Risk-Aware Regenerative AI-based Multimodal Visual-Tactical (ISRT) (Observant-AI) – Monitor, Understand, Alert, and Assist - SBIR Topic DON26BZ01-NV023

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This topic was temporarily posted by the Department of War SBIR Program on March 2nd 2026 and removed the following day.
We believe this topic is planned to be released once the SBIR program is reauthorized; however, this topic may ultimately be modified or withdrawn.

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Funding Amount:

Est. $240,000

Deadline to Apply:

Est. April 29th, 2026.

Objective:

Develop risk-aware artificial intelligence (AI)-based computing methods motivated by three naval challenge problems that enable insightful active cross-domain (Sea-Space-Air-Land-Cyber) situational awareness and AI-assisted course of action and countermeasures in real-time conditions, namely, “LIVE” machine self-teaching (i.e., Regenerative AI); contextual machine exploitation; contextual networking to gain insights from accessible all-source-intelligence (ASI) and multimodal sensors; and proactive AI-assisted targeteer and decision support to manned and unmanned assets. The Observant-AI is envisioned as a distributed system of mission-focused AI agents that self-organize and share insights via ad hoc networking. The agents autonomously form mission-oriented collaborative teams to process and fuse multidomain anomalous events and activities for real-time AI-generated visual-tactical understanding, monitoring, alerts, and related operational risks. It applies natural language explanations for human-AI interactions, course of action assistance, and reasoning about risky engagements. For example, submersible X is tracking you, change course to southwest, speed up…; Cargo-Ship Y is armed, Container Marking is…, Departing Port XYZ; 20 UAVs are shadowing, armed, turn around go south; Littoral Zone X, Torpedo-Mines, Bottom-Mines, Deep Fencing, Actively Guarded, Speed Boats, Risk-High Navigation, Need Minesweeper, Check with CENTCOM; etc.

Description:

Problem scope and capability concerns. First, over the past three decades, advancements in AI and machine learning (ML) for applications in hybrid networked teaming of manned and unmanned systems and sensors have unlocked new possibilities across a range of naval operations for novel missions. On the other hand, the defensive and offensive effectiveness of these technologies against near-peer adversaries remains a significant challenge.

Second, current Naval ISRT operations follow rigorous protocols supported by wide-ranging wargaming scenarios to plan tactics, techniques, and procedures (TTPs) with contingencies as operations unfold. TTPs focus on various situational details, such as adversary strength, leadership temperament, past and present operational performance, logistics, and exploitation opportunities for friendly cross-domain actions and effects. These plans are vital to be followed. However, they are extremely vulnerable to human biases and omissions that undermine the assessment of evidence, statistical analysis, and the understanding of cause and effect.

Third, generative AI methods are being integrated into the operational planning process and can enrich the development of a range of ISRT strategies. However, it must start all over again if “Unknown-Unknown” events crash the ongoing TTPs. Also, generative AI needs high-quality training datasets; otherwise, it is prone to inaccuracies and biases.

This SBIR topic will develop Observant-AI agents as a class of regenerative AI that learn in real time, enables active visual and tactical monitoring of anomalous activities, and trigger I&W alerts in naval operations. The envisioned Observant-AI agents proactively enforce the fail-safe execution of approved ISRT operational plans. They exploit unexpected events in real-time by leveraging insights from all-source intelligence (ASI) and remote sensors (i.e., space assets). They generate and execute novel all-domain ISRT TTPs plans consistent with the approved plans to counter evolving adversarial intents and undesired events, LIVE. In other words, the Observant-AI agents enable fault-tolerant mission-focused reconfiguration by analyzing existing assets’ capabilities through novel tactical teaming arrangements from approved deployable capabilities (sensors, manned and unmanned weapon platforms, intelligence data sources, etc.). Observant-AI will automatically alert the chain of command at all levels with emerging or mission-altering observables that may interfere with operational objectives.

The goal of the effort is to perform a combination of offline and online predictive engagement modeling to plan for trusted AI-enabled TTPs that will strategically adjust plans in real time to adapt to emerging events and conditions. It will use Monte Carlo simulation to model the probability of various outcomes under countless AI-generated Red vs. Blue engagement (action-reaction) scenarios for offline TTP planning and mission success assessment. Regenerative AI will ensure Observant-AI can quickly adapt the blue’s creative ISRT strategies against near-peer adversaries (Red). Regenerative AI offers unique capabilities such as learning from sparse data and predicting complex interactions. It will achieve this objective by testing novel all-domain penetration strategies, including offensive cyber and information operations to find advantageous strategies, then running them against many emerging scenarios, identifying the vulnerability points and engagement risks, and modifying strategies to sustain their performance with acceptable risks.

Critical AI technology components and developments are as follows:

Contextual modeling: relational modeling, graph-based modeling, spatial modeling, logic-based modeling, uncertainty modeling, ontology-based modeling, hybrid context modeling.

Multidomain multimodal all-source intelligence data and signals: multi-level secure connectivity and access.

Data learning: decision tree classifier, multilayer perception classifier, collaborative filtering, frequent pattern mining, K-means, deep learning.

Data quality, data interoperability, data generation.

Data storage: signal-oriented database, graph-based database, associative database, text-oriented database.

Spatiotemporal synchronization methods for multimodal data across decentralized architectures.

Multimodal contextual signal processing and fusion.

Cross-domain contextual collaborative learning, inference, and recognition.

Contextual collaboration, adaptation, and teaming via ad-hoc networking.

Contextual reasoning, risk assessment, and risk reduction.

Contextual query, question-answering (Q&A), and natural language processing.

Contextual priority-based task management and balancing competing multifaceted ISRT operational objectives such as persistence, endurance, opportunistic collections, and targeting.

AI-risk escalation control methods that will not erode decisions across the integrated chain-of-command.

AI-assisted targeteer maneuvers and engagements.

Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by 32 U.S.C. § 2004.20 et seq., National Industrial Security Program Executive Agent and Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Counterintelligence and Security Agency (DCSA) formerly Defense Security Service (DSS). The selected contractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances. This will allow contractor personnel to perform on advanced phases of this project as set forth by DCSA and ONR in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material during the advanced phases of this contract IAW the National Industrial Security Program Operating Manual (NISPOM), which can be found at Title 32, Part 2004.20 of the Code of Federal Regulations.

Who will win?

If you can achieve the objective above better than any other company on the market, you have a very high-likelihood of success and should apply.

Who is eligible to apply?

Any company that meets the following criteria:

• For-profit company

• U.S.-owned and controlled.

• 500 or fewer employees (including affiliates)

How Can BW&CO Help?

1) End-to-end support including, strategy, writing of the full proposal, and administrative & compliance support.

2) Proposal strategy and review.

3) Administrative & compliance support.

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