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EXOANALYTIC SOLUTIONS INC

Address

27042 TOWNE CENTRE DR STE 250
EL TORO, CA, 92610-2811
USA

View website

UEI: DGB4YJSKHM25

Number of Employees: 133

HUBZone Owned: No

Woman Owned: No

Socially and Economically Disadvantaged: No

SBIR/STTR Involvement

Year of first award: 2010

Phase I Awards

Phase II Awards

57.58%

Conversion Rate

$4,418,975

Phase I Dollars

$22,059,046

Phase II Dollars

$26,478,020

Total Awarded

Awards

Up to 10 of the most recent awards are being displayed. To view all of this company's awards, visit the Award Data search page.

InterceptAI: Adaptive Defense in Dynamic Warfare

Amount: $149,897 Topic: MDA24B-T001

Threat missile systems are evolving in type, volume, and capabilities. New threats, such as hypersonic glide vehicles, can now achieve speeds greater than Mach 5 in the atmosphere, maneuver over long distances, and strike targets with high accuracy. The missile threat issue is exacerbated when considering the variety of missile types and the potential for raid scenarios. Additionally, the potential use of Artificial Intelligence (AI) in threat systems to enhance performance and enable autonomous operations is a growing concern. In response, ExoAnalytic Solutions (Exo) and Auburn University are proposing InterceptAI. InterceptAI will develop an advanced simulation platform that leverages cutting-edge AI and reinforcement learning techniques to model and optimize engagements between threat missile systems and interceptor defense. InterceptAI aims to enhance national security by providing decision-makers with valuable insights into optimal engagement strategies, technology advancements, threat assessments, mission planning, and system performance evaluation in dynamic warfare scenarios. By simulating complex interactions between threat missile systems and defensive interceptors, InterceptAI enables the analysis of adversary behavior, prediction of threats, and identification of opportunities for enhancing defensive capabilities. Through continual learning and adaptation, InterceptAI empowers the Missile Defense Agency (MDA) to stay ahead of evolving threats and safeguard critical assets effectively. Approved for Public Release | 25-MDA-12052 (27 Mar 25)

STTR

Phase I

2025

DOW

MDA

Dynamic Re-entry Impact Forecasting Tool

Amount: $179,977 Topic: SF243-0003

Proliferation of the Low Earth Orbit (LEO) regime increases the need for tools that can provide risk mitigation strategies associated with uncontrolled re-entry events. Historical events such as NASA Skylab and the Soviet Cosmos as well as recent events like the Tiangong-1 space station have demonstrated the potential dangers and risks associated with re-entry debris. The oversaturation in LEO will lead to debris causing events and in turn increased probabilities of uncontrolled re-entries.áThese factors highlight the need for a tool which can take orbits and real-time observations to provide a medium to high fidelity forecasting of potential re-entry events. The Dynamic Re-entry Impact Forecasting Tool (DRIFT) will take in high fidelity Low Earth Orbit (LEO) drag data from Arcfieldĺs Dragster model and integrate that with advanced orbital propagation tools, graphical visualizations, and external observation processing algorithms.áDRIFT will provide robust re-entry prediction probabilities along with the resulting potential debris fields and the graphical visualizations of such events. This will help ensure the safe and responsible design for end of life planning of future LEO missions as well as reducing potential risks to human life and property in the event of uncontrolled re-entries.

SBIR

Phase I

2025

DOW

USAF

Lucius: Generative Network for Continuous Situational Awareness and Reliable Insight in Dynamic, High-Stakes Operational Environments

Amount: $1,999,938 Topic: A244-064

This work focuses on advancing continuous situational awareness capabilities by developing innovative methods to detect and mitigate biases in Large Language Model (LLM) outputs while integrating multimodal data sources for comprehensive analysis. The proposed solution improves the reliability, accuracy, and adaptability of LLMs in dynamic military contexts, enabling them to analyze diverse data types—including text, images, videos, and sensor data—to create a holistic operational picture. By employing advanced bias mitigation techniques, hallucination detection methods, and dynamic data labeling, the approach ensures that all generated intelligence is accurate, fair, and actionable, supporting real-time decision-making across various domains. The project will deliver a scalable prototype that continuously adapts to incoming information, using state-of-the-art data retrieval and integration methods to maintain a high level of situational awareness in rapidly changing environments. This effort also incorporates innovative machine learning techniques, such as federated learning and entropy-based analysis, to ensure data privacy, security, and resilience against evolving threats. By integrating real-time data fusion, multimodal processing, and continuous learning, this project aims to enhance decision quality, reduce response times, and provide reliable insights for mission-critical operations, ultimately strengthening the Army's ability to respond effectively to complex and unpredictable scenarios.

