototypes, including a Video RAG tool for MP tactical operations, known as MP Sentinel-Video RAG, a TA-50 computer vision application designed to eliminate the inventory tax or system overload and a 'Soldier Passport' system built to streamline administrative readiness."
"Soldiers experimented with practical AI applications to streamline daily administrative and operational tasks, ultimately enhancing efficiency, accuracy and overall mission readiness."
"building and operationalizing advanced AI models from scratch, like a novel multimodal Video RAG [Retrieval-Augmented Generation] engine"
"the AAG-developed Video RAG capability, which aims to modernize how security teams collect and analyze checkpoint footage and other critical site surveillance."
Transparency Evidence & methodology 70/100 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 value70/100
  • 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 scale5/10
    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
    6 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. 75th U.S. Army Reserve Innovation Command

    75th U.S. Army Reserve Innovation Command customer story

    First-party
    https://www.army.mil/article/293712/75th_usaric_pioneers_ai_solutions_for_osj_26
    Source type
    Customer-published story
    Published
    Jul 3, 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 Jul 3, 2026 · The United States Army · 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.