Cap Vista Accelerator

How might we leverage AI to detect anomalies in complex, high-volume datasets?

Operators today are often overwhelmed with information across multiple sources, which increases the likelihood of missing out on subtle warning signs. AI-enabled anomaly detection based on behavior analysis and intent inference for preemptive alerts could minimize the cognitive load of operators and increase their operational efficiency.

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Context

What We Are Looking For

We are looking for platform-agnostic capabilities capable of achieving the following within 12 months:

AI-enabled anomaly detection based on behavior analysis and intent inference on multiple (video & text) streams to generate pre-emptive alerts.
AI solutions capable of ingesting and analyzing multi-modal, large-scale datasets (text, images, video). Tools that highlight anomalies indicative of coordinated campaigns, or irregular activity.
Approaches that balance detection accuracy with explainability, enabling human analysts to trust and act on outputs.

Key Metrics

Detection Accuracy: Precision/recall in identifying anomalies or irregular activity.
Scalability: Ability to handle millions of data points across diverse sources in near real-time.
Explainability: Clear reasoning or traceability of flagged anomalies.
Adaptability: Performance across different domains (e.g., defence OSINT, commercial data streams).
Latency: Speed of detection and alerting for operational relevance.

Your Technology Solution Should Demonstrate

Integration of AI/ML models with anomaly detection tailored for unstructured and semi-structured data.
Visualization or dashboarding that enables analysts to interpret anomalies quickly.
Modular architecture for integration with existing intelligence or monitoring platforms.

What A Trial Looks Like

Pilot deployment on synthetic and real-world OSINT datasets (e.g., social media feeds, news archives, metadata). Evaluation against known mis/disinformation case studies and adversarial scenarios.
Testing scalability across datasets of varying size and complexity.
Up to 6 months trial with iterative feedback loops from analysts and stakeholders.

What We Are Not Looking For

Purely academic prototypes without operational scalability. Black-box models with no explainability or analyst interpretability.
Solutions limited to single-source datasets (e.g., only Twitter or only text).
Tools focused solely on content moderation rather than anomaly detection and intelligence support.

Follow-On Implementation Cost

Leasing or licensing option over a longer period

How might we optimize AI solutions for field deployment by balancing size, weight, and power (SWaP) constraints without compromising performance?

Operational environments, whether defence, humanitarian, or expeditionary, demand AI systems that are portable, rugged, and energy-efficient. Traditional AI deployments often rely on large compute clusters or cloud connectivity, which are impractical in austere or contested settings. Balancing SWaP is critical to ensure AI can be embedded into edge devices, unmanned platforms, and portable systems, enabling real-time decision-making under resource constraints.

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How might we ensure that AI-enabled autonomous systems are safe, reliable and robust to operate independently or alongside human operators to expand mission capability in complex environments while reducing personnel risk?

AI-enabled autonomous systems such as UxVs are increasingly expected to perform complex tasks. These systems must remain safe, reliable and robust under uncertainties, including in unpredictable and dynamic changes in its surroundings.

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AI Joint Challenge

Inviting global tech providers to address challenges in the areas of AI.

AI Joint Challenge

Inviting global tech providers to address challenges in the areas of AI.

1. How might we leverage AI to detect anomalies in complex, high-volume datasets?​

Context

What We Are Looking For

Key Metrics

Your Technology Solution Should Demonstrate

What A Trial Looks Like

What We Are Not Looking For

Follow-On Implementation Cost

​2. How might we optimize AI solutions for field deployment by balancing size, weight, and power (SWaP) constraints without compromising performance?

Context

What We Are Looking For

Key Metrics

Your Technology Solution Should Demonstrate

What A Trial Looks Like

What We Are Not Looking For

Follow-on Implementation Costs

​3. How might we ensure that AI-enabled autonomous systems are safe, reliable and robust to operate independently or alongside human operators to expand mission capability in complex environments while reducing personnel risk?

Context

What We Are Looking For

Key Metrics

Your Technology Solution Should Demonstrate

What A Trial Looks Like

What We Are Not Looking For

Follow-on Implementation Costs

1. How might we leverage AI to detect anomalies in complex, high-volume datasets?

2. How might we optimize AI solutions for field deployment by balancing size, weight, and power (SWaP) constraints without compromising performance?

3. How might we ensure that AI-enabled autonomous systems are safe, reliable and robust to operate independently or alongside human operators to expand mission capability in complex environments while reducing personnel risk?