SBIR

Phase II

2025

DOW

ARMY

Datavolume Intrinsic Characteristics Evaluation to Gauge AI and ML Enhancements (DICE GAME)

Amount: $179,928 Topic: SF24B-T004

The Datavolume Intrinsic Characteristics Evaluation to Gauge AI and ML Enhancements (DICE GAME) effort will develop and utilize quantifiable metrics to assess the relationships between volume and value for databodies. After identifying viable metrics, this effort will apply them to available data in the Space Situational Awareness (SSA) domain and will assess the degree to which such metrics can be used to evaluate how valuable a databody of a given size is. The questions of whether such metrics generalize both to multiple characteristics within a domain and across domains will be addressed, providing insight into the utility of these metrics for any databody that might be used for artificial intelligence (AI) or machine learning (ML) purposes.

STTR

Phase I

2025

DOW

USAF

FATES Phase II

Amount: $1,370,434 Topic: N241-037

FATES is an advanced decision-aid framework designed to provide operators with optimal weapon assignment recommendations. The system comprises three integrated components: (1) a Weapon-Target Graph Database capturing synergies and conflicts across hard-kill (HK) and soft-kill (SK) assets; (2) a Weapon-Target Opportunity Evaluator that assesses availability, feasibility, and effectiveness of engagement options; and (3) an Approximate Dynamic Programming (ADP) WTA algorithm using Least-Squares Temporal Difference learning to adapt to dynamic raid environments. FATES delivers real-time scheduling recommendations for SSDS by optimizing HK/SK coordination, enabling layered defense under uncertain and evolving threat conditions. FATES will deliver a modular, real-time, hardware-agnostic decision support framework designed to optimize weapon-target assignment (WTA) for the U.S. Navy’s Ship Self Defense System (SSDS).

SBIR

Phase II

2025

DOW

NAVY

Artificial Intelligence/Machine Learning (AI/ML) Battle Management for Rapid, Interactive Weapon Selection

Amount: $149,898 Topic: MDA241-012

Threat missile systems are evolving in type, volume, and capabilities. Current missile defense programs rely on Kinetic (K) kill weapons and various sensors such as ground-based radars, sea-based radars, and Electro-Optical and Infrared (EO/IR) sensors on aircraft and spacecraft to defend against the evolving threats. Kinetic weapons are limited by available interceptor numbers, engagement volume, and time to intercept potentially limiting the CommanderÆs battle plan options. To increase our defense capabilities, Non-Kinetic (NK) weapons, also known as Directed Energy (DE), will be integrated into the Missile Defense System (MDS). These new NK weapons will bring game changing speed-of-light kill capabilities to the MDS. While NK weapons are game changers, they are not without their own limitations. Weather conditions can degrade NK weapon effectiveness which must be a consideration for the MDS and Commanders in planning and executing engagements. To address this complex battlespace, the Commander will need new tools for situational awareness, weapon selection, and rapid decision aides for real-time assessment and execution. ExoAnalytic Solutions (ExoAnalytic) is proposing an integrated Artificial Intelligence (AI)/Machine Learning (ML) approach that blends the speed and optimization advantages of deep reinforcement learning with the human understandable advantages of probabilistic-based reasoning. The system will be fast and accurate but include the human in the loop in a telegraphic and rapid-feedback manner allowing commanders the opportunity to rapidly assess multiple battle plans updated over time as the battle evolves. Approved for Public Release | 24-MDA-11906 (16 Sep 24)

SBIR

Phase I

2024

DOW

MDA

Spacecraft Proximal Intent Notifications for Navigation Assurance and Knowledge-Enabled Resiliency

Amount: $149,930 Topic: SF233-0007

The environment in which space traffic persists is becoming ever increasingly operationally dynamic. It is no longer sufficient to provide answers to the question of where resident space objects (RSOs) are located and where natural motion will take them, the realm of space situational awareness. Nor is it significantly more tactically foolproof to systematically answer the question of where such objects will be headed given an understanding of expected mission profiles, arguably space domain awareness (SDA). Rather, we must seek answers to a more wholistic set of queries. Why are certain RSOs positioned in a region? What will a given object be doing next and with what indicators? What capabilities and behaviors are expected over the course of a day, a week, or a month? What are the potential risks in terms of hardware, programs, or geopolitics? These deeper inquiries rely on a multidomain understanding of mission profiles and warrant a closer look at the traditional algorithms of statistical inference employed in questions of orbit determination. To further the challenge at hand are the complexities of the modern operational picture. With frequent commercial and international launches, new hardware technologies being deployed, autonomous maneuvering, and fast strides in the domain of software and cybersecurity, the pace of information flow threatens to render obsolete traditional methods of RSO encounter analysis. While target tracking has seen the proliferation of a variety of vary capable algorithms, a particular challenge remains in the practical application of ever-sophisticated approaches to statistical reasoning: the imperfect characterizations of dynamical system uncertainty. Where there is purely random phenomena involved, aleatory uncertainty provides a perfect description of the behavior and the probabilistic assumptions are satisfied. In the presence of systematic errors, epistemic uncertainty, it is, however, not uncommon for certain methods to show a more delicate side to their construction. More succinctly, it is becoming necessary for the random, aleatory, component of Bayesian reasoning to be fused with the systematic errors, epistemic uncertainty, surrounding common spacecraft “behavioral” profiles. Drawing from our commercial SDA offerings, and with an aim to both enable faster decision making for the modern operator and in so doing, add a further degree of resiliency to spacecraft mission execution, ExoAnalytic Solutions proposes the Spacecraft Proximal Intent Notifications for Navigation Assurance and Knowledge-Enabled Resiliency (SPINNAKER) effort. By providing data-driven intent alerts and analytics to support informed decision making in the space operational environment, a comprehensive domain understanding can be more greatly leveraged in joint command and control efforts to ensure mission responsiveness on tactical timelines and pave the way for robust, resilient autonomous spacecraft capabilities.

