Company
Portfolio Data
Back to Company SearchREAL-TIME INNOVATIONS, INC.
UEI: H293LTV8R2U3
Number of Employees: 267
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
SBIR/STTR Involvement
Year of first award: 1995
58
Phase I Awards
32
Phase II Awards
55.17%
Conversion Rate
$6,719,266
Phase I Dollars
$29,814,267
Phase II Dollars
$36,533,532
Total Awarded
Awards
APIARY: Aegis Platform Integration of Aerial Robotic sYstems
Amount: $139,895 Topic: N251-036
In modern conflict, Unmanned Aircraft Systems (UAS) have proven essential for real-time surveillance, precision strikes, and other missions. The U.S. Navy seeks to integrate these platformsóranging from off-the-shelf drones to advanced, fully autonomous systemsóinto the Aegis Combat System. Our proposed software solution, APIARY (Aegis Platform for Integrating Aerial Robotic sYstems), will harmonize diverse UAS protocols using open standards and software-defined networking, ensuring that operators can seamlessly discover, command, and coordinate multiple platforms.APIARY addresses the Navyís goals of ìspeed and quality of information,î secure communications, and ìcost benefits Ö through improved maintenance and reduced manning.î It unifies different UAS data models, reduces the need for specialized hardware, and provides a scalable architecture capable of coordinating numerous drones across multiple vessels. Robust security mechanisms are designed to protect mission-critical data under a variety of operational conditions and threat environments.Through a phased approach, we will validate a common set of data interfaces, explore a flexible gateway architecture, examine large-scale resource management, and ensure secure data exchange. Ultimately, APIARY lays the groundwork for seamless interoperability, lower costs, and enhanced mission effectiveness, paving the way for the future integration of emerging UAS technologies, including swarms, into the Navyís arsenal.
Tagged as:
SBIR
Phase I
2025
DOD
NAVY
The GENESIS Agent Toolkit for Simulink
Amount: $179,998 Topic: AF242-0002
DAF PEO Digital has also recognized that generative AI agents will be transformative in nearly all parts of military operations beginning with Simulation. ĀThis SBIR topic will help to inform the DAF about the potential of next-generation distributed agent frameworks to deliver compounded returns by enabling tens to hundreds of collaborative agents to collectively work together.ĀToday, while industry has produced many agent frameworks to extend the capabilities of generative AI, the focus has been on conceptual demonstrations to evaluate interacting agent capabilities in a single process.Ā While some of these are becoming quite elaborate and capable, they focus on single process / single code base frameworks that inherently lack the distributed agent/human collaboration that is a primary component to the vision of generative AI agents in the enterprise.Ā Further, these systems leave it up to individual developers to integrate these agents with relevant systems.ĀĀIn order to reap the substantial benefits of generative AI (Gen AI), industrial and academic research has shown the significant leap in performance from multiple collaborating agents.Ā Today, no such framework exists that will facilitate the development, testing, integration, and execution of large-scale heterogeneous agent-based systems.Ā We are proposing the first framework of its kind to deliver this capability to the DAF.
Tagged as:
SBIR
Phase I
2025
DOD
USAF
Non-Intrusive Data-Centric Track Synchronization and TDL Monitoring for AEGIS Combat System
Amount: $139,876 Topic: N251-046
This proposal addresses the Navy's requirement for automated track synchronization across the AEGIS Combat System (ACS). The current architecture, where multiple source managers maintain local track stores alongside the Joint Track Manager (JTM), can experience synchronization issues due to race conditions in data processing and concurrent updates. These inconsistencies can degrade situational awareness and impact combat system effectiveness.RTI proposes a non-intrusive, data-centric solution that leverages the existing DDS infrastructure within AEGIS. Our approach introduces a modular framework that monitors DDS message traffic to detect track inconsistencies, validates track relationships through semantic models, and executes corrections using standard DDS mechanisms. The solution will monitor DDS topics including tactical data link (TDL) messages to maintain comprehensive track synchronization.The Phase I effort will focus on concept development and risk reduction through detailed requirements analysis and technology exploration. We will create a test environment to reproduce synchronization issues and evaluate different detection strategies. Working with stakeholders, we will identify representative use cases that demonstrate both technical challenges and operational impact. Our investigation will explore semantic modeling approaches that enable sophisticated track correlation while maintaining DDS performance characteristics.RTI brings extensive DDS expertise, proven monitoring capabilities, and established relationships with AEGIS stakeholders. If funded through Phase II, this effort will deliver a prototype that enhances AEGIS track management reliability while providing a foundation for broader adoption across Navy combat systems.
