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LSUBP00004 Information Warfare Research Project 3 (IWRP-3), Project Number 26-LANT-2974 MBSE with AI Plug-In Prototype (C)

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Federal

Contract Overview

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The Department of the Navy, specifically the NIWC Expeditionary C3 Solutions Division, is seeking information for a prototype project to integrate Generative AI and GPT technology into Cameo Systems Modeler workflows. The objective is to implement a robust plugin capable of automating requirements analysis, model element creation, and documentation generation while supporting both SysML and Unified Architecture Framework (UAF) modeling paradigms. A primary focus is placed on the ability to fine-tune large language models with domain-specific knowledge, such as DoD architectures and MIL-STD requirements, to ensure high accuracy and data integrity within a defense context. Qualified vendors must demonstrate a proven track record of deploying AI-driven plugins that integrate seamlessly with existing MBSE toolchains and data architectures. Key requirements include the ability to manage various UAF viewpoints, ensure compatibility across Cameo versions, and provide comprehensive training and maintenance strategies. Additionally, the selected partner must adhere to strict government data security regulations and compliance standards to optimize the system development lifecycle, reduce costs, and improve the overall reliability of systems developed in Charleston, South Carolina.

General Info

Navy seeks AI plugin for Cameo to automate MBSE workflows and requirements analysis.

NAICS

TBD - TBD

Place of Performance

Charleston, SC, USA

Set-Aside

NONE

Documents

0

No documents available

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Timeline

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Organization & Contact Information

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AgencyDepartment Of The Navy → LSUBP00004 - LSUBP00004 - Expeditionary C3 Solutions Division Head - Niwclant
ContactsNo contacts available
OfficeN/A
Office AddressN/A
ContactsNo contact information available

Full Description

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NIWC is exploring the potential of integrating Generative AI, specifically GPT technology, into our Cameo Systems Modeler (SysML) workflows to optimize efficiency, improve data integrity, and enhance collaboration across project teams. We are seeking information from qualified vendors capable of providing a robust and integrated Cameo SysML plugin that leverages GPT for tasks such as requirements analysis and generation, automated model element creation and validation (including the generation of UAF viewpoints and SysML diagrams), and streamlined documentation generation directly within the Cameo environment. We aim to identify partners who can demonstrate a proven track record of success in developing and deploying AI-driven Cameo SysML plugins that specifically support both SysML and UAF modeling, ideally within the defense or government contracting sector. This Request for Information (RFI) is intended to gather insights into potential Cameo SysML plugin solutions that can seamlessly integrate with our existing MBSE toolchain and data architecture, with a particular focus on generating and managing UAF models. We are particularly interested in understanding how vendors can fine-tune LLMs with domain-specific knowledge (e.g., MIL-STD requirements, DoD architectures, UAF metamodel) to ensure optimal performance and accuracy in both SysML and UAF contexts. Furthermore, we seek clarity on plugin architecture and compatibility with various Cameo versions, the ability to generate and manage UAF viewpoints (e.g., Strategic, Operational, System), training and enablement programs for NIWC engineers focused on both SysML and UAF with AI assistance, ongoing support and maintenance strategies, and approaches to ensuring data security and compliance with relevant government regulations . Ultimately, our goal is to identify a partner who can enable us to achieve significant improvements in our system development lifecycle, reducing time-to-market, optimizing costs, and increasing the overall quality, security, and reliability of our systems developed using Cameo Systems Modeler, with seamless support for both SysML and UAF modeling paradigms.

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