SBIR

Phase I

2024

DOW

USAF

ExoAnalytics ChatTTP

Amount: $179,947 Topic: AF242-0002

Large language models (LLMs) and natural language processing are rapidly evolving and expanding fields. Tools developed utilizing LLMs are increasingly effective across multiple domains. The use of a network of LLM-based agents to improve in-theater communications for operators, pilots, and autonomous vehicles is an important emerging area of investigation.ĀĀOperator-like agents using LLMs and natural language processing to perform functionally defined roles will allow the development of a low-cost, realistic, and configuration-controlled training environment for in-theater communications. This capability will make possible the instruction of autonomous vehicle operations over secure voice or natural language text-based communications, improving integration between crewed and autonomous systems.ĀFor Phase I, ExoAnalytic's ChatTTP will provide an end-to-end framework that allows operators the ability to instruct networks of agents to perform various communications and TTPs for theater operations.ĀThis framework will provide a base agent for each operator position defined for theater operations trained on their specific functions using documents related to the specific role. The Phase I delivery will be a prompt-based graphical user interface (GUI) which provides communication logs between agents based on the scenarios given and a containerized package of agents.

SBIR

Phase I

2024

DOW

USAF

Transportable Lasercom Ground Station

Amount: $1,249,945 Topic: SDA24-P001

ExoAnalytic is proposing to lead development of the Rapidly Relocatable DSO Demonstrator (R2D2), where we will identify and complete initial integration steps for a transportable lasercom capability. ExoAnalytic will leverage our extensive experience in observatory design, observatory operation, sensor command and control, as well as precision pointing to execute the design and future transportability of this lasercom ground station. Working with Subject Matter Experts (SMEs) from PlaneWave Instruments and HartSCI, the team will demonstrate the ability to track satellites with the accuracy required for lasercom and the transmit/receive capability for a prototype ground station. Further work with SME from the Space Development Agency will ensure required communication protocols and tracking orbits of interest are identified and designed towards. At the conclusion of this effort, the team will have demonstrated the key technical components to a transportable lasercom ground-station and developed the roadmap for rapid acquisition and deployment of such a system.

SBIR

Phase II

2024

DOW

SDA

Countering Cyber and RF Threats to National Security Space Systems

Amount: $149,945 Topic: OSD234-P002

The ability to recognize, understand, characterize, and visualize the cyber and radio frequency (Cyber/RF) warfare environment in space is a prerequisite for creating actionable intelligence, enabling critical decisions, and creating operational advantage to counter devastating cyber intrusion and RF disruption of national security space assets. Cyber/RF engagements in space require position and alignment parameters, exquisite timing, and appropriate onboard sensors for both target and aggressor spacecraft. The effects of operating in space are visible through phenomenological signature changes such as photometric effects from reorienting a spacecraft and RF Doppler or modulation shifts based changes to onboard modes of operation. While invisible in cyberspace, varying degrees of observability of space offer an opportunity to provide indications and warning (I&W) prior to a Cyber/RF engagement. As a multi-phenomenology, persistent global monitor of the space domain with an established history in modeling and simulation (M&S) and battlespace visualization, ExoAnalytic Solutions (Exo) is uniquely postured to use its World-class observability of the space domain against this problem set. Exo’s cutting-edge optical tracking and passive RF capabilities feed real-time data to advanced analytical and visualization technologies supporting warfighters in decision-making. Exo will create a Cyber/RF threat profile ontology that maps specific observable satellite actions to the Cyber/RF threat continuum, identifying actions that must be or likely will be taken to execute a threat behavior and observable conditions those actions will create. Spacecraft behaviors that may appear ordinary or routine could be indicators of threat activity when viewed in a Cyber/RF threat context. Mapping the indicators to the range of Cyber/RF behaviors will form the context from which an alerting matrix can be established. Exo will create weighted alerts for each Cyber/RF threat behavior by considering observable data at scale, across spacecraft and their surrounding environments, across time, and using multiple phenomenologies. Instead of creating alerts from scratch, the Cyber/RF threat profile ontology will be overlaid on Exo’s automated alerting system (ExoALERT) to contextualize standard activity recognition algorithms that already include a wide range of observable events such as maneuvers and orientation changes. Exo will then use its SPySE threat visualization platform to model the created ontology’s execution against simulated data. Exo will simulate Cyber/RF behaviors to test the alerting system’s effectiveness in responding correctly and efficiently to Cyber/RF engagements.

SBIR

Phase I

2024

DOW

SCO