Tagged as:
SBIR
Phase I
2025
DOD
NAVY
Track Store Synchronization Using Semantic Models for AEGIS Combat System
Amount: $139,876 Topic: N251-051
This proposal addresses the Navy's requirement for automated track synchronization across the AEGIS Combat System (ACS). The current architecture, where multiple source managers maintain local track stores alongside the Joint Track Manager (JTM), can experience synchronization issues due to race conditions in data processing and concurrent updates. These inconsistencies can degrade situational awareness and impact combat system effectiveness.RTI proposes a non-intrusive solution that leverages data-centric modeling capabilities. The key innovation of our approach is the use of semantic models to capture complex relationships between track attributes and understand the specialized roles of different track sources. These models enable intelligent comparison and correction strategies that account for partial track data sets and normal fusion-related variations. By understanding the semantic meaning of track data, our system can better distinguish between expected temporary inconsistencies and true synchronization problems that require correction.The Phase I effort will focus on concept development and risk reduction through detailed requirements analysis and technology exploration. We will create a test environment to reproduce synchronization issues and evaluate different detection strategies. Working with stakeholders, we will identify representative use cases that demonstrate both technical challenges and operational impact. Our investigation will explore semantic modeling approaches that enable sophisticated track correlation while maintaining DDS performance characteristics.RTI brings extensive DDS expertise, proven monitoring capabilities, and established relationships with AEGIS stakeholders. If funded through Phase II, this effort will deliver a prototype that enhances AEGIS track management reliability while providing a foundation for broader adoption across Navy combat systems.
Tagged as:
SBIR
Phase I
2025
DOD
NAVY
Next-Gen MOSA-Based Real-Time Sensor Data Processing Architecture for Big Iron
Amount: $1,249,969 Topic: AFX237-PCSO1
The project aims to enhance the key avionics program's capabilities by integrating IEEE TSN and OMG DDS for high-performance, real-time data communication. It addresses the limitations of traditional network standards, offering dynamic rescheduling and reconfiguration for flexible mission adaptation. The solution promotes modularity and interoperability, reducing costs by leveraging low-cost Ethernet hardware and enhancing security through data-centric measures. Additionally, it accelerates capability deployment with MBSE tools and improves operational efficiency with converged networking, ensuring seamless data exchange across diverse systems.
