This Challenge notice is issued under the Innovation for Defence Excellence and Security (IDEaS) Program Call for Proposals (CFP) Call 006 (W7714-248676/A).
Solicitation Documents reference: See “Bidding details” section.
*For additional general information on the IDEaS Program, visit: https://www.canada.ca/en/department-national-defence/…
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This challenge is open to receive proposals for Component 1a, Component 1b and Component 2. Proposed solutions that fall within technology readiness levels (TRL) 1-9 can be submitted to this challenge.
Steps to apply:
Step 1: read this challenge
Step 2: read the Call for Proposals : See “Bidding details” section
Step 3: propose your solution here : https://defence-innovation-portal.my.site.com/
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Maximum Funding and Performance Period
Multiple contracts could result from this Challenge.
The individual maximum contract funding available under Component 1a (TRL 1 to 3) is up to $250,000 CAD (excluding applicable taxes) for a maximum performance period of up to 6 months.
The maximum individual contract funding available under Component 1b (TRL 4 and 5) is up to $1,500,000 CAD (excluding applicable taxes) for a maximum performance period of 12 months.
The maximum individual contract funding available under Component 2 (TRL 6 to 9) is up to $5,000,000 CAD (excluding applicable taxes). The period of performance will be determined at the time of contract negotiation.
The maximum individual contractual funding and the maximum performance period offered under Component 3 will be determined by Canada at the time of contract negotiation.
This disclosure is made in good faith and does not commit Canada to contract for the total approximate funding.
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Challenge Details
Challenge Title: W7714-248676/015 - Turning Urban Data into Real-Time Insight through AI
The Department of National Defence and Canadian Armed Forces (DND/CAF) are seeking innovative Artificial Intelligence (AI) driven solutions that repurpose existing urban infrastructure as a distributed, passive sensing network capable of delivering scalable, real-time situational awareness and anomaly detection.
Background and Context
CAF’s current detection and monitoring capabilities rely on purpose-built, dedicated sensor systems that are costly to procure, slow to deploy, and difficult to scale across the breadth of urban landscape. Meanwhile, cities already operate thousands of cameras, acoustic arrays, air-quality monitors, vibration sensors, and wireless access points that generate rich, continuous data streams—none of which are currently exploited for situational awareness by DND/CAF.
The opportunity is to develop an AI middleware layer that sits on top of these existing feeds, transforming ambient urban data into actionable intelligence without installing new hardware. This is a force-multiplier concept: turning every city into a sensor grid that can detect unauthorized drone activity, unusual vehicular or pedestrian patterns, anomalous radio frequency (RF) emissions, chemical or environmental signatures, and emerging threats in real time and at a fraction of the cost of dedicated systems.
This challenge supports the CAF Digital Campaign Plan, Strong, Secure, Engaged urban defence priorities, and operations readiness. The resulting platform becomes a reusable building block for any future scenario requiring rapid urban intelligence—from G7 summit security to allied interoperability in coalition urban operations.
This challenge seeks to grow Canadian industrial capacity and intellectual property in ambient urban intelligence—a capability that does not yet exist as a sovereign Canadian product—while ensuring compliance with Canadian values and legal standards from the ground up.
DND/CAF are hosting this challenge to observe advancements in AI technology to resolve this issue.
Examples of application can be described as, but are not limited to:
Urban Counter-Uncrewed Aerial Systems (UAS): Fuse traffic camera video, acoustic sensors, and passive RF analytics to detect, classify, and track unauthorized drones over cities—without deploying any new hardware.
Critical Infrastructure Protection: Repurpose vibration sensors on bridges and buildings, air-quality monitors near water treatment plants, and surveillance cameras at power substations to create an anomaly-detection mesh.
Environmental Early Warning: Use environmental sensors as secondary indicators for abnormal activity.
DND/CAF will not provide any data (classified, unclassified, operational, synthetic, or representative) for use in training, fine-tuning, or validating AI models. Participation in this challenge assumes that innovators possess sufficient sensor-domain expertise to independently generate or obtain appropriate datasets for model development and testing.
Essential Outcomes
Proposed solutions must:
- Build one or more AI-enabled modules capable of ingesting and processing data from multiple (at least two), heterogeneous sources.
- Perform real-time analysis to identify anomalous patterns or potential threat indicators within the ingested data streams.
- Produce machine-readable outputs (e.g., alerts, scores, or flags) corresponding to detected anomalies or threat indicators for further review by an operator.
- Be compliant with applicable legal, privacy, and data-protection requirements when processing data derived from civilian or non-defence sources. The offeror is responsible for determining which are applicable.
Desired Outcomes
Proposed solutions should include capabilities and considerations such as, but not limited to, the following:
- Rapid deployability: A portable, edge-deployable solution that can connect to locally available data feeds and become operational in a new environment.
- Adaptive sensor ingestion: The ability to connect to diverse and evolving data feeds using standard protocols, when individual sources degrade or go offline.
- Explainable and actionable outputs: Anomaly detections include sufficient context for operators to understand what was detected, which sources contributed, and the confidence level of the assessment.
- Scalable architecture: A design that can grow from a localized and that is architecturally compatible with international standards.