Tagged as:
SBIR
Phase II
2024
DOD
USAF
A Data-Centric Distributed Agentic Framework for DAF Simulation
Amount: $1,249,976 Topic: AFX246-DPCSO1
GENESIS (Gen-AI Network for Enhanced Simulation Integration and Security) aims to integrate AI agents into simulation environments safely, securely, and reliably. Utilizing the OMG DDS standard, particularly RTI’s Connext DDS, GENESIS completes the stack for a multi-agent simulation integration.Key Objectives1. Safety: Ensure safe AI agent operation through comprehensive state monitoring and semantic-level interaction, including tools for semantic analysis, logging, and real-time monitoring.2. Security: Implement a multi-faceted security environment leveraging Connext DDS’s existing mechanisms with additional semantic controls, including fine-grained access control, encryption, and content monitoring.3. Reliability: Provide a standardized communication layer using DDS for inter-agent communication, facilitating real-time data exchange and supporting integration with MATLAB/Simulink and future integration with AFSIM. This includes communication libraries for widely used agent frameworks like Semantic Kernel and LangChain.Technical Approach- Discovery: Enable endpoints to discover each other without network configuration.- Security: Provide built-in access control, authentication, encryption, and semantic oversight.- Data Delivery: Ensure data is presented as known structures, eliminating integration issues with additional agents/services- Content Filtering and Keying: Allow endpoints to send data only when needed by others.- Comprehensive monitoring: Enable oversight, model training, and in context agent improvement through agent input/output state data.Phase II Technical Objectives and Key Results1. Develop Comprehensive State Monitoring and Interaction - Implement tools for semantic analysis and logging. - Demonstrate real-time monitoring in a test environment.2. Develop Multi-faceted Security Environment - Implement fine-grained access control policies. - Integrate semantic controls for monitoring communications.3. Develop Seamless Integration with MATLAB/Simulink - Implement and test the MATLAB/Simulink plug-in. - Develop an initial integration plan for AFSIM.4. Develop a Collaborative AI Environment - Implement DDS-based communication for real-time data exchange. - Develop tools for collaborative task planning and execution.5. Develop and Demonstrate Inter-agent Library - Develop communication libraries for Python, C#, and JavaScript. - Conduct multiple demonstrations to stakeholders and gather feedback.
Tagged as:
SBIR
Phase II
2024
DOD
USAF
Next-Gen MOSA-Based Real-Time Sensor Data Processing Architecture
Amount: $74,677 Topic: AFX237-PCSO1
We are focused on exploring advancements in resilient real-time networking using open standards (time-sensitive networking [TSN]) to substantially improve the DAF's ability to process sensor data in real-time.
Tagged as:
SBIR
Phase I
2024
DOD
USAF
Accelerating Threat and Risk Detection within Data Centric Networks
Amount: $74,816 Topic: X224-OCSO1
Continuous monitoring of critical distributed systems is essential for the early detection of risks and security threats. However, traditional networks are opaque and do not provide the transparency needed for advanced data monitoring. Existing monitoring tools can identify network-layer information but cannot observe data semantics. Next-generation data-centric networks present an opportunity to revolutionize network monitoring. In data-centric networks, data semantics are decoupled from the specifics of the application stack, so a trusted observer can not only identify data flows but also understand the semantics of the communications. Monitoring and visualizing data streams and their semantics can accelerate threat detection by identifying undesired communication flows caused by misconfigurations, bugs, or an intruder, and answer questions such as who is talking to who, and what are they talking about? RTI’s product is the world’s leading implementation of the OMG DDS standard and military-grade data-centric connectivity software. We propose to design, implement and test a revolutionary monitoring framework for Air Force’s next-gen data-centric networks. If fully funded, the framework will accelerate threat detection by visualizing data streams and extending AI/ML-based monitoring and data analytic tools. The framework will also enable automatic system validation against UAF models and other security standards. In Phase I, we will collect stakeholders' requirements and derisk Phase II.
Tagged as:
SBIR
Phase I
2023
DOD
USAF
Accelerating Threat and Risk Detection within Data Centric Networks
Amount: $1,249,951 Topic: X224-OCSO1
Threat detection and response are critical components of the United States Air Force's (USAF) mission to protect national security and maintain air superiority by outpacing enemy threats. The Air Force Life Cycle Management Center (AFLCMC) defines continu
Tagged as:
SBIR
Phase II
2023
DOD
USAF
Outpacing Threats By Enabling Rapid Integration of Autonomous Capabilities
Amount: $64,918 Topic: AFX235-CSO1
In autonomous systems warfare, stepping inside the enemies’ OODA loop will require automated on-the-fly kill web construction from capabilities offered across disparate systems/domains/forces not fully designed for interoperation. Dynamically assembling a
Tagged as:
SBIR
Phase I
2023
DOD
USAF